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Michael Scherbela - High accuracy wavefunctions using deep-learning-based variational Monte Carlo
Presenter
- Michael Scherbela
May 27, 2022
IPAM
Julia Westermayr - Physically inspired machine learning for excited states
Presenter
- Julia Westermayr
May 27, 2022
IPAM
Ilyes Batatia - Unified understanding of E(3)-Equivariant Interatomic Potentials Theory/Applications
Presenter
- Ilyes Batatia
May 27, 2022
IPAM
Matthew Foulkes - Approximating Many-Electron Wave Functions using Neural Networks
Presenter
- Matthew Foulkes
May 26, 2022
IPAM
Kieron Burke - Conditional probability density functional theory
Presenter
- Kieron Burke
May 26, 2022
IPAM
David Ceperley - Quantum Monte Carlo and Machine Learning Simulations of Dense Hydrogen
Presenter
- David Ceperley
May 26, 2022
IPAM
Mark Tuckerman - Quantum time correlation functions in an open-chain path integral distribution
Presenter
- Mark Tuckerman
May 26, 2022
IPAM
Lucas Wagner - Compact representations of excited states from QMC, as data for low-energy models
Presenter
- Lucas Wagner
May 26, 2022
IPAM
Marivi Fernandez Serra - Machine learning to improve the exchange and correlation functional in DFT
Presenter
- Marivi Fernandez-Serra
May 26, 2022
IPAM
Bryan Clark - Variational Wavefunctions, Machine Learning Architecture for Fermions & Gauge Theories
Presenter
- Bryan Clark
May 26, 2022
IPAM
Brenda Rubenstein - Extending the Reach of Quantum Monte Carlo Methods via Machine Learning
Presenter
- Brenda Rubenstein
May 26, 2022
IPAM
