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Hilke Bahmann (Chemistry, Wuppertal) |
Ha Quang Minh (AI, RIKEN-AIP) |
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