Toward intelligent design of solid-state hydrogen storage: trends, challenges, and machine learning insights

Abstract

Solid-state hydrogen storage is widely recognized as a promising pathway for safe, high-density, and reversible hydrogen utilization, yet its advancement remains hampered by complex thermodynamic, kinetic, and structural constraints. This review highlights the emerging role of big data and machine learning in reshaping the research landscape. Through analyses enabled by the Digital Hydrogen-S platform, recent material development trends and persistent bottlenecks are systematically identified, revealing widespread misalignments with the US Department of Energy targets in storage capacity, operating temperature, and pressure. Data-driven approaches are shown to accelerate property prediction, high-throughput screening, and inverse design, while the integration with high-throughput computation and experimental validation is forming an intelligent closed-loop paradigm. Meanwhile, neural network potentials offer near-first-principles accuracy for probing hydrogen adsorption, dissociation, and diffusion, though challenges in long-range interactions and transferability remain. Looking ahead, establishing open-access multimodal databases (combining numbers, text, spectra, and images), developing multimodal large language models, implementing inverse design strategies, and constructing generalized neural network potentials capable of describing complete absorption-desorption cycles represent critical steps toward intelligent and practical material discovery. This review provides a structured framework to guide future research and accelerate the deployment of solid-state hydrogen storage technologies.

Graphical abstract

Keywords

solid-state hydrogen storage / data-driven materials design / machine learning neural network potentials

References

[1] Le P A , Trung V D , Nguyen P L , Phung T V B , Natsuki J , Natsuki T . The current status of hydrogen energy: an overview. RSC Advances, 2023, 13(40): 28262–28287

[2] Willich C . Hydrogen as an energy carrier—an overview over technology, status, and challenges in Germany. Multidisciplinary Scientific Journal, 2024, 7(4): 546–570

[3] Abdin Z , Al Khafaf N , McGrath B , Catchpole K , Gray E . A review of renewable hydrogen hybrid energy systems towards a sustainable energy value chain. Sustainable Energy & Fuels, 2023, 7(9): 2042–2062

[4] van der Spek M , Banet C , Bauer C , Gabrielli P , Goldthorpe W , Mazzotti M , Munkejord S T , Røkke N A , Shah N , Sunny N . et al. Perspective on the hydrogen economy as a pathway to reach net-zero CO₂ emissions in Europe. Energy & Environmental Science, 2022, 15(3): 1034–1077

[5] Rampai M M , Mtshali C B , Seroka N S , Khotseng L . Hydrogen production, storage, and transportation: recent advances. RSC Advances, 2024, 14(10): 6699–6718

[6] Mehrabianbardar A , Shirinbayan M , Jendli Z , Gillet S , Nouira S , Fitoussi J . A review: challenges, processes, and innovations in high-pressure hydrogen storage technologies. International Journal of Material Forming, 2025, 18(3): 77

[7] Zhang T , Uratani J , Huang Y , Xu L , Griffiths S , Ding Y . Hydrogen liquefaction and storage: recent progress and perspectives. Renewable & Sustainable Energy Reviews, 2023, 176: 113204

[8] Abdechafik E , Ait Ousaleh H , Mehmood S , Filali Baba Y , Bürger I , Linder M , Faik A . An analytical review of recent advancements on solid-state hydrogen storage. International Journal of Hydrogen Energy, 2024, 52: 1182–1193

[9] Chen G , Liang D , Kang Z , Fan J , Fan S , Zhou X . Review of hydrogen storage in solid-state materials. Energies, 2025, 18(11): 2930

[10] Xu Y , Zhou Y , Li Y , Ding Z . Research progress and application prospects of solid-state hydrogen storage technology. Molecules, 2024, 29(8): 1767

[11] Zheng H , Jia Y , Jin C , Che H , Lee C T , Liu G , Wang L , Zhao Y , He S , Liu H . et al. Experimental and theoretical study on high hydrogen storage performance of Mg(NH₂)₂-2LiH composite system driven by nano CeO₂ oxygen vacancies. Journal of Materials Science and Technology, 2025, 223: 173–185

[12] Muduli R C , Chen Z , Guo F , Jain A , Miyaoka H , Ichikawa T , Kale P . Enhancing the solid-state hydrogen storage properties of lithium hydride through thermodynamic tuning with porous silicon nanowires. Energy Advances, 2024, 3(9): 2212–2219

