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Elevating universal interatomic potentials with large-scale off-equilibrium data

delete2026-06-02
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PRE
AI
J
Jiayu Peng *
DOI：10.1038/s43588-026-00994-ydelete
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Abstract

Abstract

En 中文
The Open Materials 2024 dataset provides a large-scale, open-access collection of quantum-chemical atomistic simulations that encompasses diverse off-equilibrium crystal structures, thereby making machine-learning interatomic potentials more robust across different materials prediction tasks.

Journal

Nature Computational Science cover
Nature Computational Science
IF:
18.3
Papers:
3.1K
Citations:
4.0K

Organization

U
University at Buffalo
Scholars:
1.2K
Papers: 629
Citations: 1
Cited Papers

Cited Papers

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CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling
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Maximizing efficiency of dataset compression for machine learning potentials with information theory
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IF0
err2026-04-28
err0
PREAI
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Scaling deep learning for materials discovery
errNATURE
IF48.5
err2023-11-29
err210
errOAAI
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The Open Materials 2024 (OMat24) inorganic materials dataset and models
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IF18.3
err2026-06-02
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PREAI
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errNature Reviews Chemistry
IF51.7
err2026-02-11
err0
PREAI
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Improving machine-learning models in materials science through large datasets
errMATERIALS TODAY PHYSICS
IF9.7
err2024-11-01
err2
errOAAI
errSchmidt, Jonathan; Cerqueira, Tiago F. T.; Romero, Aldo H.; Loew, Antoine; Jager, Fabian; Wang, Hai-Chen; Botti, Silvana; Marques, Miguel A. L.
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Systematic softening in universal machine learning interatomic potentials
errNPJ COMPUTATIONAL MATERIALS
IF11.9
err2025-01-10
err9
errOAAI
errDeng, Bowen; Riebesell, Janosh; Anand, Shashwat; Jun, Kyujung
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A universal graph deep learning interatomic potential for the periodic table
errNATURE COMPUTATIONAL SCIENCE
IF18.3
err2022-11-28
err268
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A foundation model for atomistic materials chemistry
errThe Journal of Chemical Physics
IF0
err2025-11-14
err0
PREAI
errBatatia,Ilyes; Benner,Philipp; Chiang,Yuan; Elena,Alin M.; Kovács,Dávid P.; Riebesell,Janosh; Advincula,Xavier R.; Asta,Mark; Avaylon,Matthew; Baldwin,William J.; Berger,Fabian; Bernstein,Noam; Bhowmik,Arghya; Bigi,Filippo; Blau,Samuel M.; Cărare,Vlad; Ceriotti,Michele; Chong,Sanggyu; Darby,James P.; De,Sandip; Della Pia,Flaviano; Deringer,Volker L.; Elijošius,Rokas; El-Machachi,Zakariya; Fako,Edvin; Falcioni,Fabio; Ferrari,Andrea C.; Gardner,John L. A.; Gawkowski,Mikołaj J.; Genreith-Schriever,Annalena; George,Janine; Goodall,Rhys E. A.; Grandel,Jonas; Grey,Clare P.; Grigorev,Petr; Han,Shuang; Handley,Will; Heenen,Hendrik H.; Hermansson,Kersti; Ho,Cheuk Hin; Hofmann,Stephan; Holm,Christian; Jaafar,Jad; Jakob,Konstantin S.; Jung,Hyunwook; Kapil,Venkat; Kaplan,Aaron D.; Karimitari,Nima; Kermode,James R.; Kourtis,Panagiotis; Kroupa,Namu; Kullgren,Jolla; Kuner,Matthew C.; Kuryla,Domantas; Liepuoniute,Guoda; Lin,Chen; Margraf,Johannes T.; Magdău,Ioan-Bogdan; Michaelides,Angelos; Moore,J. Harry; Naik,Aakash A.; Niblett,Samuel P.; Norwood,Sam Walton; O’Neill,Niamh; Ortner,Christoph; Persson,Kristin A.; Reuter,Karsten; Rosen,Andrew S.; Rosset,Louise A. M.; Schaaf,Lars L.; Schran,Christoph; Shi,Benjamin X.; Sivonxay,Eric; Stenczel,Tamás K.; Sutton,Christopher; Svahn,Viktor; Swinburne,Thomas D.; Tilly,Jules; van der Oord,Cas; Vargas,Santiago; Varga-Umbrich,Eszter; Vegge,Tejs; Vondrák,Martin; Wang,Yangshuai; Witt,William C.; Wolf,Thomas; Zills,Fabian; Csányi,Gábor
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