Quantum-assisted machine learning screening for sustainable anode discovery in lithium-ion batteries

Abstract

A comprehensive analysis of 9835 crystal structures, 211 of which are calculated to be thermodynamically stable, is presented, assessing their potential as anode materials for lithium-ion batteries. Density functional theory (DFT) calculations and advanced machine learning techniques are employed to explore the stability, lithium diffusion, bulk modulus and shear stress, along with the relationships between atomic orbital overlap, energy density, and ion mobility, which is a crucial factors for rapid charging capabilities. The study also examines the combined effects of elemental composition and crystallographic space groups to identify the key drivers of structural toughness. A number of cr..