AI-Assisted Chemistry: Predicting Chemical Properties with Minimal Expert Knowledge
Using deep learning and with virtually no expert knowledge, we construct computational chemistry models that perform favorably to existing state-of-the-art models developed by expert practitioners, whose models rely on the knowledge gained from decades of academic research. Our findings potentially demonstrates the impact of AI assistance in accelerating the scientific discovery process, where we envision future applications not just in chemistry, but in affiliated fields, such as biotechnology, pharmaceuticals, consumer goods, and perhaps other domains as well.
Garrett Goh is a Scientist at the Pacific Northwest National Lab (PNNL), in the Advanced Computing, Mathematics & Data Division. He was previously awarded the Howard Hughes Medical Institute fellowship which supported his PhD in Computational Chemistry at the University of Michigan. At PNNL, he was awarded the Pauling Fellowship that supports his research initiative of combining deep learning and artificial intelligence with traditional chemistry applications. His current interests is in AI-assisted computational chemistry, which is the application of deep learning to predict chemical properties and the discovery of new chemical insights, while using minimal expert knowledge.