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Found 9 papers, 8 papers with code
Flow Annealed Importance Sampling Bootstrap
Normalizing flows are tractable density models that can approximate complicated target distributions, e. g. Boltzmann distributions of physical systems.
MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields
In particular, we show that using four-body messages reduces the required number of message passing iterations to just two, resulting in a fast and highly parallelizable model, reaching or exceeding state-of-the-art accuracy on the rMD17, 3BPA, and AcAc benchmark tasks.
The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials
The rapid progress of machine learning interatomic potentials over the past couple of years produced a number of new architectures.
Bootstrap Your Flow
Normalizing flows are flexible, parameterized distributions that can be used to approximate expectations from intractable distributions via importance sampling.
DOCKSTRING: easy molecular docking yields better benchmarks for ligand design
The field of machine learning for drug discovery is witnessing an explosion of novel methods.
A Fresh Look at De Novo Molecular Design Benchmarks
De novo molecular design is a thriving research area in machine learning (ML) that lacks ubiquitous, high-quality, standardized benchmark tasks.
Symmetry-Aware Actor-Critic for 3D Molecular Design
Automating molecular design using deep reinforcement learning (RL) has the potential to greatly accelerate the search for novel materials.
Reinforcement Learning for Molecular Design Guided by Quantum Mechanics
Automating molecular design using deep reinforcement learning (RL) holds the promise of accelerating the discovery of new chemical compounds.
A Generative Model for Molecular Distance Geometry
Great computational effort is invested in generating equilibrium states for molecular systems using, for example, Markov chain Monte Carlo.
