高压晶体结构预测：方法发展、数据驱动加速与极端物态探索

王振雨; 李泓霖; 罗啸山; 张微; 吕健; 刘寒雨; 王彦超

doi:10.11858/gywlxb.20261158

高压晶体结构预测：方法发展、数据驱动加速与极端物态探索

doi: 10.11858/gywlxb.20261158

王振雨^{1, 2,},
李泓霖^{1, 2},
罗啸山^{1, 2,*, ,},
张微^{1, 2,*, ,},
吕健^{1, 2},
刘寒雨^{1, 2},
王彦超^{1, 2}

1.
吉林大学物质模拟方法与软件教育部重点实验室, 吉林长春　130012
2.
吉林大学物理学院, 吉林长春　130012

基金项目: 国家自然科学基金（12374005，T2225013，52288102，U22A2098）；教育部基础学科和交叉学科突破计划（JYB2025XDXM403）；吉林省长白山实验室科技创新项目（CBS2025007-01）；国家资助博士后研究人员计划（GZC20252243）

详细信息

作者简介:
王振雨（1998－），男，博士，助理研究员，主要从事材料设计方法发展和应用研究. E-mail：wzy@calypso.cn

通讯作者:
罗啸山（1998－），男，博士，实验师，主要从事材料设计方法发展和应用研究. E-mail：luoxs@calypso.cn

张　微（1979－），女，博士，副教授，主要从事极端高压下的计算凝聚态物理研究. E-mail：zhangw_bxx@jlu.edu.cn

中图分类号: O521.2
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- 文章访问数: 145
- HTML全文浏览量: 54
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出版历程
- 收稿日期: 2026-09-01
- 修回日期: 2026-09-23
- 网络出版日期: 2026-09-25
- 刊出日期: 2026-11-05

Crystal Structure Prediction at High Pressure: Methodological Development, Data-Driven Acceleration, and Exploration of Matter under Extreme Conditions

WANG Zhenyu^{1, 2
,},
LI Honglin^{1, 2},
LUO Xiaoshan^{1, 2,*
, ,},
ZHANG Wei^{1, 2,*
, ,},
LYU Jian^{1, 2},
LIU Hanyu^{1, 2},
WANG Yanchao^{1, 2}

1.
Key Laboratory of Material Simulation Methods and Software of Ministry of Education, Jilin University, Changchun 130012, Jilin, China
2.
College of Physics, Jilin University, Changchun 130012, Jilin, China

摘要

摘要: 高压能够显著改变凝聚态物质的原子排列、电子结构和化学键合，是调控物质结构与性质、拓展材料空间的重要手段。晶体结构预测能够仅从化学组分出发，预测不同压强条件下的晶体结构，是发现高压新相和解析极端物态的关键理论手段。围绕压强约束下高维势能面/焓面的全局优化问题，系统综述了传统晶体结构预测方法，以及机器学习势和晶体生成模型在高压晶体结构预测中的应用；并按实验可达压强与超高压区间，归纳了富氢化合物和硼碳体系高温超导、地球及冰巨行星内部物质等理论引导的代表性进展；最后对领域的发展前景进行了展望。
- 高压 /
- 晶体结构预测 /
- 极端条件物态探索
Abstract: High pressure can profoundly alter the atomic arrangements, electronic structures, and chemical bonding of condensed matter, providing an important means of tuning material structures and properties and expanding the accessible materials space. Crystal structure prediction (CSP) enables the prediction of stable crystal structures under different pressure conditions based solely on chemical composition, and has become a key theoretical approach for discovering novel high-pressure phases and understanding matter under extreme condition. This review focuses on the global optimization problem on high-dimensional potential-energy and enthalpy landscapes under pressure constraints. We systematically review conventional crystal structure prediction methods, as well as recent applications of machine-learning interatomic potentials and crystal generative models to high-pressure CSP. Representative advances in theory-guided high-pressure materials discovery are further summarized across experimentally accessible and ultrahigh-pressure regimes, including high-temperature superconductivity in hydride compounds and boron-carbon systems, as well as materials relevant to the interiors of the Earth and ice giant planets. Finally, current challenges and future perspectives in high-pressure crystal structure prediction are discussed.
- high pressure /
- crystal structure prediction /
- material design under extreme condition

HTML全文

图 1 (a) 势能面示意图及 (b) CALYPSO主要功能与发展历程

Figure 1. (a) Schematic diagram of a potential energy surface; (b) CALYPSO’s main functions and development history

下载: 全尺寸图片幻灯片

图 2 不同压强区间对应的不同物理环境（坐标轴压强点的距离不代表实际压强差）以及不同压强区间内的部分代表性晶体结构

Figure 2. Different physical environments corresponding to different pressure ranges (The distance between pressure points on the axes does not represent the actual pressure difference) and some representative crystal structures within the different pressure ranges

下载: 全尺寸图片幻灯片

图 3 富氢化合物超导体的超导转变温度随时间的变化情况

Figure 3. Time-dependent changes in the superconducting transition temperature of hydride compound superconductors

下载: 全尺寸图片幻灯片

表 1 CSP 方法及部分相关软件

Table 1. CSP methods and part of related softwares

Class	Name	Class	Name
Evolutionary algorithm	AGA^[26]	Simulated annealing	GULP^[27]
	EVO^[28]	Minima hopping	GMIN^[29]
	GASP^[30]	Metadynamics	PLUMED^[31]
	GATor^[32]	Monte Carlo	GRINSP^[33]
	IM2ODE^[34]	Random search	AIRSS^[4]
	MAGUS^[9–10]		Genarris^[35]
	MAISE^[36]		SYDSS^[37]
	MUSE^[38]	Bayesian optimization	BEACON^[39]
	SGO^[40]		CrySPY^[41]
	USPEX^[8]		GOFEE^[42]
	XtalOpt^[11]	Particle swarm optimization	CALYPSO^{[6, 43]}

下载: 导出CSV

参考文献(306)

[1]	ZHANG L J, WANG Y C, LYU J, et al. Materials discovery at high pressures [J]. Nature Reviews Materials, 2017, 2(4): 17005. doi: 10.1038/natrevmats.2017.5
[2]	MAO H K, CHEN B, CHEN J H, et al. Recent advances in high-pressure science and technology [J]. Matter and Radiation at Extremes, 2016, 1(1): 59–75. doi: 10.1016/j.mre.2016.01.005
[3]	XU M L, LI Y W, MA Y M. Materials by design at high pressures [J]. Chemical Science, 2022, 13(2): 329–344. doi: 10.1039/d1sc04239d
[4]	PICKARD C J, NEEDS R J. Ab initio random structure searching [J]. Journal of Physics: Condensed Matter, 2011, 23(5): 053201. doi: 10.1088/0953-8984/23/5/053201
[5]	WALES D J, DOYE J P K. Global optimization by basin-hopping and the lowest energy structures of lennard-jones clusters containing up to 110 atoms [J]. The Journal of Physical Chemistry A, 1997, 101(28): 5111–5116. doi: 10.1021/jp970984n
[6]	WANG Y C, LYU J, ZHU L, et al. CALYPSO: a method for crystal structure prediction [J]. Computer Physics Communications, 2012, 183(10): 2063–2070. doi: 10.1016/j.cpc.2012.05.008
[7]	GOEDECKER S. Minima hopping: an efficient search method for the global minimum of the potential energy surface of complex molecular systems [J]. The Journal of Chemical Physics, 2004, 120(21): 9911–9917. doi: 10.1063/1.1724816
[8]	GLASS C W, OGANOV A R, HANSEN N. USPEX—evolutionary crystal structure prediction [J]. Computer Physics Communications, 2006, 175(11/12): 713–720. doi: 10.1016/j.cpc.2006.07.020
[9]	XIA K, GAO H, LIU C, et al. A novel superhard tungsten nitride predicted by machine-learning accelerated crystal structure search [J]. Science Bulletin, 2018, 63(13): 817–824. doi: 10.1016/j.scib.2018.05.027
[10]	WANG J J, GAO H, HAN Y, et al. MAGUS: machine learning and graph theory assisted universal structure searcher [J]. National Science Review, 2023, 10(7): nwad128. doi: 10.1093/nsr/nwad128
[11]	LONIE D C, ZUREK E. XtalOpt: an open-source evolutionary algorithm for crystal structure prediction [J]. Computer Physics Communications, 2011, 182(2): 372–387. doi: 10.1016/j.cpc.2010.07.048
[12]	WANG Y C, LYU J, GAO P Y, et al. Crystal structure prediction via efficient sampling of the potential energy surface [J]. Accounts of Chemical Research, 2022, 55(15): 2068–2076. doi: 10.1021/acs.accounts.2c00243
[13]	LYU J, WANG Y C, ZHU L, et al. Predicted novel high-pressure phases of lithium [J]. Physical Review Letters, 2011, 106(1): 015503. doi: 10.1103/PhysRevLett.106.015503
[14]	MARQUÉS M, MCMAHON M I, GREGORYANZ E, et al. Crystal structures of dense lithium: a metal-semiconductor-metal transition [J]. Physical Review Letters, 2011, 106(9): 095502. doi: 10.1103/PhysRevLett.106.095502
[15]	MA Y M, EREMETS M, OGANOV A R, et al. Transparent dense sodium [J]. Nature, 2009, 458(7235): 182–185. doi: 10.1038/nature07786
[16]	WANG X Y, WANG Z Y, GAO P Y, et al. Data-driven prediction of complex crystal structures of dense lithium [J]. Nature Communications, 2023, 14(1): 2924. doi: 10.1038/s41467-023-38650-y
[17]	WANG H, TSE J S, TANAKA K, et al. Superconductive sodalite-like clathrate calcium hydride at high pressures [J]. Proceedings of the National Academy of Sciences of the United States of America, 2012, 109(17): 6463–6466. doi: 10.1073/pnas.1118168109
[18]	PENG F, SUN Y, PICKARD C J, et al. Hydrogen clathrate structures in rare earth hydrides at high pressures: possible route to room-temperature superconductivity [J]. Physical Review Letters, 2017, 119(10): 107001. doi: 10.1103/PhysRevLett.119.107001
[19]	LIU H Y, NAUMOV I I, HOFFMANN R, et al. Potential high-T_c superconducting lanthanum and yttrium hydrides at high pressure [J]. Proceedings of the National Academy of Sciences of the United States of America, 2017, 114(27): 6990–6995. doi: 10.1073/pnas.1704505114
[20]	WANG Z Y, LUO X S, WANG Q C, et al. Advances in high-pressure materials discovery enabled by machine learning [J]. Matter and Radiation at Extremes, 2025, 10(3): 033801. doi: 10.1063/5.0255385
[21]	BROWN T B, MANN B, RYDER N, et al. Language models are few-shot learners [C]//Proceedings of the 34th International Conference on Neural Information Processing Systems. Vancouver: Curran Associates Inc., 2020: 159.
[22]	ROMBACH R, BLATTMANN A, LORENZ D, et al. High-resolution image synthesis with latent diffusion models [C]//2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans: IEEE, 2022: 10674–10685.
[23]	WANG G J, WANG C R, ZHANG X G, et al. Machine learning interatomic potential: bridge the gap between small-scale models and realistic device-scale simulations [J]. iScience, 2024, 27(5): 109673. doi: 10.1016/j.isci.2024.109673
[24]	HAN X Q, WANG X D, XU M Y, et al. AI-driven inverse design of materials: past, present, and future [J]. Chinese Physics Letters, 2025, 42(2): 027403. doi: 10.1088/0256-307X/42/2/027403
[25]	YIN J Y, WANG Y W, CHEN J Z Y, et al. Construction of a pathway map on a complicated energy landscape [J]. Physical Review Letters, 2020, 124(9): 090601. doi: 10.1103/PhysRevLett.124.090601
[26]	WU S Q, JI M, WANG C Z, et al. An adaptive genetic algorithm for crystal structure prediction [J]. Journal of Physics: Condensed Matter, 2013, 26(3): 035402. doi: 10.1088/0953-8984/26/3/035402
[27]	GALE J D. GULP: a computer program for the symmetry-adapted simulation of solids [J]. Journal of the Chemical Society, Faraday Transactions, 1997, 93(4): 629–637. doi: 10.1039/a606455h
[28]	BAHMANN S, KORTUS J. EVO—evolutionary algorithm for crystal structure prediction [J]. Computer Physics Communications, 2013, 184(6): 1618–1625. doi: 10.1016/j.cpc.2013.02.007
[29]	OAKLEY M T, JOHNSTON R L, WALES D J. Symmetrisation schemes for global optimisation of atomic clusters [J]. Physical Chemistry Chemical Physics, 2013, 15(11): 3965. doi: 10.1039/c3cp44332a
[30]	TIPTON W W, HENNIG R G. A grand canonical genetic algorithm for the prediction of multi-component phase diagrams and testing of empirical potentials [J]. Journal of Physics: Condensed Matter, 2013, 25(49): 495401. doi: 10.1088/0953-8984/25/49/495401
[31]	TRIBELLO G A, BONOMI M, BRANDUARDI D, et al. PLUMED 2: new feathers for an old bird [J]. Computer Physics Communications, 2014, 185(2): 604–613. doi: 10.1016/j.cpc.2013.09.018
[32]	CURTIS F, LI X Y, ROSE T, et al. GAtor: a first-principles genetic algorithm for molecular crystal structure prediction [J]. Journal of Chemical Theory and Computation, 2018, 14(4): 2246–2264. doi: 10.1021/acs.jctc.7b01152
[33]	BAIL A L. Inorganic structure prediction with GRINSP [J]. Journal of Applied Crystallography, 2005, 38(2): 389–395. doi: 10.1107/S0021889805002384
[34]	ZHANG Y Y, GAO W G, CHEN S Y, et al. Inverse design of materials by multi-objective differential evolution [J]. Computational Materials Science, 2015, 98: 51–55. doi: 10.1016/j.commatsci.2014.10.054
[35]	TOM R, ROSE T, BIER I, et al. Genarris 2.0: a random structure generator for molecular crystals [J]. Computer Physics Communications, 2020, 250: 107170. doi: 10.1016/j.cpc.2020.107170
[36]	HAJINAZAR S, THORN A, SANDOVAL E D, et al. MAISE: construction of neural network interatomic models and evolutionary structure optimization [J]. Computer Physics Communications, 2021, 259: 107679. doi: 10.1016/j.cpc.2020.107679
[37]	DOMINGOS R, SHAIK K M, MILITZER B. Prediction of novel high-pressure H₂O-NaCl and carbon oxide compounds with a symmetry-driven structure search algorithm [J]. Physical Review B, 2018, 98(17): 174107. doi: 10.1103/PhysRevB.98.174107
[38]	LIU Z L. MUSE: multi-algorithm collaborative crystal structure prediction [J]. Computer Physics Communications, 2014, 185(7): 1893–1900. doi: 10.1016/j.cpc.2014.03.017
[39]	KAAPPA S, DEL RÍO E G, JACOBSEN K W. Global optimization of atomic structures with gradient-enhanced Gaussian process regression [J]. Physical Review B, 2021, 103(17): 174114. doi: 10.1103/PhysRevB.103.174114
[40]	CHEN Z H, JIA W L, JIANG X W, et al. SGO: a fast engine for ab initio atomic structure global optimization by differential evolution [J]. Computer Physics Communications, 2017, 219: 35–44. doi: 10.1016/j.cpc.2017.05.005
[41]	YAMASHITA T, KANEHIRA S, SATO N, et al. CrySPY: a crystal structure prediction tool accelerated by machine learning [J]. Science and Technology of Advanced Materials: Methods, 2021, 1(1): 87–97. doi: 10.1080/27660400.2021.1943171
[42]	BISBO M K, HAMMER B. Efficient global structure optimization with a machine-learned surrogate model [J]. Physical Review Letters, 2020, 124(8): 086102. doi: 10.1103/PhysRevLett.124.086102
[43]	SHAO X C, LYU J, LIU P, et al. A symmetry-orientated divide-and-conquer method for crystal structure prediction [J]. The Journal of Chemical Physics, 2022, 156(1): 014105. doi: 10.1063/5.0074677
[44]	KIRKPATRICK S, GELATT C D, VECCHI M P. Optimization by simulated annealing [J]. Science, 1983, 220(4598): 671–680. doi: 10.1126/science.220.4598.671
[45]	MARTOŇÁK R, LAIO A, PARRINELLO M. Predicting crystal structures: the Parrinello-Rahman method revisited [J]. Physical Review Letters, 2003, 90(7): 075503. doi: 10.1103/PhysRevLett.90.075503
[46]	TONG Q C, XUE L T, LYU J, et al. Accelerating CALYPSO structure prediction by data-driven learning of a potential energy surface [J]. Faraday Discussions, 2018, 211: 31–43. doi: 10.1039/c8fd00055g
[47]	WANG Z Y, WANG X Y, LUO X S, et al. Concurrent learning scheme for crystal structure prediction [J]. Physical Review B, 2024, 109(9): 094117. doi: 10.1103/PhysRevB.109.094117
[48]	LUO X S, WANG Z Y, WANG Q C, et al. CrystalFlow: a flow-based generative model for crystalline materials [J]. Nature Communications, 2025, 16(1): 9267. doi: 10.1038/s41467-025-64364-4
[49]	LUO X S, WANG Z Y, GAO P Y, et al. Deep learning generative model for crystal structure prediction [J]. NPJ Computational Materials, 2024, 10(1): 254. doi: 10.1038/s41524-024-01443-y
[50]	XIN Y, LIU P, XIE Z H, et al. Bridging theory and experiment in materials discovery: machine-learning-assisted prediction of synthesizable structures [J]. Physical Review B, 2026, 113(5): 054102. doi: 10.1103/6t5z-tqym
[51]	WANG Z Y, WANG Q C, LI H L, et al. High-pressure crystal structure database [J]. Science Bulletin, 2026.
[52]	BEHLER J. Four generations of high-dimensional neural network potentials [J]. Chemical Reviews, 2021, 121(16): 10037–10072. doi: 10.1021/acs.chemrev.0c00868
[53]	DERINGER V L, BARTÓK A P, BERNSTEIN N, et al. Gaussian process regression for materials and molecules [J]. Chemical Reviews, 2021, 121(16): 10073–10141. doi: 10.1021/acs.chemrev.1c00022
[54]	BLANK T B, BROWN S D, CALHOUN A W, et al. Neural network models of potential energy surfaces [J]. The Journal of Chemical Physics, 1995, 103(10): 4129–4137. doi: 10.1063/1.469597
[55]	BEHLER J, PARRINELLO M. Generalized neural-network representation of high-dimensional potential-energy surfaces [J]. Physical Review Letters, 2007, 98(14): 146401. doi: 10.1103/PhysRevLett.98.146401
[56]	BARTÓK A P, PAYNE M C, KONDOR R, et al. Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons [J]. Physical Review Letters, 2010, 104(13): 136403. doi: 10.1103/PhysRevLett.104.136403
[57]	SHAPEEV A V. Moment tensor potentials: a class of systematically improvable interatomic potentials [J]. Multiscale Modeling & Simulation, 2016, 14(3): 1153–1173. doi: 10.1137/15M1054183
[58]	THOMPSON A P, SWILER L P, TROTT C R, et al. Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials [J]. Journal of Computational Physics, 2015, 285: 316–330. doi: 10.1016/j.jcp.2014.12.018
[59]	ZHANG L F, HAN J Q, WANG H, et al. End-to-end symmetry preserving inter-atomic potential energy model for finite and extended systems [C]//Proceedings of the 32nd International Conference on Neural Information Processing Systems. Montréal: Curran Associates Inc., 2018: 4441–4451.
[60]	TAKAMOTO S, SHINAGAWA C, MOTOKI D, et al. Towards universal neural network potential for material discovery applicable to arbitrary combination of 45 elements [J]. Nature Communications, 2022, 13(1): 2991. doi: 10.1038/s41467-022-30687-9
[61]	CHEN C, ONG S P. A universal graph deep learning interatomic potential for the periodic table [J]. Nature Computational Science, 2022, 2(11): 718–728. doi: 10.1038/s43588-022-00349-3
[62]	DENG B W, ZHONG P C, JUN K, et al. CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling [J]. Nature Machine Intelligence, 2023, 5(9): 1031–1041. doi: 10.1038/s42256-023-00716-3
[63]	ZHANG D, LIU X Z J, ZHANG X, et al. DPA-2: a large atomic model as a multi-task learner [J]. NPJ Computational Materials, 2024, 10(1): 293. doi: 10.1038/s41524-024-01493-2
[64]	ZHANG D, PENG A Y, CAI C, et al. A graph neural network for the era of large atomistic models [J]. NPJ Computational Materials, 2026, 12(1): 276. doi: 10.1038/s41524-026-02146-2
[65]	LI T C, LI W T, PENG A Y, et al. DPA4: pushing the accuracy-cost frontier of interatomic potentials with EMFA SO(2) convolution [EB/OL]. ArXiv: 2606.02419. (2026-08-20) [2026-09-01]. https://arxiv.org/abs/2606.02419.
[66]	LI T C, XUE J M, ZHANG L F, et al. Universal machine-learning molecular dynamics at the speed of empirical potentials [EB/OL]. ArXiv: 2608.19041. (2026-08-20) [2026-09-01]. https://arxiv.org/abs/2608.19041.
[67]	BATATIA I, BENNER P, CHIANG Y, et al. A foundation model for atomistic materials chemistry [J]. The Journal of Chemical Physics, 2025, 163(18): 184110. doi: 10.1063/5.0297006
[68]	YANG H, HU C X, ZHOU Y C, et al. MatterSim: a deep learning atomistic model across elements, temperatures and pressures [EB/OL]. ArXiv: 2405.04967. (2024-05-10) [2026-09-01]. https://arxiv.org/abs/2405.04967.
[69]	MAZITOV A, BIGI F, KELLNER M, et al. PET-MAD as a lightweight universal interatomic potential for advanced materials modeling [J]. Nature Communications, 2025, 16(1): 10653. doi: 10.1038/s41467-025-65662-7
[70]	KIM J, YOU J M, PARK Y, et al. Optimizing cross-domain transfer for universal machine learning interatomic potentials [J]. Nature Communications, 2026, 17(1): 3432. doi: 10.1038/s41467-026-70195-8
[71]	LYSOGORSKIY Y, BOCHKAREV A, DRAUTZ R. Graph atomic cluster expansion for foundational machine learning interatomic potentials [J]. NPJ Computational Materials, 2026, 12(1): 114. doi: 10.1038/s41524-026-01979-1
[72]	XU Z M, XIE W B, XIE D Q, et al. Towards universal property prediction in Cartesian space: TACE is all you need [EB/OL]. ArXiv: 2509.14961. (2025-09-18) [2026-09-01]. https://arxiv.org/abs/2509.14961v1.
[73]	XU Z M, XIE W B, HU P. Edge cluster expansion with radial rotary attention for interatomic potentials [EB/OL]. ArXiv: 2607.10664. (2026-07-12) [2026-09-01]. https://arxiv.org/abs/2607.10664.
[74]	LIAO Y L, SMIDT T E. Equiformer: equivariant graph attention transformer for 3D atomistic graphs [C]//The Eleventh International Conference on Learning Representations. Kigali: OpenReview. Net, 2023.
[75]	LIAO Y L, WOOD B M, DAS A, et al. EquiformerV2: improved equivariant transformer for scaling to higher-degree representations [C]//The Twelfth International Conference on Learning Representations. Vienna: OpenReview. Net, 2024.
[76]	LIAO Y L, HOFFMAN A J, SHEN S C, et al. EquiformerV3: scaling efficient, expressive, and general SE(3)-equivariant graph attention transformers [EB/OL]. ArXiv: 2604.09130. (2026-04-10) [2026-09-01]. https://arxiv.org/abs/2604.09130.
[77]	BATZNER S, MUSAELIAN A, SUN L X, et al. E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials [J]. Nature Communications, 2022, 13(1): 2453. doi: 10.1038/s41467-022-29939-5
[78]	NEUMANN M, GIN J, RHODES B, et al. Orb: a fast, scalable neural network potential [EB/OL]. ArXiv: 2410.22570. (2024-10-29) [2026-09-01]. https://arxiv.org/abs/2410.22570.
[79]	MERCHANT A, BATZNER S, SCHOENHOLZ S S, et al. Scaling deep learning for materials discovery [J]. Nature, 2023, 624(7990): 80–85. doi: 10.1038/s41586-023-06735-9
[80]	XIA J F, ZHANG Y L, JIANG B. The evolution of machine learning potentials for molecules, reactions and materials [J]. Chemical Society Reviews, 2025, 54(10): 4790–4821. doi: 10.1039/D5CS00104H
[81]	YUAN E C Y, LIU Y S, CHEN J M, et al. Foundation models for atomistic simulation of chemistry and materials [J]. Nature Reviews Chemistry, 2026, 10(3): 212–230. doi: 10.1038/s41570-025-00793-5
[82]	DERINGER V L, CSÁNYI G, PROSERPIO D M. Extracting crystal chemistry from amorphous carbon structures [J]. ChemPhysChem, 2017, 18(8): 873–877. doi: 10.1002/cphc.201700151
[83]	WANG H D, ZHANG Y Z, ZHANG L F, et al. Crystal structure prediction of binary alloys via deep potential [J]. Frontiers in Chemistry, 2020, 8: 589795. doi: 10.3389/fchem.2020.589795
[84]	SHANG C, LIU Z P. Stochastic surface walking method for structure prediction and pathway searching [J]. Journal of Chemical Theory and Computation, 2013, 9(3): 1838–1845. doi: 10.1021/ct301010b
[85]	SHANG C, ZHANG X J, LIU Z P. Stochastic surface walking method for crystal structure and phase transition pathway prediction [J]. Physical Chemistry Chemical Physics, 2014, 16(33): 17845–17856. doi: 10.1039/C4CP01485E
[86]	HUANG S D, SHANG C, KANG P L, et al. Atomic structure of boron resolved using machine learning and global sampling [J]. Chemical Science, 2018, 9(46): 8644–8655. doi: 10.1039/C8SC03427C
[87]	TONG Q C, GAO P Y, LIU H Y, et al. Combining machine learning potential and structure prediction for accelerated materials design and discovery [J]. The Journal of Physical Chemistry Letters, 2020, 11(20): 8710–8720. doi: 10.1021/acs.jpclett.0c02357
[88]	DERINGER V L, PICKARD C J, CSÁNYI G. Data-driven learning of total and local energies in elemental boron [J]. Physical Review Letters, 2018, 120(15): 156001. doi: 10.1103/PhysRevLett.120.156001
[89]	DERINGER V L, PROSERPIO D M, CSÁNYI G, et al. Data-driven learning and prediction of inorganic crystal structures [J]. Faraday Discussions, 2018, 211: 45–59. doi: 10.1039/c8fd00034d
[90]	BERNSTEIN N, CSÁNYI G, DERINGER V L. De novo exploration and self-guided learning of potential-energy surfaces [J]. NPJ Computational Materials, 2019, 5(1): 99. doi: 10.1038/s41524-019-0236-6
[91]	YANG Q P, LYU J, TONG Q C, et al. Hard and superconducting cubic boron phase via swarm-intelligence structural prediction driven by a machine-learning potential [J]. Physical Review B, 2021, 103(2): 024505. doi: 10.1103/PhysRevB.103.024505
[92]	PODRYABINKIN E V, SHAPEEV A V. Active learning of linearly parametrized interatomic potentials [J]. Computational Materials Science, 2017, 140: 171–180. doi: 10.1016/j.commatsci.2017.08.031
[93]	PODRYABINKIN E V, TIKHONOV E V, SHAPEEV A V, et al. Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning [J]. Physical Review B, 2019, 99(6): 064114. doi: 10.1103/PhysRevB.99.064114
[94]	CHENG G J, GONG X G, YIN W J. Crystal structure prediction by combining graph network and optimization algorithm [J]. Nature Communications, 2022, 13(1): 1492. doi: 10.1038/s41467-022-29241-4
[95]	PICKARD C J. Ephemeral data derived potentials for random structure search [J]. Physical Review B, 2022, 106(1): 014102. doi: 10.1103/PhysRevB.106.014102
[96]	SALZBRENNER P T, JOO S H, CONWAY L J, et al. Developments and further applications of ephemeral data derived potentials [J]. The Journal of Chemical Physics, 2023, 159(14): 144801. doi: 10.1063/5.0158710
[97]	LIU C, SHI J Y, GAO H, et al. Mixed coordination silica at megabar pressure [J]. Physical Review Letters, 2021, 126(3): 035701. doi: 10.1103/PhysRevLett.126.035701
[98]	PAN S N, HUANG T H, VAZAN A, et al. Magnesium oxide-water compounds at megabar pressure and implications on planetary interiors [J]. Nature Communications, 2023, 14(1): 1165. doi: 10.1038/s41467-023-36802-8
[99]	WANG X M, WANG Y, WANG J J, et al. Pressure stabilized lithium-aluminum compounds with both superconducting and superionic behaviors [J]. Physical Review Letters, 2022, 129(24): 246403. doi: 10.1103/PhysRevLett.129.246403
[100]	WANG X Y, ZHANG C Q, WANG Z Y, et al. Computational discovery of high-temperature superconducting ternary hydrides via deep learning [J]. National Science Review, 2026, 13(6): nwag030. doi: 10.1093/nsr/nwag030
[101]	WANG Y N, WANG X Y, WANG Z Y, et al. OpenCSP: a deep learning framework for crystal structure prediction from ambient to high pressure [J]. NPJ Computational Materials, 2026, 12(1): 318. doi: 10.1038/s41524-026-02180-0
[102]	DE BREUCK P P, WANG H C, RIGNANESE G M, et al. Generative AI for crystal structures: a review [J]. NPJ Computational Materials, 2025, 11(1): 370. doi: 10.1038/s41524-025-01881-2
[103]	NOUIRA A, SOKOLOVSKA N, CRIVELLO J C. CrystalGAN: learning to discover crystallographic structures with generative adversarial networks [C]//Proceedings of the AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering. Palo Alto: Stanford University, 2019.
[104]	HOFFMANN J, MAESTRATI L, SAWADA Y, et al. Data-driven approach to encoding and decoding 3-D crystal structures [EB/OL]. ArXiv: 1909.00949. (2019-09-03) [2026-09-01]. https://arxiv.org/abs/1909.00949.
[105]	NOH J, KIM J, STEIN H S, et al. Inverse design of solid-state materials via a continuous representation [J]. Matter, 2019, 1(5): 1370–1384. doi: 10.1016/j.matt.2019.08.017
[106]	KIM S, NOH J, GU G H, et al. Generative adversarial networks for crystal structure prediction [J]. ACS Central Science, 2020, 6(8): 1412–1420. doi: 10.1021/acscentsci.0c00426
[107]	REN Z K, TIAN S I P, NOH J, et al. An invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties [J]. Matter, 2022, 5(1): 314–335. doi: 10.1016/j.matt.2021.11.032
[108]	RYAN K, LENGYEL J, SHATRUK M. Crystal structure prediction via deep learning [J]. Journal of the American Chemical Society, 2018, 140(32): 10158–10168. doi: 10.1021/jacs.8b03913
[109]	LEE I H, CHANG K J. Crystal structure prediction in a continuous representative space [J]. Computational Materials Science, 2021, 194: 110436. doi: 10.1016/j.commatsci.2021.110436
[110]	COURT C J, YILDIRIM B, JAIN A, et al. 3-D inorganic crystal structure generation and property prediction via representation learning [J]. Journal of Chemical Information and Modeling, 2020, 60(10): 4518–4535. doi: 10.1021/acs.jcim.0c00464
[111]	KIM B, LEE S, KIM J. Inverse design of porous materials using artificial neural networks [J]. Science Advances, 2020, 6(1): eaax9324. doi: 10.1126/sciadv.aax9324
[112]	LONG T, FORTUNATO N M, OPAHLE I, et al. Constrained crystals deep convolutional generative adversarial network for the inverse design of crystal structures [J]. NPJ Computational Materials, 2021, 7(1): 66. doi: 10.1038/s41524-021-00526-4
[113]	PARK J, GILL A P S, MOOSAVI S M, et al. Inverse design of porous materials: a diffusion model approach [J]. Journal of Materials Chemistry A, 2024, 12(11): 6507–6514. doi: 10.1039/D3TA06274K
[114]	ZHU R M, NONG W, YAMAZAKI S, et al. WyCryst: Wyckoff inorganic crystal generator framework [J]. Matter, 2024, 7(10): 3469–3488. doi: 10.1016/j.matt.2024.05.042
[115]	SINHA A, JIA S Y, FUNG V. Representation-space diffusion models for generating periodic materials [EB/OL]. ArXiv: 2408.07213. (2024-08-13) [2026-09-01]. https://arxiv.org/abs/2408.07213.
[116]	XIE T, FU X, GANEA O E, et al. Crystal diffusion variational autoencoder for periodic material generation [C]//The Tenth International Conference on Learning Representations. OpenReview. Net, 2022.
[117]	KLIPFEL A, FRÉGIER Y, SAYEDE A, et al. Unified model for crystalline material generation [C]//Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence. Macao, China: IJCAI. Org, 2023: 6031–6039.
[118]	HAN X Q, OUYANG Z F, GUO P J, et al. InvDesFlow: an AI-driven materials inverse design workflow to explore possible high-temperature superconductors [J]. Chinese Physics Letters, 2025, 42(4): 047301. doi: 10.1088/0256-307X/42/4/047301
[119]	LIN P J, CHEN P, JIAO R, et al. Equivariant diffusion for crystal structure prediction [C]//Proceedings of the 41st International Conference on Machine Learning. Vienna: OpenReview. Net, 2024: 29890–29913.
[120]	NGUYEN T M, TAWFIK S A, TRAN T, et al. Hierarchical GFlownet for crystal structure generation [C]//AI for Accelerated Materials Design Workshop at NeurIPS. 2023.
[121]	YE C Y, WENG H M, WU Q S. Con-CDVAE: a method for the conditional generation of crystal structures [J]. Computational Materials Today, 2024, 1: 100003. doi: 10.1016/j.commt.2024.100003
[122]	JIAO R, HUANG W B, LIN P J, et al. Crystal structure prediction by joint equivariant diffusion [C]//Proceedings of the 37th International Conference on Neural Information Processing Systems. New Orleans: Curran Associates Inc., 2023: 767.
[123]	MILLER B K, CHEN R T Q, SRIRAM A, et al. FlowMM: generating materials with Riemannian flow matching [C]//Proceedings of the 41st International Conference on Machine Learning. Vienna: OpenReview. Net, 2024: 35664−35686.
[124]	ZENI C, PINSLER R, ZÜGNER D, et al. A generative model for inorganic materials design [J]. Nature, 2025, 639(8055): 624–632. doi: 10.1038/s41586-025-08628-5
[125]	WU H L, SONG Y X, GONG J J, et al. A periodic Bayesian flow for material generation [C]//The Thirteenth International Conference on Learning Representations. Singapore: OpenReview. Net, 2025.
[126]	XIAO H, LI R, SHI X Y, et al. An invertible, invariant crystal representation for inverse design of solid-state materials using generative deep learning [J]. Nature Communications, 2023, 14(1): 7027. doi: 10.1038/s41467-023-42870-7
[127]	ANTUNES L M, BUTLER K T, GRAU-CRESPO R. Crystal structure generation with autoregressive large language modeling [J]. Nature Communications, 2024, 15(1): 10570. doi: 10.1038/s41467-024-54639-7
[128]	SITAPURE N, KWON J S I. CrystalGPT: enhancing system-to-system transferability in crystallization prediction and control using time-series-transformers [J]. Computers & Chemical Engineering, 2023, 177: 108339. doi: 10.1016/j.compchemeng.2023.108339
[129]	CHEN Y, WANG X R, DENG X B, et al. MatterGPT: a generative transformer for multi-property inverse design of solid-state materials [EB/OL]. ArXiv: 2408.07608. (2024-08-14) [2026-09-01]. https://arxiv.org/abs/2408.07608.
[130]	WANG B N, XU Z Y, HAN Z Y, et al. SLICES-PLUS: a crystal representation leveraging spatial symmetry [J]. Materials & Design, 2025, 253: 113856. doi: 10.1016/j.matdes.2025.113856
[131]	KAZEEV N, ZHU R M, ROMANOV I, et al. WyckoffTransformer: generation of symmetric crystal [C]//Proceedings of the 38th International Conference on Neural Information Processing Systems. Vancouver: Curran Associates Inc., 2024.
[132]	CAO Z D, LUO X S, LYU J, et al. Space group informed transformer for crystalline materials generation [J]. Science Bulletin, 2025, 70(21): 3522–3533. doi: 10.1016/j.scib.2025.09.035
[133]	GRUVER N, SRIRAM A, MADOTTO A, et al. Fine-tuned language models generate stable inorganic materials as text [C]//Proceedings of the Twelfth International Conference on Learning Representations. Vienna: OpenReview. Net, 2024.
[134]	SRIRAM A, MILLER B K, CHEN R T Q, et al. FlowLLM: flow matching for material generation with large language models as base distributions [C]//Proceedings of the 38th International Conference on Neural Information Processing Systems. Vancouver: Curran Associates Inc., 2024: 1464.
[135]	CHOUDHARY K. AtomGPT: atomistic generative pretrained transformer for forward and inverse materials design [J]. The Journal of Physical Chemistry Letters, 2024, 15(27): 6909–6917. doi: 10.1021/acs.jpclett.4c01126
[136]	MOHANTY T, MEHTA M, SAYEED H M, et al. CrysText: a generative AI approach for text-conditioned crystal structure generation using LLM [J]. Integrating Materials and Manufacturing Innovation, 2026, 15(2): 416–430. doi: 10.1007/s40192-026-00451-8
[137]	YAN K Q, LI X E, LING H Y, et al. Invariant tokenization of crystalline materials for language model enabled generation [C]//Proceedings of the 38th International Conference on Neural Information Processing Systems. Vancouver: Curran Associates Inc., 2024: 3971.
[138]	PARK H, ONWULI A, WALSH A. Exploration of crystal chemical space using text-guided generative artificial intelligence [J]. Nature Communications, 2025, 16(1): 4379. doi: 10.1038/s41467-025-59636-y
[139]	JIAO R, HUANG W B, LIU Y, et al. Space group constrained crystal generation [C]//The Twelfth International Conference on Learning Representations. Vienna: OpenReview. Net, 2024.
[140]	LEVY D, PANIGRAHI S S, KABA S O, et al. SymmCD: symmetry-preserving crystal generation with diffusion models [C]//The Thirteenth International Conference on Learning Representations. Singapore: OpenReview. Net, 2025.
[141]	EKSTRÖM KELVINIUS F, ANDERSSON O B, PARACKAL A S, et al. WyckoffDiff–a generative diffusion model for crystal symmetry [C]//Proceedings of the 42nd International Conference on Machine Learning. Vancouver: OpenReview. Net, 2025: 15130−15147.
[142]	RUPLE L, TORRESI L, SCHOPMANS H, et al. Symmetry-aware Bayesian flow networks for crystal generation [J]. NPJ Computational Materials, 2026, 12(1): 182. doi: 10.1038/s41524-026-02140-8
[143]	JAIN A, ONG S P, HAUTIER G, et al. Commentary: the materials project: a materials genome approach to accelerating materials innovation [J]. APL Materials, 2013, 1(1): 011002. doi: 10.1063/1.4812323
[144]	LI W L, LI H L, LIU C H, et al. Conditional generative modeling for amorphous multi-element materials [J]. Research Square, 2025.
[145]	NIU G L, MAN L J, MU P Y, et al. High-pressure generation above 45 GPa over a 10 mm³ volume with a multi-anvil press [J]. Review of Scientific Instruments, 2026, 97(4): 043903. doi: 10.1063/5.0323039
[146]	SHANG Y C, LIU Z D, DONG J J, et al. Ultrahard bulk amorphous carbon from collapsed fullerene [J]. Nature, 2021, 599(7886): 599–604. doi: 10.1038/s41586-021-03882-9
[147]	NI K, PAN F, ZHU Y W. Structural evolution of C₆₀ molecular crystal predicted by neural network potential [J]. Advanced Functional Materials, 2022, 32(42): 2203894. doi: 10.1002/adfm.202203894
[148]	CHEN D S, CHEN G W, LYU L, et al. General approach for synthesizing hexagonal diamond by heating post-graphite phases [J]. Nature Materials, 2025, 24(4): 513–518. doi: 10.1038/s41563-025-02126-9
[149]	YANG L X, LAU K C, ZENG Z D, et al. Synthesis of bulk hexagonal diamond [J]. Nature, 2025, 644(8076): 370–375. doi: 10.1038/s41586-025-09343-x
[150]	LAI S L, YANG X G, SHI J Y, et al. Bulk hexagonal diamond [J]. Nature, 2026, 651(8106): 621–625. doi: 10.1038/s41586-026-10212-4
[151]	ISHII T, CRINITI G, OHTANI E, et al. Superhydrous aluminous silica phases as major water hosts in high-temperature lower mantle [J]. Proceedings of the National Academy of Sciences of the United States of America, 2022, 119(44): e2211243119. doi: 10.1073/pnas.2211243119
[152]	SHAO S, LYU J, LI X, et al. New MgSiO₄H₂ phases as potential primary water carriers into the deep Earth [J]. Physical Review Letters, 2024, 133(21): 214101. doi: 10.1103/PhysRevLett.133.214101
[153]	SAN-MIGUEL A. How to make macroscale non-crystalline diamonds [J]. Nature, 2021, 599(7886): 563–564. doi: 10.1038/d41586-021-02957-x
[154]	SHANG Y C, YAO M G, LIU Z D, et al. Enhancement of short/medium-range order and thermal conductivity in ultrahard sp³ amorphous carbon by C₇₀ precursor [J]. Nature Communications, 2023, 14(1): 7860. doi: 10.1038/s41467-023-42195-5
[155]	LI H L, WANG F Y, WANG Z Y, et al. Pressure-induced discovery of a topological phase of Bi₄Br₄ with AA-stacking [J]. The Journal of Physical Chemistry Letters, 2026, 17(13): 3759–3765.
[156]	LI B, JI C, YANG W G, et al. Diamond anvil cell behavior up to 4 Mbar [J]. Proceedings of the National Academy of Sciences of the United States of America, 2018, 115(8): 1713–1717. doi: 10.1073/pnas.1721425115
[157]	WIGNER E, HUNTINGTON H B. On the possibility of a metallic modification of hydrogen [J]. The Journal of Chemical Physics, 1935, 3(12): 764–770. doi: 10.1063/1.1749590
[158]	ASHCROFT N W. Metallic hydrogen: a high-temperature superconductor? [J]. Physical Review Letters, 1968, 21(26): 1748–1749. doi: 10.1103/PhysRevLett.21.1748
[159]	BARBEE T W, GARCÍA A, COHEN M L. First-principles prediction of high-temperature superconductivity in metallic hydrogen [J]. Nature, 1989, 340(6232): 369–371. doi: 10.1038/340369a0
[160]	MCMAHON J M, CEPERLEY D M. High-temperature superconductivity in atomic metallic hydrogen [J]. Physical Review B, 2011, 84(14): 144515. doi: 10.1103/PhysRevB.84.144515
[161]	PICKARD C J, NEEDS R J. Structure of phase Ⅲ of solid hydrogen [J]. Nature Physics, 2007, 3(7): 473–476. doi: 10.1038/nphys625
[162]	ZHANG L J, NIU Y L, LI Q, et al. Ab initio prediction of superconductivity in molecular metallic hydrogen under high pressure [J]. Solid State Communications, 2007, 141(11): 610–614. doi: 10.1016/j.ssc.2006.12.029
[163]	CUDAZZO P, PROFETA G, SANNA A, et al. Ab initio description of high-temperature superconductivity in dense molecular hydrogen [J]. Physical Review Letters, 2008, 100(25): 257001. doi: 10.1103/physrevlett.100.257001
[164]	LIU H Y, ZHU L, CUI W W, et al. Room-temperature structures of solid hydrogen at high pressures [J]. The Journal of Chemical Physics, 2012, 137(7): 074501. doi: 10.1063/1.4745186
[165]	LIU H Y, WANG H, MA Y M. Quasi-molecular and atomic phases of dense solid hydrogen [J]. The Journal of Physical Chemistry C, 2012, 116(16): 9221–9226. doi: 10.1021/jp301596v
[166]	DIAS R P, SILVERA I F. Observation of the Wigner-Huntington transition to metallic hydrogen [J]. Science, 2017, 355(6326): 715–718. doi: 10.1126/science.aal1579
[167]	DALLADAY-SIMPSON P, HOWIE R T, GREGORYANZ E. Evidence for a new phase of dense hydrogen above 325 gigapascals [J]. Nature, 2016, 529(7584): 63–67. doi: 10.1038/nature16164
[168]	LOUBEYRE P, OCCELLI F, DUMAS P. Synchrotron infrared spectroscopic evidence of the probable transition to metal hydrogen [J]. Nature, 2020, 577(7792): 631–635. doi: 10.1038/s41586-019-1927-3
[169]	EREMETS M I, DROZDOV A P, KONG P P, et al. Semimetallic molecular hydrogen at pressure above 350 GPa [J]. Nature Physics, 2019, 15(12): 1246–1249. doi: 10.1038/s41567-019-0646-x
[170]	GILMAN J J. Lithium dihydrogen fluoride—an approach to metallic hydrogen [J]. Physical Review Letters, 1971, 26(10): 546–548. doi: 10.1103/PhysRevLett.26.546
[171]	SKOSKIEWICZ T. Superconductivity in the palladium-hydrogen and palladium-nickel-hydrogen systems [J]. Physica Status Solidi (A), 1972, 11(2): K123–K126. doi: 10.1002/pssa.2210110253
[172]	STRITZKER B. High superconducting transition temperatures in the palladium-noble metal-hydrogen system [J]. Zeitschrift für Physik, 1974, 268(2): 261–264. doi: 10.1007/BF01669889
[173]	WELTER J M, JOHNEN F J. Superconducting transition temperature and low temperature resistivity in the niobium-hydrogen system [J]. Zeitschrift für Physik B Condensed Matter, 1977, 27(3): 227–232. doi: 10.1007/BF01325532
[174]	ASHCROFT N W. Hydrogen dominant metallic alloys: high temperature superconductors? [J]. Physical Review Letters, 2004, 92(18): 187002. doi: 10.1103/PhysRevLett.92.187002
[175]	FENG J, GROCHALA W, JAROŃ T, et al. Structures and potential superconductivity in SiH₄ at high pressure: en route to “metallic hydrogen” [J]. Physical Review Letters, 2006, 96(1): 017006. doi: 10.1103/PhysRevLett.96.017006
[176]	MARTINEZ-CANALES M, BERGARA A, FENG J, et al. Pressure induced metallization of Germane [J]. Journal of Physics and Chemistry of Solids, 2006, 67(9/10): 2095–2099. doi: 10.1016/j.jpcs.2006.05.050
[177]	TSE J S, YAO Y, TANAKA K. Novel superconductivity in metallic SnH₄ under high pressure [J]. Physical Review Letters, 2006, 98(11): 117004. doi: 10.1103/PhysRevLett.98.117004
[178]	EREMETS M I, TROJAN I A, MEDVEDEV S A, et al. Superconductivity in hydrogen dominant materials: silane [J]. Science, 2008, 319(5869): 1506–1509. doi: 10.1126/science.1153282
[179]	ZHANG L J, WANG Y C, ZHANG X X, et al. High-pressure phase transitions of solid HF, HCl, and HBr: an ab initio evolutionary study [J]. Physical Review B, 2010, 82(1): 014108. doi: 10.1103/PhysRevB.82.014108
[180]	GAO G Y, WANG H, BERGARA A, et al. Metallic and superconducting gallane under high pressure [J]. Physical Review B, 2011, 84(6): 064118. doi: 10.1103/PhysRevB.84.064118
[181]	DROZDOV A P, EREMETS M I, TROYAN I A. Superconductivity above 100 K in PH3 at high pressures [EB/OL]. ArXiv: 1508.06224. (2015-08-25) [2026-09-01]. https://arxiv.org/abs/1508.06224.
[182]	GU Q Y, LU P C, XIA K, et al. High-temperature superconducting phase of HBr under pressure predicted by first-principles calculations [J]. Physical Review B, 2017, 96(6): 064517. doi: 10.1103/PhysRevB.96.064517
[183]	LI Y W, HAO J, LIU H Y, et al. The metallization and superconductivity of dense hydrogen sulfide [J]. The Journal of Chemical Physics, 2014, 140(17): 174712. doi: 10.1063/1.4874158
[184]	DROZDOV A P, EREMETS M I, TROYAN I A, et al. Conventional superconductivity at 203 kelvin at high pressures in the sulfur hydride system [J]. Nature, 2015, 525(7567): 73–76. doi: 10.1038/nature14964
[185]	DUAN D F, LIU Y X, TIAN F B, et al. Pressure-induced metallization of dense (H₂S)₂H₂ with high-T_c superconductivity [J]. Scientific Reports, 2014, 4(1): 6968. doi: 10.1038/srep06968
[186]	ERREA I, CALANDRA M, PICKARD C J, et al. Quantum hydrogen-bond symmetrization in the superconducting hydrogen sulfide system [J]. Nature, 2016, 532(7597): 81–84. doi: 10.1038/nature17175
[187]	EINAGA M, SAKATA M, ISHIKAWA T, et al. Crystal structure of the superconducting phase of sulfur hydride [J]. Nature Physics, 2016, 12(9): 835–838. doi: 10.1038/nphys3760
[188]	LI Y W, WANG L, LIU H Y, et al. Dissociation products and structures of solid H₂S at strong compression [J]. Physical Review B, 2016, 93(2): 020103. doi: 10.1103/PhysRevB.93.020103
[189]	DUAN D F, HUANG X L, TIAN F B, et al. Pressure-induced decomposition of solid hydrogen sulfide [J]. Physical Review B, 2015, 91(18): 180502. doi: 10.1103/PhysRevB.91.180502
[190]	BERNSTEIN N, HELLBERG C S, JOHANNES M D, et al. What superconducts in sulfur hydrides under pressure and why [J]. Physical Review B, 2015, 91(6): 060511. doi: 10.1103/PhysRevB.91.060511
[191]	ERREA I, CALANDRA M, PICKARD C J, et al. High-pressure hydrogen sulfide from first principles: a strongly anharmonic phonon-mediated superconductor [J]. Physical Review Letters, 2015, 114(15): 157004. doi: 10.1103/PhysRevLett.114.157004
[192]	SCHILLING A, CANTONI M, GUO J D, et al. Superconductivity above 130 K in the Hg-Ba-Ca-Cu-O system [J]. Nature, 1993, 363(6424): 56–58. doi: 10.1038/363056a0
[193]	CHU C W, GAO L, CHEN F, et al. Superconductivity above 150 K in HgBa₂Ca₂Cu₃O_8+δ at high pressures [J]. Nature, 1993, 365(6444): 323–325. doi: 10.1038/365323a0
[194]	GAO L, XUE Y Y, CHEN F, et al. Superconductivity up to 164 K in HgBa₂Ca_m–1Cu_mO_2m+2+δ (m=1, 2, and 3) under quasihydrostatic pressures [J]. Physical Review B, 1994, 50(6): 4260–4263. doi: 10.1103/PhysRevB.50.4260
[195]	BEDNORZ J G, MÜLLER K A. Possible high T_c superconductivity in the Ba-La-Cu-O system [J]. Zeitschrift für Physik B Condensed Matter, 1986, 64(2): 189–193. doi: 10.1007/BF01303701
[196]	WU M K, ASHBURN J R, TORNG C J, et al. Superconductivity at 93 K in a new mixed-phase Y-Ba-Cu-O compound system at ambient pressure [J]. Physical Review Letters, 1987, 58(9): 908–910. doi: 10.1103/PhysRevLett.58.908
[197]	赵忠贤, 陈立泉, 杨乾声, 等. Ba-Y-Cu氧化物液氮温区的超导电性 [J]. 科学通报, 2017, 62(34): 3923–3924. ZHAO Z X, CHEN L Q, YANG Q S, et al . Superconductivity above liquid nitrogen temperature in Ba-Y-Cu oxides (original reprint) [J]. Chinese Science Bulletin, 2017, 62(34): 3923–3924.
[198]	REN Z A, LU W, YANG J, et al. Superconductivity at 55 K in iron-based F-doped layered quaternary compound Sm[O_1–xF_x] FeAs [J]. Chinese Physics Letters, 2008, 25(6): 2215–2216. doi: 10.1088/0256-307X/25/6/080
[199]	WU G, XIE Y L, CHEN H, et al. Superconductivity at 56 K in samarium-doped SrFeAsF [J]. Journal of Physics: Condensed Matter, 2009, 21(14): 142203. doi: 10.1088/0953-8984/21/14/142203
[200]	MA L, WANG K, XIE Y, et al. High-temperature superconducting phase in clathrate calcium hydride CaH₆ up to 215 K at a pressure of 172 GPa [J]. Physical Review Letters, 2022, 128(16): 167001. doi: 10.1103/PhysRevLett.128.167001
[201]	LI Z W, HE X, ZHANG C L, et al. Superconductivity above 200 K discovered in superhydrides of calcium [J]. Nature Communications, 2022, 13(1): 2863. doi: 10.1038/s41467-022-30454-w
[202]	XIE H, YAO Y S, FENG X L, et al. Hydrogen pentagraphenelike structure stabilized by hafnium: a high-temperature conventional superconductor [J]. Physical Review Letters, 2020, 125(21): 217001. doi: 10.1103/PhysRevLett.125.217001
[203]	ZHONG X, SUN Y, IITAKA T, et al. Prediction of above-room-temperature superconductivity in lanthanide/actinide extreme superhydrides [J]. Journal of the American Chemical Society, 2022, 144(29): 13394–13400. doi: 10.1021/jacs.2c05834
[204]	FENG X L, ZHANG J R, GAO G Y, et al. Compressed sodalite-like MgH₆ as a potential high-temperature superconductor [J]. RSC Advances, 2015, 5(73): 59292–59296. doi: 10.1039/C5RA11459D
[205]	SUN Y, ZHONG X, LIU H Y, et al. Clathrate metal superhydrides under high-pressure conditions: enroute to room-temperature superconductivity [J]. National Science Review, 2024, 11(7): nwad270. doi: 10.1093/nsr/nwad270
[206]	ZHOU D, SEMENOK D V, DUAN D F, et al. Superconducting praseodymium superhydrides [J]. Science Advances, 2020, 6(9): eaax6849. doi: 10.1126/sciadv.aax6849
[207]	CHEN W H, SEMENOK D V, HUANG X L, et al. High-temperature superconducting phases in cerium superhydride with a T_c up to 115 K below a pressure of 1 megabar [J]. Physical Review Letters, 2021, 127(11): 117001. doi: 10.1103/PhysRevLett.127.117001
[208]	MA L, ZHOU M, WANG Y Y, et al. Experimental clathrate superhydrides EuH₆ and EuH₉ at extreme pressure conditions [J]. Physical Review Research, 2021, 3(4): 043107. doi: 10.1103/PhysRevResearch.3.043107
[209]	TROYAN I A, SEMENOK D V, KVASHNIN A G, et al. Anomalous high-temperature superconductivity in YH₆ [J]. Advanced Materials, 2021, 33(15): 2006832. doi: 10.1002/adma.202006832
[210]	ZHOU D, SEMENOK D V, XIE H, et al. High-pressure synthesis of magnetic neodymium polyhydrides [J]. Journal of the American Chemical Society, 2020, 142(6): 2803–2811. doi: 10.1021/jacs.9b10439
[211]	SEMENOK D V, KVASHNIN A G, IVANOVA A G, et al. Superconductivity at 161 K in thorium hydride ThH₁₀: synthesis and properties [J]. Materials Today, 2020, 33: 36–44. doi: 10.1016/j.mattod.2019.10.005
[212]	KONG P P, MINKOV V S, KUZOVNIKOV M A, et al. Superconductivity up to 243 K in the yttrium-hydrogen system under high pressure [J]. Nature Communications, 2021, 12(1): 5075. doi: 10.1038/s41467-021-25372-2
[213]	SOMAYAZULU M, AHART M, MISHRA A K, et al. Evidence for Superconductivity above 260 K in lanthanum superhydride at megabar pressures [J]. Physical Review Letters, 2019, 122(2): 027001. doi: 10.1103/PhysRevLett.122.027001
[214]	DROZDOV A P, KONG P P, MINKOV V S, et al. Superconductivity at 250 K in lanthanum hydride under high pressures [J]. Nature, 2019, 569(7757): 528–531. doi: 10.1038/s41586-019-1201-8
[215]	SUN Y, LYU J, XIE Y, et al. Route to a superconducting phase above room temperature in electron-doped hydride compounds under high pressure [J]. Physical Review Letters, 2019, 123(9): 097001. doi: 10.1103/PhysRevLett.123.097001
[216]	SUN Y, WANG Y C, ZHONG X, et al. High-temperature superconducting ternary Li-R-H superhydrides at high pressures (R=Sc, Y, La) [J]. Physical Review B, 2022, 106(2): 024519. doi: 10.1103/PhysRevB.106.024519
[217]	DI CATALDO S, HEIL C, VON DER LINDEN W, et al. LaBH₈: towards high-T_c low-pressure superconductivity in ternary superhydrides [J]. Physical Review B, 2021, 104(2): l020511. doi: 10.1103/PhysRevB.104.L020511
[218]	ZHANG Z H, CUI T, HUTCHEON M J, et al. Design principles for high-temperature superconductors with a hydrogen-based alloy backbone at moderate pressure [J]. Physical Review Letters, 2022, 128(4): 047001. doi: 10.1103/PhysRevLett.128.047001
[219]	SUN Y, TIAN Y F, JIANG B W, et al. Computational discovery of a dynamically stable cubic SH₃-like high-temperature superconductor at 100 GPa via CH₄ intercalation [J]. Physical Review B, 2020, 101(17): 174102. doi: 10.1103/PhysRevB.101.174102
[220]	AN D C, DUAN D F, ZHANG Z H, et al. Thermodynamically stable room-temperature superconductors in Li-Na hydrides under high pressures [EB/OL]. ArXiv: 2303.09805. (2023-03-17) [2026-09-01]. https://arxiv.org/abs/2303.09805.
[221]	ZHANG P Y, SUN Y, LI X, et al. Structure and superconductivity in compressed Li-Si-H compounds: density functional theory calculations [J]. Physical Review B, 2020, 102(18): 184103. doi: 10.1103/PhysRevB.102.184103
[222]	SHAO Z J, DUAN D F, MA Y B, et al. Ternary superconducting cophosphorus hydrides stabilized via lithium [J]. NPJ Computational Materials, 2019, 5(1): 104. doi: 10.1038/s41524-019-0244-6
[223]	LI X, XIE Y, SUN Y, et al. Chemically tuning stability and superconductivity of P-H compounds [J]. The Journal of Physical Chemistry Letters, 2020, 11(3): 935–939. doi: 10.1021/acs.jpclett.9b03856
[224]	DI CATALDO S, VON DER LINDEN W, BOERI L. First-principles search of hot superconductivity in La-X-H ternary hydrides [J]. NPJ Computational Materials, 2022, 8(1): 2. doi: 10.1038/s41524-021-00691-6
[225]	LIANG X W, BERGARA A, WEI X D, et al. Prediction of high-T_c superconductivity in ternary lanthanum borohydrides [J]. Physical Review B, 2021, 104(13): 134501. doi: 10.1103/PhysRevB.104.134501
[226]	DU M Y, SONG H, ZHANG Z H, et al. Room-temperature superconductivity in Yb/Lu substituted clathrate hexahydrides under moderate pressure [J]. Research, 2022, 2022: 1–12. doi: 10.34133/2022/9784309
[227]	SUKMAS W, TSUPPAYAKORN-AEK P, PINSOOK U, et al. Near-room-temperature superconductivity of Mg/Ca substituted metal hexahydride under pressure [J]. Journal of Alloys and Compounds, 2020, 849: 156434. doi: 10.1016/j.jallcom.2020.156434
[228]	YANG K P, SUN H J, CHEN H L, et al. Stable structures and superconducting properties of Ca-La-H compounds under pressure [J]. Journal of Physics: Condensed Matter, 2022, 34(35): 355401. doi: 10.1088/1361-648X/ac79ed
[229]	LIU L L, PENG F, SONG P, et al. Generic rules for achieving room-temperature superconductivity in ternary hydrides with clathrate structures [J]. Physical Review B, 2023, 107(2): l020504. doi: 10.1103/PhysRevB.107.L020504
[230]	SONG P, HOU Z F, DE CASTRO P B, et al. The systematic study on the stability and superconductivity of Y-Mg-H compounds under high pressure [J]. Advanced Theory and Simulations, 2022, 5(3): 2100364. doi: 10.1002/adts.202100364
[231]	GE Y F, ZHANG F, YAO Y G. First-principles demonstration of superconductivity at 280 K in hydrogen sulfide with low phosphorus substitution [J]. Physical Review B, 2016, 93(22): 224513. doi: 10.1103/PhysRevB.93.224513
[232]	GENG N S, BI T G, ZUREK E. Structural diversity and superconductivity in S-P-H ternary hydrides under pressure [J]. The Journal of Physical Chemistry C, 2022, 126(16): 7208–7220. doi: 10.1021/acs.jpcc.1c10976
[233]	LIU A L, MA C H, WANG Z F, et al. A synergistic theoretical and experimental study on ternary La-B-H systems under pressure [J]. Journal of the American Chemical Society, 2026, 148(13): 13934–13939. doi: 10.1021/jacs.5c22431
[234]	SONG Y G, BI J K, NAKAMOTO Y, et al. Stoichiometric ternary superhydride LaBeH₈ as a new template for high-temperature superconductivity at 110 K under 80 GPa [J]. Physical Review Letters, 2023, 130(26): 266001. doi: 10.1103/PhysRevLett.130.266001
[235]	SEMENOK D V, TROYAN I A, IVANOVA A G, et al. Superconductivity at 253 K in lanthanum-yttrium ternary hydrides [J]. Materials Today, 2021, 48: 18–28. doi: 10.1016/j.mattod.2021.03.025
[236]	BI J, NAKAMOTO Y, ZHANG P, et al. Stabilization of superconductive La-Y alloy superhydride with T_c above 90 K at megabar pressure [J]. Materials Today Physics, 2022, 28: 100840. doi: 10.1016/j.mtphys.2022.100840
[237]	BI J K, NAKAMOTO Y, ZHANG P Y, et al. Giant enhancement of superconducting critical temperature in substitutional alloy (La, Ce)H₉ [J]. Nature Communications, 2022, 13(1): 5952. doi: 10.1038/s41467-022-33743-6
[238]	CHEN S, QIAN Y C, HUANG X L, et al. High-temperature superconductivity up to 223 K in the Al stabilized metastable hexagonal lanthanum superhydride [J]. National Science Review, 2023, 11(1): nwad107. doi: 10.1093/nsr/nwad107
[239]	TALANTSEV E F. Electron-phonon coupling constant and BCS ratios in LaH_10−y doped with magnetic rare-earth element [J]. Superconductor Science and Technology, 2022, 35(9): 095008. doi: 10.1088/1361-6668/ac7d78
[240]	HE X L, ZHAO W B, XIE Y, et al. Predicted hot superconductivity in LaSc₂H₂₄ under pressure [J]. Proceedings of the National Academy of Sciences of the United States of America, 2024, 121(26): e2401840121. doi: 10.1073/pnas.2401840121
[241]	SONG Y G, MA C H, WANG H B, et al. Room-temperature superconductivity at 298 K in ternary La-Sc-H system at high-pressure conditions [EB/OL]. ArXiv: 2510.01273. (2025-09-29) [2026-09-01]. https://arxiv.org/abs/2510.01273.
[242]	陈胤圻, 王洪波. 高压极端条件下的富氢高温超导体 [J]. 高压物理学报, 2024, 38(2): 020103. doi: 10.11858/gywlxb.20230842 CHEN Y Q, WANG H B. Hydrogen-rich superconductors with high critical temperature under high pressure [J]. Chinese Journal of High Pressure Physics, 2024, 38(2): 020103. doi: 10.11858/gywlxb.20230842
[243]	NAGAMATSU J, NAKAGAWA N, MURANAKA T, et al. Superconductivity at 39 K in magnesium diboride [J]. Nature, 2001, 410(6824): 63–64. doi: 10.1038/35065039
[244]	AN J M, PICKETT W E. Superconductivity of MgB₂: covalent bonds driven metallic [J]. Physical Review Letters, 2001, 86(19): 4366–4369. doi: 10.1103/PhysRevLett.86.4366
[245]	BOERI L, KORTUS J, ANDERSEN O K. Three-dimensional MgB₂-type superconductivity in hole-doped diamond [J]. Physical Review Letters, 2004, 93(23): 237002. doi: 10.1103/PhysRevLett.93.237002
[246]	贾欣芮, 刘爱玲, 钟鑫, 等. 高压下多元硼碳基高温超导体的研究进展 [J]. 高压物理学报, 2025, 39(9): 090201. doi: 10.11858/gywlxb.20251074 JIA X R, LIU A L, ZHONG X, et al. Research progress in multi-boron-carbon-based high-temperature superconductors under high pressures [J]. Chinese Journal of High Pressure Physics, 2025, 39(9): 090201. doi: 10.11858/gywlxb.20251074
[247]	WANG R H, SUN Y, ZHANG F, et al. High-throughput screening of strong electron-phonon couplings in ternary metal diborides [J]. Inorganic Chemistry, 2022, 61(45): 18154–18161. doi: 10.1021/acs.inorgchem.2c02829
[248]	MA L, YANG X, LIU G T, et al. Design and synthesis of clathrate LaB₈ with superconductivity [J]. Physical Review B, 2021, 104(17): 174112. doi: 10.1103/PhysRevB.104.174112
[249]	MA L, WANG L R, YUAN Y F, et al. High-temperature superconductivity in doped boron clathrates [J]. Chinese Physics Letters, 2023, 40(8): 086201. doi: 10.1088/0256-307X/40/8/086201
[250]	YANG X, ZHAO W B, MA L, et al. Prediction of fully metallic $ \sigma $-bonded boron framework induced high superconductivity above 100 K in thermodynamically stable Sr₂B₅ at 40 GPa [EB/OL]. ArXiv: 2310.13945. (2023-10-21) [2026-09-01]. https://arxiv.org/abs/2310.13945.
[251]	ZHU L, BORSTAD G M, LIU H Y, et al. Carbon-boron clathrates as a new class of sp³-bonded framework materials [J]. Science Advances, 2020, 6(2): eaay8361. doi: 10.1126/sciadv.aay8361
[252]	ZHU L, LIU H Y, SOMAYAZULU M, et al. Superconductivity in SrB₃C₃ clathrate [J]. Physical Review Research, 2023, 5(1): 013012. doi: 10.1103/PhysRevResearch.5.013012
[253]	WANG J N, YAN X W, GAO M. High-temperature superconductivity in SrB₃C₃ and BaB₃C₃ predicted from first-principles anisotropic Migdal-Eliashberg theory [J]. Physical Review B, 2021, 103(14): 144515. doi: 10.1103/PhysRevB.103.144515
[254]	GENG N S, HILLEKE K P, ZHU L, et al. Conventional high-temperature superconductivity in metallic, covalently bonded, binary-guest C-B clathrates [J]. Journal of the American Chemical Society, 2023, 145(3): 1696–1706. doi: 10.1021/jacs.2c10089
[255]	DI CATALDO S, QULAGHASI S, BACHELET G B, et al. High-T_c superconductivity in doped boron-carbon clathrates [J]. Physical Review B, 2022, 105(6): 064516. doi: 10.1103/PhysRevB.105.064516
[256]	DUAN Q Z, ZHAN L H, SHEN J Y, et al. Predicting superconductivity near 70 K in 1166-type boron-carbon clathrates at ambient pressure [J]. Physical Review B, 2024, 109(5): 054505. doi: 10.1103/PhysRevB.109.054505
[257]	GAI T T, GUO P J, YANG H C, et al. Van Hove singularity induced phonon-mediated superconductivity above 77 K in hole-doped SrB₃C₃ [J]. Physical Review B, 2022, 105(22): 224514. doi: 10.1103/PhysRevB.105.224514
[258]	ZHANG P Y, LI X, YANG X, et al. Path to high-T_c superconductivity via Rb substitution of guest metal atoms in the SrB₃C₃ clathrate [J]. Physical Review B, 2022, 105(9): 094503. doi: 10.1103/PhysRevB.105.094503
[259]	SUN Y, ZHU L. Hydride units filled boron-carbon clathrate: a pathway for high-temperature superconductivity at ambient pressure [J]. Communications Physics, 2024, 7(1): 324. doi: 10.1038/s42005-024-01814-3
[260]	SUN H L, HUO M W, HU X W, et al. Signatures of superconductivity near 80 K in a nickelate under high pressure [J]. Nature, 2023, 621(7979): 493–498. doi: 10.1038/s41586-023-06408-7
[261]	ZHANG Y N, SU D J, HUANG Y N, et al. High-temperature superconductivity with zero resistance and strange-metal behaviour in La₃Ni₂O_7−δ [J]. Nature Physics, 2024, 20(8): 1269–1273. doi: 10.1038/s41567-024-02515-y
[262]	MANDYAM S V, WANG E, WANG Z, et al. Uncovering origins of heterogeneous superconductivity in La₃Ni₂O₇ [J]. Nature, 2026, 651(8104): 54–60. doi: 10.1038/s41586-025-10095-x
[263]	LI F Y, XING Z F, PENG D, et al. Bulk superconductivity up to 96 K in pressurized nickelate single crystals [J]. Nature, 2026, 649(8098): 871–878. doi: 10.1038/s41586-025-09954-4
[264]	WANG Y X, JIANG K, YING J J, et al. Recent progress in nickelate superconductors [J]. National Science Review, 2025, 12(10): nwaf373. doi: 10.1093/nsr/nwaf373
[265]	DUBROVINSKAIA N, DUBROVINSKY L, SOLOPOVA N A, et al. Terapascal static pressure generation with ultrahigh yield strength nanodiamond [J]. Science Advances, 2016, 2(7): e1600341. doi: 10.1126/sciadv.1600341
[266]	DEWAELE A, LOUBEYRE P, OCCELLI F, et al. Toroidal diamond anvil cell for detailed measurements under extreme static pressures [J]. Nature Communications, 2018, 9(1): 2913. doi: 10.1038/s41467-018-05294-2
[267]	OHTANI E. Hydration and dehydration in Earth’s interior [J]. Annual Review of Earth and Planetary Sciences, 2021, 49: 253–278. doi: 10.1146/annurev-earth-080320-062509
[268]	HU Q Y, KIM D Y, YANG W G, et al. FeO₂ and FeOOH under deep lower-mantle conditions and Earth’s oxygen-hydrogen cycles [J]. Nature, 2016, 534(7606): 241–244. doi: 10.1038/nature18018
[269]	NISHI M, KUWAYAMA Y, TSUCHIYA J, et al. The pyrite-type high-pressure form of FeOOH [J]. Nature, 2017, 547(7662): 205–208. doi: 10.1038/nature22823
[270]	ZHANG L, YUAN H S, MENG Y, et al. Discovery of a hexagonal ultradense hydrous phase in (Fe, Al)OOH [J]. Proceedings of the National Academy of Sciences of the United States of America, 2018, 115(12): 2908–2911. doi: 10.1073/pnas.1720510115
[271]	HU Q Y, LIU J, CHEN J H, et al. Mineralogy of the deep lower mantle in the presence of H₂O [J]. National Science Review, 2021, 8(4): nwaa098. doi: 10.1093/nsr/nwaa098
[272]	胡清扬, 白承禾. 黄铁矿型(Fe, Mg)O₂H晶体到超离子态相变 [J]. 矿物岩石地球化学通报, 2024, 43(2): 319–325. doi: 10.19658/j.issn.1007-2802.2023.42.112 HU Q Y, BAI C H. The transition from crystal to superionic phases for the pyrite-type (Fe, Mg)O₂H [J]. Bulletin of Mineralogy, Petrology and Geochemistry, 2024, 43(2): 319–325. doi: 10.19658/j.issn.1007-2802.2023.42.112
[273]	SHAO S, LYU J, LIU H Y, et al. FeSiO₄H₂ stabilized at subducting slab conditions: a geologically viable water carrier into the Earth’s lower mantle [J]. Physical Review B, 2023, 108(21): 214107. doi: 10.1103/PhysRevB.108.214107
[274]	LI H F, OGANOV A R, CUI H X, et al. Ultrahigh-pressure magnesium hydrosilicates as reservoirs of water in early Earth [J]. Physical Review Letters, 2022, 128(3): 035703. doi: 10.1103/PhysRevLett.128.035703
[275]	WANG Z F, HE Y, MAO H K, et al. Hydrogen site-dependent physical properties of hydrous magnesium silicates in the mantle transition zone [J]. Nature Communications, 2026, 17(1): 5970. doi: 10.1038/s41467-026-72807-9
[276]	BAI C H, DU Z X, HU Q Y. Novel water-mineral chemistry and deep mantle heterogeneities [J]. Science China Earth Sciences, 2025: 1−9.
[277]	ZHANG J R, LYU J, LI H F, et al. Rare helium-bearing compound FeO₂He stabilized at deep-Earth conditions [J]. Physical Review Letters, 2018, 121(25): 255703. doi: 10.1103/PhysRevLett.121.255703
[278]	ZHANG J R, LIU H Y, MA Y M, et al. Direct H-He chemical association in superionic FeO₂H₂He at deep-Earth conditions [J]. National Science Review, 2022, 9(7): nwab168. doi: 10.1093/nsr/nwab168
[279]	ZHANG J R, LIU H Y, CHEN C F, et al. Sequestration of helium and xenon via iron-halide compounds in early Earth [J]. Matter and Radiation at Extremes, 2024, 9(3): 037403. doi: 10.1063/5.0164149
[280]	LIU C, GAO H, WANG Y, et al. Multiple superionic states in helium-water compounds [J]. Nature Physics, 2019, 15(10): 1065–1070. doi: 10.1038/s41567-019-0568-7
[281]	LIU C, GAO H, HERMANN A, et al. Plastic and superionic helium ammonia compounds under high pressure and high temperature [J]. Physical Review X, 2020, 10(2): 021007. doi: 10.1103/PhysRevX.10.021007
[282]	GAO H, LIU C, HERMANN A, et al. Coexistence of plastic and partially diffusive phases in a helium-methane compound [J]. National Science Review, 2020, 7(10): 1540–1547. doi: 10.1093/nsr/nwaa064
[283]	HUANG P H, LIU H Y, LYU J, et al. Stability of H₃O at extreme conditions and implications for the magnetic fields of Uranus and Neptune [J]. Proceedings of the National Academy of Sciences of the United States of America, 2020, 117(11): 5638–5643. doi: 10.1073/pnas.1921811117
[284]	PICKARD C J, MARTINEZ-CANALES M, NEEDS R J. Decomposition and terapascal phases of water ice [J]. Physical Review Letters, 2013, 110(24): 245701. doi: 10.1103/PhysRevLett.110.245701
[285]	LIU C, COHEN R E, SUN J. Prediction of thermally driven quasi-1D superionic states in carbon hydride under giant planetary conditions [J]. Nature Communications, 2026, 17(1): 3980. doi: 10.1038/s41467-026-70603-z
[286]	LU C, MIAO M S, MA Y M. Structural evolution of carbon dioxide under high pressure [J]. Journal of the American Chemical Society, 2013, 135(38): 14167–14171. doi: 10.1021/ja404854x
[287]	GAO H, LIU C, SHI J Y, et al. Superionic silica-water and silica-hydrogen compounds in the deep interiors of Uranus and Neptune [J]. Physical Review Letters, 2022, 128(3): 035702. doi: 10.1103/PhysRevLett.128.035702
[288]	CONWAY L J, PICKARD C J, HERMANN A. Rules of formation of H-C-N-O compounds at high pressure and the fates of planetary ices [J]. Proceedings of the National Academy of Sciences of the United States of America, 2021, 118(19): e2026360118. doi: 10.1073/pnas.2026360118
[289]	HELLED R, FORTNEY J J. The interiors of Uranus and Neptune: current understanding and open questions [J]. Philosophical Transactions of the Royal Society A: mathematical, Physical and Engineering Sciences, 2020, 378(2187): 20190474. doi: 10.1098/rsta.2019.0474
[290]	HELLED R, NETTELMANN N, GUILLOT T. Uranus and Neptune: origin, evolution and internal structure [J]. Space Science Reviews, 2020, 216(3): 38. doi: 10.1007/s11214-020-00660-3
[291]	TEANBY N A, IRWIN P G J, MOSES J I, et al. Neptune and Uranus: ice or rock giants? [J]. Philosophical Transactions of the Royal Society A: mathematical, Physical and Engineering Sciences, 2020, 378(2187): 20190489. doi: 10.1098/rsta.2019.0489
[292]	MIAO M S, SUN Y H, ZUREK E, et al. Chemistry under high pressure [J]. Nature Reviews Chemistry, 2020, 4(10): 508–527. doi: 10.1038/s41570-020-0213-0
[293]	贺芝宇, 黄秀光, 舒桦, 等. 冰巨行星内部深处物理与化学过程研究进展 [J]. 高压物理学报, 2023, 37(5): 050105. doi: 10.11858/gywlxb.20230721 HE Z Y, HUANG X G, SHU H, et al. Progress on physical and chemical processes deep inside ice giants [J]. Chinese Journal of High Pressure Physics, 2023, 37(5): 050105. doi: 10.11858/gywlxb.20230721
[294]	HERMANN A. Geoscience material structures prediction via CALYPSO methodology [J]. Chinese Physics B, 2019, 28(10): 106107. doi: 10.1088/1674-1056/ab43bc
[295]	NIU H Y, OGANOV A R, CHEN X Q, et al. Prediction of novel stable compounds in the Mg-Si-O system under exoplanet pressures [J]. Scientific Reports, 2015, 5(1): 18347. doi: 10.1038/srep18347
[296]	SCHUBERT G. Treatise on geophysics [M]. 2nd ed. Amsterdam: Elsevier, 2015: 149–178.
[297]	CONWAY L J, PICKARD C J, HERMANN A. First principles crystal structure prediction [M]//REEDIJK J, POEPPELMEIER K R. Comprehensive Inorganic Chemistry Ⅲ. 3rd ed. Amsterdam: Elsevier, 2023: 393−420.
[298]	DING Y J, SUN Y D, JIANG S Q, et al. Frontier in the diamond anvil cell techniques for ultrahigh pressure generation [J]. Journal of Physics: Condensed Matter, 2023, 35(31): 313002. doi: 10.1088/1361-648X/acd15e
[299]	苏磊, 杨国强. 动态压力加载/卸载装置dDAC及原位表征技术研究进展 [J]. 高压物理学报, 2021, 35(6): 060102. doi: 10.11858/gywlxb.20210505 SU L, YANG G Q. Research progress of dynamic pressure loading/unloading device and in-situ characterization technology [J]. Chinese Journal of High Pressure Physics, 2021, 35(6): 060102. doi: 10.11858/gywlxb.20210505
[300]	李晓东, 袁清习, 徐伟, 等. 第四代高能同步辐射光源HEPS及高压相关线站建设 [J]. 高压物理学报, 2020, 34(5): 050101. doi: 10.11858/gywlxb.20200554 LI X D, YUAN Q X, XU W, et al. Introduction of fourth-generation high energy photon source HEPS and the beamlines for high-pressure research [J]. Chinese Journal of High Pressure Physics, 2020, 34(5): 050101. doi: 10.11858/gywlxb.20200554
[301]	SHEN G Y, MAO H K. High-pressure studies with X-rays using diamond anvil cells [J]. Reports on Progress in Physics, 2017, 80(1): 016101. doi: 10.1088/1361-6633/80/1/016101
[302]	WEI B, LIN L, ZHANG J, et al. In situ measurement techniques using diamond anvil cell at high pressure-temperature conditions: a review [J]. Physica Status Solidi (RRL)-Rapid Research Letters, 2024, 18(8): 2300469. doi: 10.1002/pssr.202300469
[303]	WU J, BUSECK P R. In-situ high-pressure transmission electron microscopy for Earth and materials sciences [J]. American Mineralogist, 2014, 99(8/9): 1521–1527. doi: 10.2138/am.2014.4857
[304]	ABOLHASANI M, KUMACHEVA E. The rise of self-driving labs in chemical and materials sciences [J]. Nature Synthesis, 2023, 2(6): 483–492. doi: 10.1038/s44160-022-00231-0
[305]	SZYMANSKI N J, RENDY B, FEI Y X, et al. An autonomous laboratory for the accelerated synthesis of inorganic materials [J]. Nature, 2023, 624(7990): 86–91. doi: 10.1038/s41586-023-06734-w
[306]	KUSNE A G, MCDANNALD A. Managing autonomous materials labs with multi-agent AI and its implications for the science of science [J]. Communications Materials, 2026, 7(1): 173. doi: 10.1038/s43246-026-01219-5