Machine Learning-Assisted Bayesian Optimization for the Discovery of Effective Additives for Dendrite Suppression in Lithium Metal Batteries

In the pursuit of enhancing the performance and safety of lithium (Li)-metal batteries, the discovery of effective electrolyte additives to suppress Li dendrites has emerged as a paramount objective. In this study, we employ an inverse design strategy to identify potential additives for dendrite mit...

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Bibliographic Details
Main Author:	Damien K. J. Lee (18965362) (author)
Other Authors:	Teck Leong Tan (3856267) (author), Man-Fai Ng (1400281) (author)
Published:	2024
Subjects:	Biophysics Biochemistry Cell Biology Genetics Molecular Biology Pharmacology Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified two key mechanisms suppress li dendrites inverse design strategy assisted bayesian optimization established additives reported lithium metal batteries effective electrolyte additives identify potential additives identify potential molecules effective additives leveling additives forming additives 62 molecules 106 molecules work highlights screening process paramount objective molecular properties many applications machine learning leveling mechanism ideal properties dendrite suppression dendrite mitigation
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Machine Learning-Assisted Bayesian Optimization for the Discovery of Effective Additives for Dendrite Suppression in Lithium Metal Batteries

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