The Computational and Statistical Material Science Lab | Lead: Kristofer Reyes

About the lab

Our lab works at the interface of computation, mathematics, statistics, and materials science. We are specifically interested in meaningful machine learning in the small-data regime that dominates much of materials science. In this regime, we must be:

  1. Economical with the limited data that we do have;
  2. Strategic in acquiring new data; and
  3. Resourceful by incorporating knowledge and information from other sources.

We work on Bayesian models, decision-making under uncertainty, reinforcement learning, and methods to fuse data with physics-based knowledge and expert opinion. We do not view materials science as a black-box source of “yet-another-dataset”. Instead, the field offers a rich problem setting filled with nuance and structure. We develop methods that appreciate this nuance and utilize such structure.

More information about the lab





Project pages

Holmes: a framework for decision-making under uncertainty

Kris’s Discount Wheels: key references for ML/AI for materials

What’s new

CSMS Lab (Twitter: @CSMSLab | Email: [email protected]) | Department of Materials Design and Innovation | 134 Bell Hall, University at Buffalo