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Semantic Scholar Open Access 2023 470 sitasi

A foundation model for atomistic materials chemistry.

Ilyes Batatia Philipp Benner Chiang Yuan A. Elena D. Kov'acs +63 lainnya
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Abstrak

Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much of our understanding of chemistry and materials science. Over the last decade or so, machine-learned force fields have transformed atomistic modeling by enabling simulations of ab initio quality over unprecedented time and length scales. However, early machine-learning (ML) force fields have largely been limited by (i) the substantial computational and human effort required to develop and validate potentials for each particular system of interest and (ii) a general lack of transferability from one chemical system to the next. Here, we show that it is possible to create a general-purpose atomistic ML model, trained on a public dataset of moderate size, that is capable of running stable molecular dynamics for a wide range of molecules and materials. We demonstrate the power of the MACE-MP-0 model-and its qualitative and at times quantitative accuracy-on a diverse set of problems in the physical sciences, including properties of solids, liquids, gases, chemical reactions, interfaces, and even the dynamics of a small protein. The model can be applied out of the box as a starting or "foundation" model for any atomistic system of interest and, when desired, can be fine-tuned on just a handful of application-specific data points to reach ab initio accuracy. Establishing that a stable force-field model can cover almost all materials changes atomistic modeling in a fundamental way: experienced users obtain reliable results much faster, and beginners face a lower barrier to entry. Foundation models thus represent a step toward democratizing the revolution in atomic-scale modeling that has been brought about by ML force fields.

Topik & Kata Kunci

Physics Medicine

Penulis (68)

I

Ilyes Batatia

P

Philipp Benner

C

Chiang Yuan

A

A. Elena

D

D. Kov'acs

J

Janosh Riebesell

X

Xavier R Advincula

M

M. Asta

W

William J. Baldwin

N

Noam Bernstein

A

Arghya Bhowmik

S

Samuel M. Blau

V

Vlad Cuarare

J

James P Darby

S

Sandip De

F

Flaviano Della Pia

V

Volker L. Deringer

R

Rokas Elijovsius

Z

Zakariya El-Machachi

E

Edvin Fako

A

Andrea C. Ferrari

A

A. Genreith‐Schriever

J

Janine George

R

Rhys E. A. Goodall

C

Clare P. Grey

S

Shuang Han

W

Will Handley

H

H. H. Heenen

K

K. Hermansson

C

Christian Holm

J

Jad Jaafar

S

Stephan Hofmann

K

Konstantin S. Jakob

H

H. Jung

V

V. Kapil

A

Aaron D. Kaplan

N

Nima Karimitari

N

Namu Kroupa

J

J. Kullgren

M

Matthew C Kuner

D

Domantas Kuryla

G

Guoda Liepuoniute

J

Johannes T. Margraf

I

Ioan B Magduau

A

A. Michaelides

J

J. Moore

A

A. Naik

S

Samuel P Niblett

S

Sam Walton Norwood

N

N. O'Neill

C

Christoph Ortner

K

Kristin A. Persson

K

K. Reuter

A

Andrew S. Rosen

L

L. Schaaf

C

Christoph Schran

E

E. Sivonxay

T

T. Stenczel

V

V. Svahn

C

Christopher Sutton

C

C. V. D. Oord

E

E. Varga-Umbrich

T

T. Vegge

M

Martin Vondr'ak

Y

Yangshuai Wang

W

William C Witt

F

F. Zills

G

G'abor Cs'anyi

Format Sitasi

APA MLA BibTeX
Batatia, I., Benner, P., Yuan, C., Elena, A., Kov'acs, D., Riebesell, J. et al. (2023). A foundation model for atomistic materials chemistry.. https://doi.org/10.1063/5.0297006

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Lihat di Sumber doi.org/10.1063/5.0297006
Informasi Jurnal
Tahun Terbit
2023
Bahasa
en
Total Sitasi
470×
Sumber Database
Semantic Scholar
DOI
10.1063/5.0297006
Akses
Open Access ✓