[13] Didi Y , Bahhar S , Tahiri A , Naji M , Rjeb A . A first-principles study of manganese-based perovskite-type hydrides for hydrogen storage application. Physica Status Solidi, 2024, 2400657

[14] Kim H , Kim H , Kim W , Kwon C , Jin S W , Ha T , Shim J H , Park S , Jamal A , Kim S . et al. Facile synthesis of nanoporous Mg crystalline structure by organic solvent-based reduction for solid-state hydrogen storage. Nature Communications, 2024, 15(1): 10800

[15] Ludwig A . Discovery of new materials using combinatorial synthesis and high-throughput characterization of thin-film materials libraries combined with computational methods. npj Computational Materials, 2019, 5(1): 70

[16] Zhu H , Zhou D , Chen D , Cheng H . Design of ultra-efficient and automatically temperature-variable cycle (TVC) Sieverts apparatus for testing sorption properties of hydrogen storage materials. International Journal of Hydrogen Energy, 2024, 62: 172–185

[17] Abdessameud S , Mezbahul-Islam M , Medraj M . Thermodynamic modeling of hydrogen storage capacity in Mg-Na alloys. The Scientific World Journal, 2014, 2014(1): 190320

[18] Palumbo M , Dematteis E M , Fenocchio L , Cacciamani G , Baricco M . Advances in CALPHAD methodology for modeling hydrides: a comprehensive review. Journal of Phase Equilibria and Diffusion, 2024, 45(3): 273–289

[19] Ruihan L , Feng H , Ting X , Yongzhi L , Xin Z , Jiaqi Z . Progress in the application of first principles to hydrogen storage materials. International Journal of Hydrogen Energy, 2024, 56: 1079–1091

[20] Mouvet F , Villard J , Bolnykh V , Rothlisberger U . Recent advances in first-principles based molecular dynamics. Accounts of Chemical Research, 2022, 55(3): 221–230

[21] Spataru C D , Heo T W , Wood B C , Stavila V , Kang S , Allendorf M D , Zhou X W . Statistically averaged molecular dynamics simulations of hydrogen diffusion in magnesium and magnesium hydrides. Physical Review Materials, 2020, 4(10): 105401

[22] Thanh H V , Dai Z X , Rahimi M . Data-driven explainable machine learning approaches for predicting hydrogen adsorption in porous crystalline materials. Journal of Alloys and Compounds, 2025, 1028: 180709

[23] Sun Y , DeJaco R F , Li Z , Tang D , Glante S , Sholl D S , Colina C M , Snurr R Q , Thommes M , Hartmann M . et al. Fingerprinting diverse nanoporous materials for optimal hydrogen storage conditions using meta-learning. Science Advances, 2021, 7(30): eabg3983

[24] Verma A , Wilson N , Joshi K . Solid state hydrogen storage: decoding the path through machine learning. International Journal of Hydrogen Energy, 2024, 50: 1518–1528

[25] Bhattacharjee S , Das P , Ram S , Lee S C . A hybrid machine learning framework for predicting hydrogen storage capacities in metal hydrides: unsupervised feature learning with deep neural networks. ACS Applied Materials & Interfaces, 2025, 17(20): 29681–29694

[26] Dangwal S , Ikeda Y , Grabowski B , Edalati K . Machine learning to explore high-entropy alloys with desired enthalpy for room-temperature hydrogen storage: prediction of density functional theory and experimental data. Chemical Engineering Journal, 2024, 493: 152606

[27] Kim W T , Lee W G , An H E , Furukawa H , Jeong W , Kim S C , Long J R , Jeong S , Lee J H . Machine learning-assisted design of metal-organic frameworks for hydrogen storage: a high-throughput screening and experimental approach. Chemical Engineering Journal, 2025, 507: 160766

[28] Liu Y , Yang Z , Zou X , Ma S , Liu D , Avdeev M , Shi S . Data quantity governance for machine learning in materials science. National Science Review, 2023, 10(7): nwad125

[29] Ding C , Pereira T , Xiao R , Lee R J , Hu X . Impact of label noise on the learning based models for a binary classification of physiological signal. Sensors, 2022, 22(19): 7166

[30] Sandrock G , Thomas G . The IEA/DOE/SNL on-line hydride databases. Applied Physics A: Materials Science & Processing, 2001, 72(2): 153–155

[31] Jain A , Ong S P , Hautier G , Chen W , Richards W D , Dacek S , Cholia S , Gunter D , Skinner D , Ceder G . et al. Commentary: the materials project: a materials genome approach to accelerating materials innovation. APL Materials, 2013, 1(1): 011002

[32] Dalian Institute of Chemical Physics CAo S . CHydrogen Storage Materials Database. , ,

[33] Matsuda A , Demura M , Yamazaki M , Kadohira T , Ashino T , Furuya Y , Sawada K , Nishikawa N . A unified model for metallic materials reliability data and its application in NIMS metallic materials database (Kinzoku). Science and Technology of Advanced Materials: Methods, 2025, 5(1): 2518745

[34] Huang P , Cai D , Lin H , Liu J , Li Z , Li B , Zou Y , Chu H , Sun L , Xu F . Materials genome engineering-based hydrogen storage materialsdatabase and its applications. Scientia Sinica Chimica, 2022, 52(10): 1863–1870

[35] Lu Z , Wang J , Wu Y , Guo X , Xiao W . Predicting hydrogen storage capacity of V-Ti-Cr-Fe alloy via ensemble machine learning. International Journal of Hydrogen Energy, 2022, 47(81): 34583–34593

[36] Dong S , Wang Y , Li J , Li Y , Wang L , Zhang J . Exploration and design of Mg alloys for hydrogen storage with supervised machine learning. International Journal of Hydrogen Energy, 2023, 48(97): 38412–38424

[37] Zhou P , Xiao X , Zhu X , Chen Y , Lu W , Piao M , Cao Z , Lu M , Fang F , Li Z . et al. Machine learning enabled customization of performance-oriented hydrogen storage materials for fuel cell systems. Energy Storage Materials, 2023, 63: 102964

[38] Kim J , Ha T , Lee J , Lee Y , Shim J . Prediction of pressure-composition-temperature curves of AB₂-type hydrogen storage alloys by machine learning. Metals and Materials International, 2023, 29(3): 861–869

[39] North China Electric Power University . Digital Hydrogen-S: Digital Platform for Solid-State Hydrogen Storage, 2025. , ,

[40] Wang L , Liu W , Sun H , Yang L , Huang L . Advancements and policy implications of green hydrogen production from renewable sources. Energies, 2024, 17(14): 3548

[41] Sadeq A M , Homod R Z , Hussein A K , Togun H , Mahmoodi A , Isleem H F , Patil A R , Moghaddam A H . Hydrogen energy systems: technologies, trends, and future prospects. Science of the Total Environment, 2024, 939: 173622

[42] Marques F , Balcerzak M , Winkelmann F , Zepon G , Felderhoff M . Review and outlook on high-entropy alloys for hydrogen storage. Energy & Environmental Science, 2021, 14(10): 5191–5227

[43] U.S. Department of Energy . DOE Technical Targets for Onboard Hydrogen Storage for Light-Duty Vehicles: U.S. Department of Energy, 2025. , ,

[44] Lu L , Shiwen L , Jiongyang C , Minjie Y , Tongao X , Zhuoran Y , Yujie L , Jun S , Xuqiang G . et al. FIND: a forward–inverse navigation and discovery platform for hydrogen storage alloys powered by data-driven machine learning. Journal of Surveillance. Security and Safety, 2025, 5(4): 48

[45] Jordan M I , Mitchell T M . Machine learning: trends, perspectives, and prospects. Science, 2015, 349(6245): 255–260

[46] Noé F , Tkatchenko A , Müller K R , Clementi C . Machine learning for molecular simulation. Annual Review of Physical Chemistry, 2020, 71(1): 361–390

[47] Rahnama A , Zepon G , Sridhar S . Machine learning based prediction of metal hydrides for hydrogen storage, part I: Prediction of hydrogen weight percent. International Journal of Hydrogen Energy, 2019, 44(14): 7337–7344

[48] Somo T R , Modibane K D , Maponya T C , Teffu D M . Theoretical hydrogen storage properties of high entropy alloys: a combined DFT and machine learning approach. Materials Today Communications, 2025, 48: 113366

[49] Zhou P , Zhou Q , Xiao X , Fan X , Zou Y , Sun L , Jiang J , Song D , Chen L . Machine learning in solid-state hydrogen storage materials: challenges and perspectives. Advanced Materials, 2025, 37(6): 2413430

[50] Salehi K , Rahmani M , Atashrouz S . Machine learning assisted predictions for hydrogen storage in metal-organic frameworks. International Journal of Hydrogen Energy, 2023, 48(85): 33260–33275

[51] Nations S , Nandi T , Ramazani A , Wang S , Duan Y . Metal hydride composition-derived parameters as machine learning features for material design and H₂ storage. Journal of Energy Storage, 2023, 70: 107980

[52] Gheytanzadeh M , Rajabhasani F , Baghban A , Habibzadeh S , Abida O , Esmaeili A , Munir M T . Estimating hydrogen absorption energy on different metal hydrides using Gaussian process regression approach. Scientific Reports, 2022, 12(1): 21902

[53] Jiang Q , Jia Z , Lu S , Song P , Gao Z , Wang Z , Peng T , Bai X , Cui H , Tian W . et al. Novel NLi₄-BGra/MgH₂-based heterojunctions for efficient hydrogen storage and modulation of hydrogen-desorption temperature ranges. Ceramics International, 2024, 50(13): 23058–23069

[54] Shekhar S , Chowdhury C . Prediction of hydrogen storage in metal-organic frameworks: a neural network based approach. Results in Surfaces and Interfaces, 2024, 14: 100166

[55] Borja N K , Fabros C J E , Doma B T Jr . Prediction of hydrogen adsorption and moduli of metal-organic frameworks (MOFs) using machine learning strategies. Energies, 2024, 17(4): 927

[56] Ding Z , Chen Z , Ma T , Lu C , Ma W , Shaw L . Predicting the hydrogen release ability of LiBH₄-based mixtures by ensemble machine learning. Energy Storage Materials, 2020, 27: 466–477

[57] Tian Q , Zhang Y , Wu Y , Tan Z . The cycle life prediction of Mg-based hydrogen storage alloys by artificial neural network. International Journal of Hydrogen Energy, 2009, 34(4): 1931–1936

[58] Wang G , Luo Z , Desta H G , Chen M , Dong Y , Lin B . AI-driven development of high-performance solid-state hydrogen storage. Energy Reviews, 2025, 4(1): 100106

[59] Altintas C , Keskin S . On the shoulders of high-throughput computational screening and machine learning: design and discovery of MOFs for H₂ storage and purification. Materials Today Energy, 2023, 38: 101426

[60] Giappa R M , Tylianakis E , Di Gennaro M , Gkagkas K , Froudakis G E . A combination of multi-scale calculations with machine learning for investigating hydrogen storage in metal organic frameworks. International Journal of Hydrogen Energy, 2021, 46(54): 27612–27621

[61] Cao Z , Liu Y , Wang Z , Yang Q , Zheng B . Prediction of hydrogen storage in IL/COF composites based on high-throughput computational screening and machine learning. International Journal of Hydrogen Energy, 2025, 135: 525–536

[62] Chen Y , Zhao G , Yoon S , Habibi P , Hong C S , Li S , Moultos O A , Dey P , Vlugt T J H , Chung Y G . Computational exploration of adsorption-based hydrogen storage in Mg-alkoxide functionalized covalent-organic frameworks (COFs): force-field and machine learning models. ACS Applied Materials & Interfaces, 2024, 16(45): 61995–62009

[63] Ward L , Agrawal A , Choudhary A , Wolverton C . A general-purpose machine learning framework for predicting properties of inorganic materials. npj Computational Materials, 2016, 2(1): 16028

[64] Witman M D , Ling S , Wadge M , Bouzidi A , Pineda-Romero N , Clulow R , Ek G , Chames J M , Allendorf E J , Agarwal S . et al. Towards Pareto optimal high entropy hydrides via data-driven materials discovery. Journal of Materials Chemistry A: Materials for Energy and Sustainability, 2023, 11(29): 15878–15888

[65] Hattrick-Simpers J R , Choudhary K , Corgnale C . A simple constrained machine learning model for predicting high-pressure-hydrogen-compressor materials. Molecular Systems Design & Engineering, 2018, 3(3): 509–517

[66] Lee J , Sung D , Chung Y K , Bin Song S , Huh J . Unveiling two-dimensional magnesium hydride as a hydrogen storage material via a generative adversarial network. Nanoscale Advances, 2022, 4(10): 2332–2338

[67] Jia Y , Fang H , Chen L , Han B , Tang L , Wang J , Xia Y , Zou Y , Sun L , Li H . et al. Interpretable machine learning enables high performance of magnesium borohydride hydrogen storage system. Journal of Magnesium and Alloys, 2025, 13(9): 4430–4445

[68] Yao T , Yang Y , Cai J , Liu R , Dong Z , Tang X , Shao X , Gao Z , An G , Yang W . From LLM to agent: a large-language-model-driven machine learning framework for catalyst design of MgH₂ dehydrogenation. Journal of Magnesium and Alloys, 2025,

[69] Zhang D , Jia X , Hung T B , Jang S H , Zhang L , Sato R , Hashimoto Y , Sato T , Konno K , Orimo S-i . et al. “DIVE” into Hydrogen Storage Materials Discovery with AI Agents. arXiv, 2025,

[70] Witman M , Ek G , Ling S , Chames J , Agarwal S , Wong J , Allendorf M D , Sahlberg M , Stavila V . Data-driven discovery and synthesis of high entropy alloy hydrides with targeted thermodynamic stability. Chemistry of Materials, 2021, 33(11): 4067–4076

[71] Butler K T , Davies D W , Cartwright H , Isayev O , Walsh A . Machine learning for molecular and materials science. Nature, 2018, 559(7715): 547–555

[72] Witman M , Allendorf M , Stavila V . Database for machine learning of hydrogen storage materials properties. , 2024,

[73] Wilson N , Verma A , Maharana P R , Sahoo A B , Joshi K . HyStor: an experimental database of hydrogen storage properties for various metal alloy classes. International Journal of Hydrogen Energy, 2024, 90: 460–469

[74] Batalovic K , Radakovic J , Mamula B P , Kuzmanovic B , Ilic M M . Predicting the heat of hydride formation by graph neural network-exploring the structure-property relation for metal hydrides. Advanced Theory and Simulations, 2022, 5(9): 2200293

[75] Velliangiri S , Alagumuthukrishnan S , Thankumar joseph S I . A review of dimensionality reduction techniques for efficient computation. Procedia Computer Science, 2019, 165: 104–111

[76] Chen D , Sun D , Fu J , Liu S . Semi-supervised learning framework for aluminum alloy metallographic image segmentation. IEEE Access: Practical Innovations, Open Solutions, 2021, 9: 30858–30867

[77] Xu Q , Shao Z , Yao T , Yan Y , Li M , Gao Z , Yang W . FAIR-Platform: Feature-space Analysis and Insight for Reliability in Machine Learning. , 2025,

[78] Radhika N , Sabarinathan M , Ragunath S , Adediran A A , Jen T C . Machine learning based prediction of Young’s modulus of stainless steel coated with high entropy alloys. Results in Materials, 2024, 23: 100607

[79] Yang X , Yuan C , He S , Jiang D , Cao B , Wang S . Machine learning prediction of specific capacitance in biomass derived carbon materials: effects of activation and biochar characteristics. Fuel, 2023, 331: 125718

[80] Gao J , Guo X , Wu Y , Xiao W , Hao L . The hydrogen absorption process prediction of AB₂ hydrogen storage device based on data-driven approach. International Journal of Hydrogen Energy, 2024, 58: 657–667

[81] Kulichenko M , Nebgen B , Lubbers N , Smith J S , Barros K , Allen A E A , Habib A , Shinkle E , Fedik N , Li Y W . et al. Data generation for machine learning interatomic potentials and beyond. Chemical Reviews, 2024, 124(24): 13681–13714

[82] Iftimie R , Minary P , Tuckerman M E . Ab-initio molecular dynamics: concepts, recent developments, and future trends. Proceedings of the National Academy of Sciences of the United States of America, 2005, 102(19): 6654–6659

[83] Duignan T T . The potential of neural network potentials. ACS Physical Chemistry Au, 2024, 4(3): 232–241

[84] Zhang L , Han J , Wang H , Car R E W . Deep potential molecular dynamics: a scalable model with the accuracy of quantum mechanics. Physical Review Letters, 2018, 120(14): 143001

