{"work":{"id":"29bfbd6f-762e-4448-8f07-4f24bf553e51","openalex_id":"https://openalex.org/W4390528994","doi":"10.48550/arxiv.2401.00096","arxiv_id":"2401.00096","raw_key":null,"title":"A foundation model for atomistic materials chemistry","authors":null,"authors_text":"Ilyes Batatia, Philipp Benner, Yuan Chiang, Alin M. Elena, D\\'avid P. Kov\\'acs, Janosh Riebesell","year":2023,"venue":"physics.chem-ph","abstract":"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 ML force fields have largely been limited by: (i) the substantial computational and human effort of developing and validating 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 get reliable results much faster, and beginners face a lower barrier to entry. Foundation models thus represent a step towards democratising the revolution in atomic-scale modeling that has been brought about by ML force fields.","external_url":"https://arxiv.org/abs/2401.00096","cited_by_count":246,"metadata_source":"pith","metadata_fetched_at":"2026-08-05T02:28:24.338817+00:00","pith_arxiv_id":"2401.00096","created_at":"2026-05-10T00:24:46.904677+00:00","updated_at":"2026-08-05T02:28:24.338817+00:00","title_quality_ok":true,"display_title":"A foundation model for atomistic materials chemistry","render_title":"A foundation model for atomistic materials chemistry"},"hub":{"state":{"work_id":"29bfbd6f-762e-4448-8f07-4f24bf553e51","tier":"hub","tier_reason":"10+ Pith inbound or 1,000+ external citations","pith_inbound_count":35,"external_cited_by_count":246,"distinct_field_count":6,"first_pith_cited_at":"2024-05-08T11:13:30+00:00","last_pith_cited_at":"2026-07-06T16:59:26+00:00","author_build_status":"not_needed","summary_status":"needed","contexts_status":"needed","graph_status":"needed","ask_index_status":"not_needed","reader_status":"not_needed","recognition_status":"not_needed","updated_at":"2026-08-23T04:19:34.285936+00:00","tier_text":"hub"},"tier":"hub","role_counts":[{"context_role":"method","n":4},{"context_role":"background","n":2},{"context_role":"baseline","n":2},{"context_role":"dataset","n":1}],"polarity_counts":[{"context_polarity":"use_method","n":4},{"context_polarity":"background","n":2},{"context_polarity":"baseline","n":2},{"context_polarity":"use_dataset","n":1}],"runs":{},"summary":{},"graph":{},"authors":[]}}