Autonomous and dynamic precursor selection for solid-state materials synthesis.

ABSTRACT: Solid-state synthesis plays an important role in the development of new materials and technologies. While in situ characterization and ab-initio computations have advanced our understanding of materials synthesis, experiments targeting new compounds often still require many different precursors and conditions to be tested. Here we introduce an algorithm (ARROWS³) designed to automate the selection of optimal precursors for solid-state materials synthesis. This algorithm actively learns from experimental outcomes to determine which precursors lead to unfavorable reactions that form highly stable intermediates, preventing the target material's formation. Based on this information, ARROWS³ proposes new experiments using precursors it predicts to avoid such intermediates,

SUBMITTER: Szymanski NJ 

PROVIDER: S-EPMC10618174 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature