The OpenLAM Challenges
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915125488779264 |
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| author | Peng, Anyang Liu, Xinzijian Guo, Ming-Yu Zhang, Linfeng Wang, Han |
| author_facet | Peng, Anyang Liu, Xinzijian Guo, Ming-Yu Zhang, Linfeng Wang, Han |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_16358 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The OpenLAM Challenges Peng, Anyang Liu, Xinzijian Guo, Ming-Yu Zhang, Linfeng Wang, Han Machine Learning Materials Science Computational Physics 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. |
| title | The OpenLAM Challenges |
| topic | Machine Learning Materials Science Computational Physics |
| url | https://arxiv.org/abs/2501.16358 |