Geometric Deep Learning for Molecular Crystal Structure Prediction.

ABSTRACT: We develop and test new machine learning strategies for accelerating molecular crystal structure ranking and crystal property prediction using tools from geometric deep learning on molecular graphs. Leveraging developments in graph-based learning and the availability of large molecular crystal data sets, we train models for density prediction and stability ranking which are accurate, fast to evaluate, and applicable to molecules of widely varying size and composition. Our density prediction model, MolXtalNet-D, achieves state-of-the-art performance, with lower than 2% mean absolute error on a large and diverse test data set. Our crystal ranking tool, MolXtalNet-S, correctly discriminates experimental samples from synthetically generated fakes and is further validated through analysis of th

SUBMITTER: Kilgour M 

PROVIDER: S-EPMC10373482 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature