Prof. Nongnuch Artrith, Materials Chemistry and Catalysis, Debye Institute For Nanomaterials Science, Utrecht University gave a talk on Development of efficient and accurate machine-learning potentials for the simulation of complex molecules and materials on 8 April as part of the ML4Science Research Café Seminar Series.

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. During her talk Prof. Nongnuch Artrith gave an overview of their contributions to the development of ML potentials based on artificial neural networks (ANNs), showing how large computational and small experimental data sets can be integrated for the ML-guided discovery of catalyst materials.

Prof. Nongnuch Artrith is a Tenure-Track Assistant Professor in the Materials Chemistry and Catalysis Group at the Debye Institute for Nanomaterials Science. Prior to joining Utrecht University, she was a Research Scientist at Columbia University, USA, and a PI in the Columbia Center for Computational Electrochemistry. Prof. Artrith obtained her Ph.D in Theoretical Chemistry from Ruhr University Bochum, Germany, for the development of machine-learning (ML) models for materials chemistry. She was awarded a Schlumberger Foundation fellowship for postdoctoral research at MIT and subsequently joined UC Berkeley as an associate specialist. In 2019, she was named a Scialog Fellow for Advanced Energy Storage. Prof. Artrith is the main developer of the open-source ML package ænet (http://ann.atomistic.net) for atomistic simulations. Her research interests focus on the development and application of first principles and ML methods for the computational discovery of energy materials and for the interpretation of experimental observations.