Highly stable silicon oxycarbide all-solid-state batteries enabled by machined learning accelerated screening of oxides and sulfides electrolytes

Traditional trial-error approach severely limits and restricts rapid development of high-performance anode and electrolytes materials, searching huge parameters space of various anode-solid electrolyte interfaces in an effective and efficient way is the key issue. Here, a novel computational strategy combining machine learning and first-principles is proposed to achieve efficient high-throughput screening of oxides and sulfides electrolytes for highly stable silicon oxycarbide all-solid-state batteries. First-principles calculations demonstrate significant compact of material type and elemental doping on interfacial compatibility between silicon oxycarbide and various electrolytes. By proposing several novel descriptors including interfacial adhesion and formation energies of frozen system with low computation cost, the amounts of demanded trainings data are significantly reduced. Gradient-boosted regression tree model shows low mean absolute errors of 0.09 and high R