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Foundation models for materials discovery-current state and future directions
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作者 Edward O.Pyzer-Knapp Matteo Manica +5 位作者 peter staar Lucas Morin Patrick Ruch Teodoro Laino John R.Smith Alessandro Curioni 《npj Computational Materials》 2025年第1期611-620,共10页
Large language models,commonly known as LLMs,are showing promise in tacking some of the most complex tasks in AI.In this perspective,we review the wider field of foundation models-of which LLMs are a component-and the... Large language models,commonly known as LLMs,are showing promise in tacking some of the most complex tasks in AI.In this perspective,we review the wider field of foundation models-of which LLMs are a component-and their application to the field of materials discovery.In addition to the current state of the art-including applications to property prediction,synthesis planning and molecular generation-we also take a look to the future,and posit how new methods of data capture,and indeed modalities of data,will influence the direction of this emerging field. 展开更多
关键词 foundation models large language models property prediction large language modelscommonly materials discovery molecular generation we property predictionsynthesis planning materials discoveryin
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