Upper-Bound Energy Minimization to Search for Stable Functional Materials with Graph Neural Networks.

ABSTRACT: The discovery of new materials in unexplored chemical spaces necessitates quick and accurate prediction of thermodynamic stability, often assessed using density functional theory (DFT), and efficient search strategies. Here, we develop a new approach to finding stable inorganic functional materials. We start by defining an upper bound to the fully relaxed energy obtained via DFT as the energy resulting from a constrained optimization over only cell volume. Because the fractional atomic coordinates for these calculations are known a priori, this upper bound energy can be quickly and accurately predicted with a scale-invariant graph neural network (GNN). We generate new structures via ionic substitution of known prototypes, and train our GNN on a new database of 128 000 DFT calculatio

SUBMITTER: Law JN 

PROVIDER: S-EPMC9875372 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature