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
New tools enable new ways of working,and materials science is no exception.In materials discovery,traditional manual,serial,and human-intensive work is being augmented by automated,parallel,and iterative processes dri...New tools enable new ways of working,and materials science is no exception.In materials discovery,traditional manual,serial,and human-intensive work is being augmented by automated,parallel,and iterative processes driven by Artificial Intelligence (AI),simulation and experimental automation.In this perspective,we describe how these new capabilities enable the acceleration and enrichment of each stage of the discovery cycle.We show,using the example of the development of a novel chemically amplified photoresist,how these technologies’ impacts are amplified when they are used in concert with each other as powerful,heterogeneous workflows.展开更多
文摘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.
文摘New tools enable new ways of working,and materials science is no exception.In materials discovery,traditional manual,serial,and human-intensive work is being augmented by automated,parallel,and iterative processes driven by Artificial Intelligence (AI),simulation and experimental automation.In this perspective,we describe how these new capabilities enable the acceleration and enrichment of each stage of the discovery cycle.We show,using the example of the development of a novel chemically amplified photoresist,how these technologies’ impacts are amplified when they are used in concert with each other as powerful,heterogeneous workflows.
