开源原子模型基准挑战，推动材料科学生成模型发展
The OpenLAM Challenges
- 构建覆盖元素周期表的开源大原子模型预训练框架
- 收集超1980万有效晶体结构，含百万个开放晶格点数据
- 适合材料生成与科学计算研究者参考
受大型语言模型成功启发，大型原子模型（LAM）在科学计算领域快速发展。自2022年起，深度势能团队持续预训练LAM，并发起开源大原子模型计划（OpenLAM Initiative），旨在建立跨元素周期表的开源基础模型。核心目标是构建全面的评估基准，弥补现有数据集的不足。作为第一步，LAM晶体邮票竞赛已收集超过1980万条有效晶体结构，其中包括100万个位于OpenLAM凸包内的结构，显著推动了生成建模与材料科学应用的发展。
原文摘要 · Abstract (English)
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.
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