collaborators

8 papers

cond-mat.mtrl-sci2026

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…

cond-mat.mtrl-sci2026

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…

cs.LG2026

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…

cond-mat.stat-mech2026

Estimating Solvation Free Energies with Boltzmann Generators

Maximilian Schebek, Nikolas M. Froböse, Bettina G. Keller +1

Accurate calculations of solvation free energies remain a central challenge in molecular simulations, often requiring extensive sampling and numerous alchemical intermediates to en…

physics.comp-ph2026

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…

cond-mat.stat-mech2026

Assessing generative modeling approaches for free energy estimates in condensed matter

Maximilian Schebek, Jiajun He, Emil Hoffmann +3

The accurate estimation of free energy differences between two states is a long-standing challenge in molecular simulations. Traditional approaches generally rely on sampling multi…