Machine learning the metastable phase diagram of covalently bonded carbon.

ABSTRACT: Conventional phase diagram generation involves experimentation to provide an initial estimate of the set of thermodynamically accessible phases and their boundaries, followed by use of phenomenological models to interpolate between the available experimental data points and extrapolate to experimentally inaccessible regions. Such an approach, combined with high throughput first-principles calculations and data-mining techniques, has led to exhaustive thermodynamic databases (e.g. compatible with the CALPHAD method), albeit focused on the reduced set of phases observed at distinct thermodynamic equilibria. In contrast, materials during their synthesis, operation, or processing, may not reach their thermodynamic equilibrium state but, instead, remain trapped in a local (metastable) free ener

SUBMITTER: Srinivasan S 

PROVIDER: S-EPMC9170764 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature