research
∙
05/12/2023
Machine-learning-accelerated simulations enable heuristic-free surface reconstruction
Understanding material surfaces and interfaces is vital in applications ...
share
research
∙
05/02/2023
Single-model uncertainty quantification in neural network potentials does not consistently outperform model ensembles
Neural networks (NNs) often assign high confidence to their predictions,...
research
∙
03/14/2023
Automated patent extraction powers generative modeling in focused chemical spaces
Deep generative models have emerged as an exciting avenue for inverse mo...
research
∙
03/02/2023
Chemically Transferable Generative Backmapping of Coarse-Grained Proteins
Coarse-graining (CG) accelerates molecular simulations of protein dynami...
research
∙
01/09/2023
Differentiable Simulations for Enhanced Sampling of Rare Events
We develop a novel approach to enhanced sampling of chemically reactive ...
research
∙
09/16/2022
Learning Pair Potentials using Differentiable Simulations
Learning pair interactions from experimental or simulation data is of gr...
research
∙
07/23/2022
Thermal half-lives of azobenzene derivatives: virtual screening based on intersystem crossing using a machine learning potential
Molecular photoswitches are the foundation of light-activated drugs. A k...
research
∙
08/10/2021
Excited state, non-adiabatic dynamics of large photoswitchable molecules using a chemically transferable machine learning potential
Light-induced chemical processes are ubiquitous in nature and have wides...
research
∙
05/15/2021
An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming
Predicting molecular conformations (or 3D structures) from molecular gra...
research
∙
03/03/2021
Chemistry-informed Macromolecule Graph Representation for Similarity Computation and Supervised Learning
Macromolecules are large, complex molecules composed of covalently bonde...
research
∙
01/27/2021
Adversarial Attacks on Uncertainty Enable Active Learning for Neural Network Potentials
Neural network (NN)-based interatomic potentials provide fast prediction...
research
∙
01/13/2021
Accelerating the screening of amorphous polymer electrolytes by learning to reduce random and systematic errors in molecular dynamics simulations
Machine learning has been widely adopted to accelerate the screening of ...
research
∙
12/15/2020
Molecular machine learning with conformer ensembles
Virtual screening can accelerate drug discovery by identifying top candi...
research
∙
06/09/2020
GEOM: Energy-annotated molecular conformations for property prediction and molecular generation
Machine learning outperforms traditional approaches in many molecular de...
research
∙
02/27/2020
Differentiable Molecular Simulations for Control and Learning
Molecular dynamics simulations use statistical mechanics at the atomisti...
research
∙
07/02/2019
Generative Models for Automatic Chemical Design
Materials discovery is decisive for tackling urgent challenges related t...
research
∙
12/06/2018
Variational Coarse-Graining for Molecular Dynamics
Molecular dynamics simulations provide theoretical insight into the micr...
research
∙
09/30/2015