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The OpenLAM Challenges

Machine Learning 2025-01-29 v1 Materials Science Computational Physics

Abstract

Inspired by the success of Large Language Models (LLMs), the development of Large Atom Models (LAMs) has gained significant momentum in scientific computation. Since 2022, the Deep Potential team has been actively pretraining LAMs and launched the OpenLAM Initiative to develop an open-source foundation model spanning the periodic table. A core objective is establishing comprehensive benchmarks for reliable LAM evaluation, addressing limitations in existing datasets. As a first step, the LAM Crystal Philately competition has collected over 19.8 million valid structures, including 1 million on the OpenLAM convex hull, driving advancements in generative modeling and materials science applications.

Keywords

Cite

@article{arxiv.2501.16358,
  title  = {The OpenLAM Challenges},
  author = {Anyang Peng and Xinzijian Liu and Ming-Yu Guo and Linfeng Zhang and Han Wang},
  journal= {arXiv preprint arXiv:2501.16358},
  year   = {2025}
}
R2 v1 2026-06-28T21:20:24.612Z