Atomically precise phosphine-stabilized gold nanoclusters are commonly characterized by single-crystal X-ray diffraction, yet the extent to which these static structures represent finite-temperature behavior remains unclear. To explore the free-energy landscapes, equilibrium populations, and isomerization kinetics of these nanoclusters in the gas phase, we establish a general framework that combines molecular dynamics simulations based on a machine-learned interatomic potential with Markov state models (MSMs). Analysis of the MSMs indicates that experimentally reported crystal structures frequently correspond to minor metastable states or transient configurations rather than the dominant finite-temperature structures. Increasing ligand coverage systematically alters both the thermodynamics and kinetics of structural rearrangements, driving the transition from planar to three-dimensional gold cores while accelerating isomerization dynamics. Moreover, catalytically accessible geometries are often only minor members of the equilibrium ensemble, highlighting a trade-off between structural stability and surface accessibility. These results emphasize that ligand-protected nanoclusters need to be viewed as dynamic ensembles and their finite-temperature behavior cannot be fully captured by their corresponding crystallographic structures alone.
Caitlin A. McCandler, D. Sanwal, Jutta Rogal· 0 citations
Molecular crystals are a highly polymorphic class of materials, with a single molecule commonly crystallizing via multiple packing patterns, making structure and property prediction very challenging. Crystal structure prediction typically comprises the production of sets of promising candidate structures, each considered in isolation rather than as samples in a thermodynamic distribution. Likewise, modern generative approaches to this problem, despite naturally sampling distributions of crystals, lack a concrete formulation of the distributions being sampled. Two components are required to impart meaning to the distributions of crystals generated under such models: a canonical parameterization, and a loss function which equilibrates the generated samples to some target distribution. We develop such a parameterization, and train energy-based generative flow networks (GFlowNets) to approximate the Boltzmann distribution over crystal structures for target molecules and space groups. Combined, these components comprise our MXtalGFlow framework for molecular crystal modeling. Going beyond sampling disconnected sets of low-energy structures, MXtalGFlow yields a thermodynamic distribution over crystal structures. We sample and analyze distributions of crystals for two molecules, each under two energy functions, a Lennard-Jones potential and the Universal Model for Atoms. We characterize the local structural basins about the known polymorphs, and identify additional as-yet un-reported packing modes with competitive probabilities to the known experimental structures. With MXtalGFlow, we illustrate how to define and train a model to sample a thermodynamically meaningful distribution of molecular crystals, and analyze such a distribution to glean useful information.
Michael Kilgour, A. Dong, M. Tuckerman et al.· 0 citations
JANUS is a multimodal neural sampler that couples continuous and masked discrete diffusion through an equivariant graph neural network trained directly from energy evaluations, without pre-generated equilibrium data, providing a foundation for thermodynamic sampling, characterization and inverse design of chemically disordered materials.
Denis Blessing, Mouyang Cheng, M. Schebek et al.· 0 citations
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