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The OpenLAM Challenges
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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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Cited by 1 Pith paper
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Universal Machine Learning Potentials under Pressure
Universal machine learning interatomic potentials systematically lose accuracy under pressure up to 150 GPa, and fine-tuning on high-pressure DFT data recovers most of the lost performance.
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