Abstract Scope |
Many materials with applications in energy applications, e.g., catalysis or batteries, are non-crystalline with amorphous structures, chemical disorder, and complex compositions, which makes the direct modeling with first principles methods challenging. To address this challenge, we developed accelerated sampling strategies based on machine learning potentials, genetic algorithms, and molecular-dynamics simulations. Here, I will discuss the methodology and applications to amorphous battery materials. We constructed the phase diagram of amorphous LiSi alloys, prospective anode materials for lithium-ion batteries. And we mapped the composition and structure space of amorphous LiPON and LPS solid electrolytes. The thermodynamic stability and ionic conductivity of the non-crystalline phases was correlated with local structural motifs, leading to the identification of structure-composition-conductivity relationships that can be used for materials optimization and design. Further, I will show how large computational and small experimental data sets can be integrated for the ML-guided discovery of catalyst materials. |