304 citations · 1.4k across the 15 of their papers we have counts for
17 papers
A strategic roadmap for an atomistic machine-learning ecosystem
Jörg Behler, Michele Ceriotti, Cecilia Clementi +47
Data-driven machine learning (ML) techniques have become an essential tool in many domains of science. Their application to atomistic simulations of matter is particularly widespre…
Flow Annealed Importance Sampling Bootstrap
Laurence Illing Midgley, Vincent Stimper, Gregor N. C. Simm +2
Normalizing flows are tractable density models that can approximate complicated target distributions, e.g. Boltzmann distributions of physical systems. However, current methods for…
MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields
Ilyes Batatia, Dávid Péter Kovács, Gregor N. C. Simm +2
Creating fast and accurate force fields is a long-standing challenge in computational chemistry and materials science. Recently, several equivariant message passing neural networks…
The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials
Ilyes Batatia, Simon Batzner, Dávid Péter Kovács +6
The rapid progress of machine learning interatomic potentials over the past couple of years produced a number of new architectures. Particularly notable among these are the Atomic…
Bootstrap Your Flow
Laurence Illing Midgley, Vincent Stimper, Gregor N. C. Simm +1
Normalizing flows are flexible, parameterized distributions that can be used to approximate expectations from intractable distributions via importance sampling. However, current fl…
DOCKSTRING: easy molecular docking yields better benchmarks for ligand design
Miguel García-Ortegón, Gregor N. C. Simm, Austin J. Tripp +3
The field of machine learning for drug discovery is witnessing an explosion of novel methods. These methods are often benchmarked on simple physicochemical properties such as solub…