The properties of materials for energy applications, such as heterogeneous catalysts and battery materials, often depend on complicated chemical compositions and complex structural features including defects and disorder. This complexity makes the direct modeling with first principles methods challenging. Machine-learning (ML) potentials trained on first principles reference data enable linear-scaling atomistic simulations with an accuracy that is close to the reference method at a fraction of the computational cost. ML models can also be trained to predict the outcome of simulations (or experiments), bypassing explicit atomistic modeling altogether. In this talk, I will give an overview of recent methodological advancements of ML potentials based on artificial neural networks (ANNs) and applications of the method to challenging materials classes including metal and oxide nanoparticles and amorphous phases. Further, I will show an example of integrating large computational and small experimental data sets for the ML-guided discovery of catalyst materials.

Modeling of complex energy materials with machine learning

ACS Fall 2021

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.