729 citations · 1.3k across the 19 of their papers we have counts for
21 papers
Scalable Simulation of Quantum Dynamics on Topological Quantum Hardware
Kritanjan Polley, Mark E. Tuckerman
Quantum computers offer a significant advantage in simulating quantum systems compared to classical computers for certain problems, although most current applications are limited t…
Integrated Alchemical and Conformational Enhanced Sampling for Solvation Free Energy Calculations
Gabriela B. Correa, Charlles R. A. Abreu, Nisanth N. Nair +1
Accurate solvation free energies from molecular dynamics simulations require efficient sampling of coupled slow variables, including solvent coordinates, solute conformational mode…
A Distributional Framework for Generative Modeling of Molecular Crystals
Michael Kilgour, Alex Dong, Mark E. Tuckerman +1
Molecular crystals are a highly polymorphic class of materials, with a single molecule commonly crystallizing via multiple packing patterns, making structure and property predictio…
Enhancing molecular dynamics with equivariant machine-learned densities
Mihail Bogojeski, Muhammad R. Hasyim, Leslie Vogt-Maranto +3
Machine-learning interatomic potentials (MLIPs) have enabled molecular dynamics at near ab initio accuracy, yet remain limited to energies and forces by construction, leaving elect…
MolCryst-MLIPs: A Machine-Learned Interatomic Potentials Database for Molecular Crystals
Adam Lahouari, Shen Ai, Jihye Han +16
We present an open Molecular Crystal (MC) database of Machine-Learned Interatomic Potentials (MLIP) called MolCryst-MLIPs. The first release comprises fine-tuned MACE models for ni…
MXtalTools: A Toolkit for Machine Learning on Molecular Crystals
Michael Kilgour, Mark E. Tuckerman, Jutta Rogal
We present MXtalTools, a flexible Python package for the data-driven modelling of molecular crystals, facilitating machine learning studies of the molecular solid state. MXtalTools…