Training: Rapid Exploration of Composition–Property Space with MedeA
In this training, we will highlight the capabilities of MedeA for performing configurational sampling of alloys and doped structures. These methods involve random substitutions, special quasi-random structures, cluster expansion methods, molecular dynamics of doped supercells using machine-learned potentials, and how electronic properties can be predicted for large, doped supercells using machine learning.
Support & Application Scientist
Today’s speaker, Dr. Cheng-Wei Lee, is passionate about enabling scientific and engineering breakthroughs through computational methods. He recently joined the team at Materials Design, Inc. as a Support and Application Scientist, bringing with him deep expertise in multiscale materials simulations, including Density Functional Theory (DFT), Molecular Dynamics (MD), and Machine Learning (ML). Before joining Materials Design Inc., Dr. Lee was a Postdoctoral Researcher at the Colorado School of Mines. There, he specialized in computational discovery and engineering of wide bandgap materials for applications in power electronics, optoelectronics, ferroelectrics, and batteries. He obtained his Ph.D. in Materials Science and Engineering from the University of Illinois at Urbana-Champaign, where his research focused on non-adiabatic electron-ion dynamics in semiconductors and their responses to ionizing ion beams.