Some Thoughts on Generative Machine Learning/Artificial Intelligenceε
Since providing one's thoughts on this subject seem to be all the rage right now (August 2026), I might as well have a crack at it. I started doing some research in machine learning (ML) applied to physical chemistry-based problems in 2021 as part of a postdoctoral stint in Prof. Pratyush Tiwary's lab at the University of Maryland, College Park because I could see how heavily my professional field (computational chemistry) was starting to lean in that direction. I didn't like ML then because it felt a bit like cheating -- instead of coming up with a physics-based theory to describe phenomena in a system (e.g. the "slow" eigenfunctions of the Markov transition matrix or transfer operator) folks started resorting to training very complicated (to me, at the time) neural networks to approximate these sometimes-complex functions. So, all the physics became buried in an inscrutable neural network, and there is still no consensus way to dig that information out, althou...