Learning Crystallographic Disorder: Bridging Prediction and Experiment in Materials Discovery.

ABSTRACT: Recent computational materials discovery efforts have led to an enormous number of predictions of previously unknown, potentially stable inorganic, crystalline compounds. In particular, both high-throughput screenings and generative models have benefited tremendously from recent advances in computational resources and available data. However, these efforts are currently limited to predicting pristine crystalline materials. As a consequence, many of these predictions cannot be realized in experiments, where kinetic effects, defects, and crystallographic disorder can be crucial. To address this shortcoming, the current work aims to introduce disorder into computational materials discovery with machine learning (ML) based classification models. Trained on the inorganic crystal structure datab

SUBMITTER: Jakob KS 

PROVIDER: S-EPMC12822528 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature