Scaling deep learning for materials discovery.

ABSTRACT: Novel functional materials enable fundamental breakthroughs across technological applications from clean energy to information processing^1-11. From microchips to batteries and photovoltaics, discovery of inorganic crystals has been bottlenecked by expensive trial-and-error approaches. Concurrently, deep-learning models for language, vision and biology have showcased emergent predictive capabilities with increasing data and computation^12-14. Here we show that graph networks trained at scale can reach unprecedented levels of generalization, improving the efficiency of materials discovery by an order of magnitude. Building on 48,000 stable crystals identified in continuing studies^15-17, improved efficiency enables the discovery of 2.2 million structures below

SUBMITTER: Merchant A 

PROVIDER: S-EPMC10700131 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature