Machine learning has become an increasingly powerful tool for many aspects of society and everyday life. The application of machine learning to the scientific community, specifically the design of materials at an electronic structure level, is still a rapidly evolving branch of research. Reliable development and production of these machine learning applications is of great importance if physics researchers want to harness the speed and efficiency improvements that machine learning has to offer
Development and Application of Robust DFT-Based Machine Learned Interatomic Potentials for Simulating and Understanding Dynamics on Solid Surfaces and at Liquid-Solid Interfaces
John W. Janisch