[85] Behler J . Perspective: machine learning potentials for atomistic simulations. Journal of Chemical Physics, 2016, 145(17): 170301

[86] Behler J . Atom-centered symmetry functions for constructing high-dimensional neural network potentials. Journal of Chemical Physics, 2011, 134(7): 074106

[87] Kocer E , Ko T W , Behler J . Neural network potentials: a concise overview of methods. Annual Review of Physical Chemistry, 2022, 73(1): 163–186

[88] Behler J . Four generations of high-dimensional neural network potentials. Chemical Reviews, 2021, 121(16): 10037–10072

[89] Ko T W , Finkler J A , Goedecker S , Behler J . A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer. Nature Communications, 2021, 12(1): 398

[90] Ludwig J , Vlachos D G . Ab initio molecular dynamics of hydrogen dissociation on metal surfaces using neural networks and novelty sampling. Journal of Chemical Physics, 2007, 127(15): 154716

[91] Zhu L , Zhang Y , Zhang L , Zhou X , Jiang B . Unified and transferable description of dynamics of H₂ dissociative adsorption on multiple copper surfaces via machine learning. Physical Chemistry Chemical Physics, 2020, 22(25): 13958–13964

[92] Stark W G , Westermayr J , Douglas-Gallardo O A , Gardner J , Habershon S , Maurer R J . Machine learning interatomic potentials for reactive hydrogen dynamics at metal surfaces based on iterative refinement of reaction probabilities. Journal of Physical Chemistry C, 2023, 127(50): 24168–24182

[93] Wille S , Jiang H , Bünermann O , Wodtke A M , Behler J , Kandratsenka A . An experimentally validated neural-network potential energy surface for H-atom on free-standing graphene in full dimensionality. Physical Chemistry Chemical Physics, 2020, 22(45): 26113–26120

[94] Kataoka Y , Haruyama J , Sugino O , Shiga M . Predictive evaluation of hydrogen diffusion coefficient on Pd(111) surface by path integral simulations using neural network potential. Physical Review Research, 2024, 6(4): 043224

[95] Morrison O , Uteva E , Walker G S , Grant D M , Ling S . Long time scale molecular dynamics simulation of magnesium hydride dehydrogenation enabled by machine learning interatomic potentials. ACS Applied Energy Materials, 2025, 8(1): 492–502

[96] Ito K , Matsumura N , Iwasaki Y , Sakai Y , Yamamura M , Omura T , Yamabe J , Matsunaga H . Predicting hydrogen diffusion in nickel-manganese random alloys using machine learning interatomic potentials. Communications Materials, 2025, 6(1): 195

[97] Wang N , Huang S . Molecular dynamics study on magnesium hydride nanoclusters with machine-learning interatomic potential. Physical Review B, 2020, 102(9): 094111

[98] Liu S , Dupuis R , Fan D , Benzaria S , Bonneau M , Bhatt P , Eddaoudi M , Maurin G . Machine learning potential for modelling H₂ adsorption/diffusion in MOFs with open metal sites. Chemical Science, 2024, 15(14): 5294–5302

[99] Anstine D M , Isayev O . Machine learning interatomic potentials and long-range physics. Journal of Physical Chemistry A, 2023, 127(11): 2417–2431

[100] Ibragimova R , Kuklin M S , Zarrouk T , Caro M A . Unifying the description of hydrocarbons and hydrogenated carbon materials with a chemically reactive machine learning interatomic potential. Chemistry of Materials, 2025, 37(3): 1094–1110

[101] Jinnouchi R , Miwa K , Karsai F , Kresse G , Asahi R . On-the-fly active learning of interatomic potentials for large-scale atomistic simulations. Journal of Physical Chemistry Letters, 2020, 11(17): 6946–6955

[102] Zhang Y , Wang H , Chen W , Zeng J , Zhang L , Wang H , Wei E . DP-GEN: a concurrent learning platform for the generation of reliable deep learning based potential energy models. Computer Physics Communications, 2020, 253: 107206

[103] Morrow J D , Gardner J L A , Deringer V L . How to validate machine-learned interatomic potentials. Journal of Chemical Physics, 2023, 158(12): 121501

[104] Batzner S , Musaelian A , Sun L , Geiger M , Mailoa J P , Kornbluth M , Molinari N , Smidt T E , Kozinsky B . E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. Nature Communications, 2022, 13(1): 2453

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