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Hilke Bahmann (Chemistry, Wuppertal) |
Ha Quang Minh (AI, RIKEN-AIP) |
Our Team
Augusto Gerolin
CRC in AI at Math & Chemistry
Assistant professor
Adolfo Vargas-Jiménez
Postdoctoral Researcher
Mathematics
Dmitry Evdokimov
PhD student
AI for Science
Nataliia Monina
PhD student
Math & Quantum Chemistry
Pavlo Pelikh
PhD student
OT & Machine Learning
Fanch Coudreuse
Visiting PhD student
Mathematics, ENS-Lyon
Vitalii Bielievtsov
MSc student
DTI & AI
Valeria Kolesnik
MSc student (with Prof. S. Schillo)
DTI & Data Sciences
Nikita Davydov
BSc student, MITACS
Computer Science, Kharkiv (Ukraine)
(with Prof. F. Gentile)
Past members
Daniel Calero
Undergraduate, MITACS (2022)
Physics, U. del Valle (Colombia)
Ben Langton
Undergraduate, MITACS (2022)
Mathematics, Durham (UK)
Akshay Raman
Undergraduate, MITACS (2022)
Computer Sciences, VIT (India)
Liam Meades
Volunteer student (2022)
Quantum Chemistry
Research interests
Calculus of Variations,
Optimal Transport,
Gradient Flows in the space of probability measures,
Numerical methods and approximation,
Theoretical and Computational Chemistry,
- Density Functional Theory
- One-body Reduced Density Matrix Theory
Mathematical Aspects of Machine learning theory
- Likelihood-free Variational Inference and Generative Modelling
- Normalizing flows
- Generative Adversarial Networks
- Statistical Learning Theory
Brief Research Description
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Calculus of Variations
We are interested in fundamental theory and computational algorithms for multi-marginal optimal transport. Examples where our methodology is applied include Wasserstein Barycenters, Mean-Field games and Trajectory Inference in Biology. We also develop tools to improve the understanding of density estimation and generation in GANs, VAEs, Flow and Diffusion-based Generative Models.
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Quantum Chemistry
The focus of our current research is to extend the accuracy of electronic Density Functional Theory (DFT) to systems in which electronic correlation plays a prominent role. In particular using the Stricly Correlated Electron (SCE) formalism in the study of ground state properties of many-electrons system (existence and next-order corrections of SCE DFT) and time-dependent DFT (1d). Another research line focus in extending the accuracy of electronic Density Functional Theory (DFT) to systems in which electronic correlation plays a prominent role. In particular using machine learning methods and the SCE formalism to help in the construction of improved approximate functionals.
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Mathematics of Machine Learning and AI for Chemistry
We are developing tools to improve the understanding of density estimation and generation in GANs, VAES and Normalizing Flows; and developing novel deep learning methods for Computational Chemistry.
Collaborators and Mentors