Machine learning has tremendous prediction to accelerate the design of novel materials. I currently collaborate with researchers at Princeton, the Colorado School of Mines, UIUC, and WashU on applications of machine learning and probabilistic methods to material science problems. We are developing new methods for active search, Markov Chain Monte Carlo simulation, and combinatorial design. Current application areas include machine learning for alloy design, ML-assisted property prediction, and topology optimization of structural materials.
Machine learning for material science
Publications
Probabilistic prediction of material stability: integrating convex hulls into Bayesian active learning
arXiv preprint, 2024
arXiv preprint, 2024
Multi-fidelity Monte Carlo: a pseudo-marginal approach
Advances in Neural Information Processing Systems (NeurIPS), 2022
Advances in Neural Information Processing Systems (NeurIPS), 2022
Diana Cai
Center for Computational Mathematics
I am broadly interested in machine learning and statistics, and in particular, developing robust and reliable methods for modeling and inference.