Material modeling is now a common tool in the investigative tool box to discover and design new materials and to interpret experimental findings. This presentation will discuss the application of material modeling to problems where an atomistic understanding is desirable at length scales that are larger than what are readily accessible with quantum chemical approaches. Illustrative applications include thin-film growth and understanding structure-property relationships in carbide-derived carbon materials.
Back to Understanding Many-Particle Systems with Machine Learning Tutorials