Context (archaeology)

Biology

Materials science

Stability (learning theory)

Operating system

Parallel computing

Computer science

Decomposition

Statistical physics

Solid mechanics

Paleontology

Thermodynamics

Physics

Organic chemistry

Chemical stability

Supercomputer

Chemistry

Machine learning

Journal of Materials Science

Improvements in the efficiency and availability of quantum chemistry codes, supercomputing centers, and open materials databases have transformed the accessibility of computational materials design approaches. Thermodynamic stability predictions play a central role in the efficacy of these approaches and should be considered carefully. This review covers the fundamentals of calculating thermodynamic stability using first-principles methods. Stability is delineated into two main topics—stability with respect to decomposition into competing phases and stability with respect to phase transition into alternative structures at fixed composition. For each topic, a summary of the state-of-the-art is provided along with a tutorial overview of practical considerations. The application of machine learning to both kinds of stability predictions is also covered. Finally, the limitations of thermodynamic stability predictions are discussed within the context of predicting the synthesizability of materials.

Review of computational approaches to predict the thermodynamic stability of inorganic solids