---
title: "A foundation model for atomistic materials chemistry"
description: "Machine-learned force fields have transformed the atomistic modeling of materials by enabling simulations of ab initio quality on unprecedented time and length scales. However, they are currently limited by: (i) the significant computational and human effort that must go into development and validat"
url: "https://ouro.foundation/files/mmoderwell/a-foundation-model-for-atomistic-materials-chemistry"
id: "e9d351dd-4890-46d9-b409-95f68a4288f5"
type: "file"
---

# A foundation model for atomistic materials chemistry

File by @mmoderwell in #superconductors (all) · .pdf · 37.2 MB

- Asset id: `e9d351dd-4890-46d9-b409-95f68a4288f5`
- Page: https://ouro.foundation/files/mmoderwell/a-foundation-model-for-atomistic-materials-chemistry

Machine-learned force fields have transformed the atomistic modeling of materials by enabling simulations of ab initio quality on unprecedented time and length scales. However, they are currently limited by: (i) the significant computational and human effort that must go into development and validation of potentials for each particular system of interest; and (ii) a general lack of transferability from one chemical system to the next. Here, using the state-of-the-art MACE architecture we introduce a single general-purpose ML model, trained on a public database of 150k inorganic crystals, that is capable of running stable molecular dynamics on molecules and materials.

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