10 citations · 11 across the 9 of their papers we have counts for
6 papers · 1 filter
Dynamic Ensembles of Phosphine-Stabilized Gold Nanoclusters
Caitlin A. McCandler, Disha Sanwal, Jutta Rogal
Atomically precise phosphine-stabilized gold nanoclusters are commonly characterized by single-crystal X-ray diffraction, yet the extent to which these static structures represent…
JANUS: A Multi-modal Foundation Neural Sampler for Disordered Materials
Denis Blessing, Mouyang Cheng, Maximilian Schebek +4
Disordered materials are central to modern technologies, from high-performance alloys to quantum devices, yet understanding and designing them requires exploring an enormous space…
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…
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…
Boltzmann Generators for Condensed Matter via Riemannian Flow Matching
Emil Hoffmann, Maximilian Schebek, Leon Klein +2
Sampling equilibrium distributions is fundamental to statistical mechanics. While flow matching has emerged as scalable state-of-the-art paradigm for generative modeling, its poten…
Markov State Models for Tracking Reaction Dynamics on Catalytic Nanoparticles
Caitlin A. McCandler, Chatipat Lorpaiboon, Timothy C. Berkelbach +1
Markov state models (MSMs) are a powerful tool to analyze and coarse-grain complex dynamical data into interpretable kinetic processes. This capability is particularly important in…