10 citations · 11 across the 9 of their papers we have counts for
10 papers
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…
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…
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…
Scalable Boltzmann Generators for equilibrium sampling of large-scale materials
Maximilian Schebek, Frank Noé, Jutta Rogal
The use of generative models to sample equilibrium distributions of many-body systems, as first demonstrated by Boltzmann Generators, has attracted substantial interest due to thei…
Efficient mapping of phase diagrams with conditional Boltzmann Generators
Maximilian Schebek, Michele Invernizzi, Frank Noé +1
The accurate prediction of phase diagrams is of central importance for both the fundamental understanding of materials as well as for technological applications in material science…