729 citations · 1.3k across the 19 of their papers we have counts for
5 papers · 1 filter
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…
Automated Machine Learning Pipeline: Large Language Models-Assisted Automated Dataset Generation for Training Machine-Learned Interatomic Potentials
Adam Lahouari, Jutta Rogal, Mark E. Tuckerman
Machine learning interatomic potentials (MLIPs) have become powerful tools to extend molecular simulations beyond the limits of quantum methods, offering near-quantum accuracy at m…
Machine learning classification of local environments in molecular crystals
Daisuke Kuroshima, Michael Kilgour, Mark E. Tuckerman +1
Identifying local structural motifs and packing patterns of molecular solids is a challenging task for both simulation and experiment. We demonstrate two novel approaches to charac…
Geometric Deep Learning for Molecular Crystal Structure Prediction
Michael Kilgour, Jutta Rogal, Mark Tuckerman
We develop and test new machine learning strategies for accelerating molecular crystal structure ranking and crystal property prediction using tools from geometric deep learning on…
Neural network based path collective variables for enhanced sampling of phase transformations
Jutta Rogal, Elia Schneider, Mark E. Tuckerman
We propose a rigorous construction of a 1D path collective variable to sample structural phase transformations in condensed matter. The path collective variable is defined in a spa…