Alexey Kolmogorov
Professor
Physics, Applied Physics and Astronomy
Background
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 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 .
Education
- PhD, Pennsylvania State University
- MS, Moscow Institute of
Physics and Technology
Research Interests
- Computational condensed matter physics
- Design of superconducting, topological and battery materials
- Machine learning and evolutionary optimization
Awards
- to design high-Tc conventional superconductors (BU 2023)
- to predict doped-covalent-bond superconductors (BU 2021)
- to design tin-based topological insulators, battery anodes, and lead-free solders (BU, 2018)
- to accelerate materials prediction with neural networks (BU, 2014)
- to develop new metal boride materials (University of Oxford, 2008)