Corey Oses

About

Corey Oses is a leading figure in computational materials discovery, specializing in high-throughput methods, machine learning, and autonomous experimentation. His most impactful work, "On-the-fly closed-loop materials discovery via Bayesian active learning" (325 citations), pioneered a paradigm where Bayesian active learning guides autonomous experiments in real time, dramatically accelerating the identification of novel materials. This closed-loop approach—integrating theory, computation, and automated synthesis—has become a cornerstone of modern materials informatics. Oses is also known for developing large-scale databases and frameworks that enable systematic exploration of inorganic crystal structures, particularly through the AFLOW consortium. His contributions have directly advanced the discovery of thermoelectrics, high-entropy alloys, and topological materials, with his work collectively amassing thousands of citations. By merging physics-based simulations with data-driven strategies, Oses has helped define a new era of accelerated materials design, making him a key figure for students and researchers interested in the intersection of artificial intelligence and solid-state chemistry.

Research Focus

Key Achievements

Top Papers

1. 1
   On-the-fly closed-loop materials discovery via Bayesian active learning

   325 citations

Key Collaborators

Contact & Links