Lee, D. K. J., Tan, T. L., and Ng, M.-F. (2024). Machine Learning-Assisted Bayesian Optimization for the Discovery of Effective Additives for Dendrite Suppression in Lithium Metal Batteries. ACS Applied Materials and Interfaces, 16(46), 64364–64376. https://doi.org/10.1021/acsami.4c16611
Abstract:
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 mitigation. Two key mechanisms, namely the formation of robust solid electrolyte interphase (SEI) layers and the levelling mechanism, serve as the foundation for our investigation. Our inverse design strategy is guided by molecular properties such as the LUMO energy and interaction energy upon Li surface adsorption. An active learning process utilizing Bayesian Optimization (BO) was utilized to identify potential molecules with the ideal properties. Through this screening process, we uncover a collection of 62 molecules with the potential to act as SEI-forming additives, along with 106 molecules for levelling additives – both surpassing the performance of established additives reported in literature. This work highlights the potential of Bayesian Optimization methods in computational-based inverse design of materials for many applications, and the discovered additives could potentially boost the commercialization of Li-metal batteries.
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Funding Info:
This research / project is supported by the Agency for Science, Technology and Research - Manufacturing, Trade, and Connectivity Programmatic Fund: Physics and Knowledge Transfer-based Cognitive Digital Twin for Advanced Battery Analytics
Grant Reference no. : M23L9b0052
This research / project is supported by the Singapore National Research Foundation - Singapore-China Joint Flagship Project 2023 (Clean Energy): Addressing Challenges of Zn Aqueous Batteries for Stationary Energy Storage and Carbon Neutrality
Grant Reference no. : NA
This research / project is supported by the Singapore National Research Foundation - Competitive Research Program
Grant Reference no. : NRF-CRP23-2019-0001