First-principles study on the lithiation process of amorphous SiO anode for Li-ion batteries with Bayesian optimization.

Amorphous silicon monoxide (a-SiO), which contains Si atoms with various valence states, has attracted much attention as a high-performance anode material for lithium (Li) ion batteries (LIBs). Although current experiments have provided some information during charge/discharge cycles, further investigation of structural changes at the atomic scale is needed. To investigate the lithiation process of a-SiO using first-principles simulations and machine learning techniques, we developed a computational code employing Bayesian optimization to efficiently identify stable sites for Li insertion in the large search-space of amorphous models to reproduce the actual lithiation process and compared this approach to the conventional random scheme by applying it to an a-SiO model previously generated with neural network potentials. The lithiation process based on Bayesian optimization resulted in lower formation energies compared to the conventional random scheme, indicating a more stable structure. During lithiation, Li atoms tended to enter the silicon (Si) phase after the SiO phase, in agreement with experimental results. We analyzed the structural changes and observed significant differences in the structural evolution between the conventional and new schemes. Our study highlights the significant influence of the lithiation process on the structural transformation of a-SiO materials, which in turn affects the reversible capacity of the material. These findings will provide a framework for improving the performance and lifetime of a-SiO materials.