Division/Committee: [PHYS] Division of Physical Chemistry

The past years have been marked by fast-paced advances in the data-driven and Machine Learning (ML)-aided design and characterization of energy materials. Equally remarkable is the broad range of theoretical methods, physical systems, and applications that ML-aided research has enabled to explore. Recent advances in the field include the design of high-performance materials for energy storage, the modeling of catalysts for renewable energy, combinatorial approaches for ceramic fuel cells, combined quantum mechanical-ML approaches for electrochemical systems, and the design of novel nanoporous materials for hydrogen capture, among others. This symposium will bring together chemists, physicists, and engineers from these different areas, with complementary computational and experimental expertise, to share the cutting-edge advances in the design of energy materials enabled by ML in their respective fields, and to explore how recent methodological developments can be transferred from one area to another, to accelerate the discovery and address outstanding challenges in energy materials research.

Jerome Delhommelle

University of Massachusetts Lowell

Appearances