Gregor N. C. Simm — accepted AI-conference papers

2023

Flow Annealed Importance Sampling Bootstrap

Laurence Illing Midgley, Vincent Stimper, Gregor N. C. Simm, Bernhard Schölkopf, José Miguel Hernández-Lobato

ICLR 2023top-25%

Normalizing flows are tractable density models that can approximate complicated target distributions, e.g. Boltzmann distributions of physical systems. However, current methods for training flows either suffer from mode-seeking behavior, use samples from the target generated beforehand by expensive…

Cited by 82 Source ↗PDF ↗code ↗Scholar ↗

2022

MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields

Ilyes Batatia, David Peter Kovacs, Gregor N. C. Simm, Christoph Ortner, Gabor Csanyi

NeurIPS 2022accept

Creating fast and accurate force fields is a long-standing challenge in computational chemistry and materials science. Recently, Equivariant Message Passing Neural Networks (MPNNs) have emerged as a powerful tool for building machine learning interatomic potentials, outperforming other approaches in…

Cited by 715 Source ↗PDF ↗code ↗Scholar ↗

2021

Symmetry-Aware Actor-Critic for 3D Molecular Design

Gregor N. C. Simm, Robert Pinsler, Gábor Csányi, José Miguel Hernández-Lobato

ICLR 2021poster

Automating molecular design using deep reinforcement learning (RL) has the potential to greatly accelerate the search for novel materials. Despite recent progress on leveraging graph representations to design molecules, such methods are fundamentally limited by the lack of three-dimensional (3D) inf…

Cited by 82 Source ↗PDF ↗code ↗Scholar ↗