Alexey Kolmogorov's research focuses on the design of new materials with density functional theory and machine learning methods. With a background in physics, materials science and computer science, he develops and uses materials modeling tools at the intersection of the three disciplines. Before joining the department in 2012, he was a postdoctoral researcher at Duke University (2004-2007) and a senior research fellow at the University of Oxford (2008-2012).
His group has developed an open-source MAISE package for predicting new synthesizable materials. MAISE features an evolutionary algorithm for finding stable crystal structures and a neural network module for modeling interatomic interactions.
Confirmed predictions include the first synthesized superconductor designed fully in silico. For more information about his research and published work, please see his website.
- PhD, Pennsylvania State University
- MS, Moscow Institute of
Physics and Technology
- Computational condensed matter physics
- Design of superconducting, topological and battery materials
- Machine learning and evolutionary optimization
- NSF Award to design high-Tc conventional superconductors (BU 2023)
- NSF EAGER Award to predict doped-covalent-bond superconductors (BU 2021)
- NSF Award to design tin-based topological insulators, battery anodes, and lead-free solders (BU, 2018)
- NSF Award to accelerate materials prediction with neural networks (BU, 2014)
- EPSRC Career Acceleration Fellowship to develop new metal boride materials (University of Oxford, 2008)