Erik Cheng

Background

About Erik Cheng

I am a materials scientist with 7 years of experience in computational chemistry and 10 years of experience in both materials chemistry and data science. My expertise lies primarily in development and applications of atomistic simulations (DFT, DFTB) for studying nonequilibrium systems, especially of surfaces. For my PhD work in those areas, I published 5 first author papers and received a number of competitive awards from the American Vacuum Society, primarily related to developing a new understanding of reaction mechanisms during plasma enhanced atomic layer etching (PEALE). I am especially interested in working in societally beneficial industries (e.g, semiconductors, batteries, materials and drug discovery), and in contexts where I can leverage both traditional simulation approaches as well as recent advances in data-driven/ML/AI techniques. Working with experimentalists would be a plus. Recently, my postdoc work has focused on developing machine learned interatomic potentials that incorporate explicit physics for better generalizability and reduced model complexity. My PhD work focused on applying atomistic simulations to understand complex chemistry and physics for semiconductor surface processing (specifically in atomic layer etching of silicon nitride). During my undergrad years, I did some experimental spectroscopy and materials processing work in both academic and industry settings. In parallel to my work in materials science in chemistry, I am also passionate about data science and statistics, with my experience including teaching data science as an undergraduate and subsequently earning a master's degree from UT Austin. Full list of publications (including 5 first-authored papers) here: & hl=en & oi=ao