Pith Number

pith:O2JXGK7I

pith:2024:O2JXGK7ICTEJQC7QRW4W2LWN4Q

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MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures

Andrew Fowler, Chang Liu, Chenxi Hu, Claudio Zeni, Daniel Z\"ugner, Guanzhi Li, Han Yang, Hongxia Hao, Jake Smith, Jielan Li, Lingyu Kong, Lixin Sun, Matthew Horton, Qian Wang, Robert Pinsler, Shuizhou Chen, Tian Xie, Xixian Liu, Yichi Zhou, Yu Shi, Zekun Chen, Ziheng Lu

MatterSim predicts Gibbs free energies of inorganic solids at near first-principles accuracy across wide temperatures and pressures.

arxiv:2405.04967 v2 · 2024-05-08 · cond-mat.mtrl-sci

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Claims

C1strongest claim

MatterSim predicts Gibbs free energies for a wide range of inorganic solids with near-first-principles accuracy and achieves a 15 meV/atom resolution for temperatures up to 1000 K compared with experiments.

C2weakest assumption

The training data from first-principles computations sufficiently covers the relevant chemical space, temperatures, and pressures so that the model generalizes accurately to unseen compositions and conditions without large extrapolation errors.

C3one line summary

MatterSim delivers a single deep learning force field that simulates inorganic materials across elements, 0-5000 K, and up to 1000 GPa with near first-principles accuracy for lattice dynamics, mechanics, and Gibbs free energies.

References

158 extracted · 158 resolved · 2 Pith anchors

[1] G. Fiori, F. Bonaccorso, G. Iannaccone, T. Palacios, D. Neumaier, A. Seabaugh, S.K. Banerjee, L. Colombo, Electronics based on two-dimensional materials. Nature nanotechnology 9(10), 768–779 (2014) 2014

[2] T. Li, G. Galli, Electronic properties of mos2 nanoparticles. The Journal of Physical Chemistry C 111(44), 16192–16196 (2007) 2007

[3] K. Mizushima, P. Jones, P. Wiseman, J.B. Goodenough, Lixcoo2 (0¡ x¡-1): A new cathode material for batteries of high energy density. Materials Research Bulletin 15(6), 783–789 (1980) 1980

[4] G. Ceder, Y.M. Chiang, D. Sadoway, M. Aydinol, Y.I. Jang, B. Huang, Identification of cathode materials for lithium batteries guided by first-principles calculations. Nature 392(6677), 694– 696 (1998) 1998

[5] M.W. Tibbitt, C.B. Rodell, J.A. Burdick, K.S. Anseth, Progress in material design for biomed- ical applications. Proceedings of the National Academy of Sciences 112(47), 14444–14451 (2015) 2015

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Cited by

28 papers in Pith

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JanusPipe: Efficient Pipeline Parallel Training for Machine Learning Interatomic Potentials

Receipt and verification

First computed	2026-05-17T23:38:46.142985Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

7693732be814c8980bf08db96d2ecde4084e9dde051e715a719748569ac5be28

Aliases

arxiv: 2405.04967 · arxiv_version: 2405.04967v2 · doi: 10.48550/arxiv.2405.04967 · pith_short_12: O2JXGK7ICTEJ · pith_short_16: O2JXGK7ICTEJQC7Q · pith_short_8: O2JXGK7I

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curl -sH 'Accept: application/ld+json' https://pith.science/pith/O2JXGK7ICTEJQC7QRW4W2LWN4Q \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 7693732be814c8980bf08db96d2ecde4084e9dde051e715a719748569ac5be28

Canonical record JSON

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