Eszter Varga-Umbrich

Name:	Eszter Varga-Umbrich
Affiliation:	University of Cambridge
E-mail:	(only provided to users who are logged into talks.cam)
Last login:	27 Apr 2025, 8:22 p.m.

Public lists managed by Eszter Varga-Umbrich

Talks given by Eszter Varga-Umbrich

Obviously this only lists talks that are listed through talks.cam. Furthermore, this facility only works if the speaker's e-mail was specified in a talk. Most talks have not done this.

Talks organised by Eszter Varga-Umbrich

This list is based on what was entered into the 'organiser' field in a talk. It may not mean that Eszter Varga-Umbrich actually organised the talk, they may have been responsible only for entering the talk into the talks.cam system.

• Neural Thermodynamic Integration: Free Energy estimation with ML-potentials
• Boltz-1 Democratizing Biomolecular Interaction Modeling
• Learning Conical Intersections for Excited States Using Smooth Invariants
• Random sampling versus active learning algorithms for machine learning potentials of quantum liquid water
• Thermodynamic Integration along energy-based Diffusion Models
• Learning Conical Intersections for Excited States Using Smooth Invariants
• Anisotropic machine learning representations for coarse-graining
• ML-MIX: A Package for Seamless Spatial Potential Mixing Inside LAMMPS
• Chemical transferability and accuracy of machine learning interatomic potentials for ionic liquid and battery solvent simulations
• Advanced materials modeling using extended Hubbard functionals
• High-throughput computational thermodynamics: a unique insight into the potential energy surface
• Tensor-Reduced Atomic Density Representations
• Physically constrained machine learning: from single-particle Hamiltonians to electronic excitations
• MatterGen: a generative model for inorganic materials design
• Creating chemical-reaction movies based on automated modeling
• ML-guided Materials Discovery
• Identifying informative distance measures in high-dimensional feature spaces
• Adding functionality to machine-learning potentials: going beyond accuracy and speed
• Machine learning for molecular design of plasmonic nanosystems
• Introducing ACEpotentials.jl
• Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamics