Machine-Learning Rationalization and Prediction of Solid-State Synthesis Conditions.

ABSTRACT: There currently exist no quantitative methods to determine the appropriate conditions for solid-state synthesis. This not only hinders the experimental realization of novel materials but also complicates the interpretation and understanding of solid-state reaction mechanisms. Here, we demonstrate a machine-learning approach that predicts synthesis conditions using large solid-state synthesis data sets text-mined from scientific journal articles. Using feature importance ranking analysis, we discovered that optimal heating temperatures have strong correlations with the stability of precursor materials quantified using melting points and formation energies (ΔG _f , ΔH _f ). In contrast, features derived from the thermodynamics of synthesis-rela

SUBMITTER: Huo H 

PROVIDER: S-EPMC9407029 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature