Showing 1–20 of 70 results
/ Date/ Name
Jul 2, 2019Generative Models for Automatic Chemical DesignDec 15, 2020Molecular machine learning with conformer ensemblesJul 11, 2021Sampling Lattices in Semi-Grand Canonical Ensemble with Autoregressive Machine LearningJul 4, 2023Learning a reactive potential for silica-water through uncertainty attributionFeb 22, 2023Mapping the space of photoswitchable ligands and photodruggable proteins with computational modelingJan 13, 2021Accelerating amorphous polymer electrolyte screening by learning to reduce errors in molecular dynamics simulated propertiesSep 20, 2024Learning Ordering in Crystalline Materials with Symmetry-Aware Graph Neural NetworksMar 16, 2026Scaling Autoregressive Models for Lattice ThermodynamicsJun 9, 2020GEOM: Energy-annotated molecular conformations for property prediction and molecular generationJan 27, 2021Differentiable sampling of molecular geometries with uncertainty-based adversarial attacksNov 14, 2021Graph theory-based structural analysis on density anomaly of silica glassMay 2, 2023Data-Driven, Physics-Informed Descriptors of Cation Ordering in Multicomponent OxidesFeb 6, 2024Enhanced sampling of robust molecular datasets with uncertainty-based collective variablesMay 15, 2021An End-to-End Framework for Molecular Conformation Generation via Bilevel ProgrammingOct 2, 2024Flow Matching for Accelerated Simulation of Atomic Transport in Crystalline MaterialsJan 9, 2023Differentiable Simulations for Enhanced Sampling of Rare EventsDec 6, 2018Coarse-Graining Auto-Encoders for Molecular DynamicsAug 10, 2021Excited state, non-adiabatic dynamics of large photoswitchable molecules using a chemically transferable machine learning potentialMar 3, 2021GLAMOUR: Graph Learning over Macromolecule RepresentationsFeb 2, 2024Learning Collective Variables with Synthetic Data Augmentation through Physics-Inspired Geodesic Interpolation