Designing optimal formulations is a major challenge in developing electrolytes for the next generation of rechargeable batteries due to the vast combinatorial design space and complex interplay between multiple consti...Designing optimal formulations is a major challenge in developing electrolytes for the next generation of rechargeable batteries due to the vast combinatorial design space and complex interplay between multiple constituents.Machine learning(ML)offers a powerful tool to uncover underlying chemical design rules and accelerate the process of formulation discovery.In this work,we present an approach to design new formulations that can achieve target performance,using a generalizable chemical foundation model.The chemical foundation model is fine-tuned on an experimental dataset of 13,666 ionic conductivity values curated from the lithium-ion battery literature.The fine-tuned model is used to discover 7 novel high conductivity electrolyte formulations through generative screening,improving the conductivity of LiFSI-and LiDFOB-based electrolytes by 82%and 172%,respectively.These findings highlight a generalizable workflow that is highly adaptable to the discovery of chemical mixtures with tailored properties to address challenges in energy storage and beyond.展开更多
文摘Designing optimal formulations is a major challenge in developing electrolytes for the next generation of rechargeable batteries due to the vast combinatorial design space and complex interplay between multiple constituents.Machine learning(ML)offers a powerful tool to uncover underlying chemical design rules and accelerate the process of formulation discovery.In this work,we present an approach to design new formulations that can achieve target performance,using a generalizable chemical foundation model.The chemical foundation model is fine-tuned on an experimental dataset of 13,666 ionic conductivity values curated from the lithium-ion battery literature.The fine-tuned model is used to discover 7 novel high conductivity electrolyte formulations through generative screening,improving the conductivity of LiFSI-and LiDFOB-based electrolytes by 82%and 172%,respectively.These findings highlight a generalizable workflow that is highly adaptable to the discovery of chemical mixtures with tailored properties to address challenges in energy storage and beyond.
