SearcharxivSearch

arXiv subjects

Disha Sanwal

Publications and source records attributed to Disha Sanwal.

2 recordsLinked to original sources

Dynamic Ensembles of Phosphine-Stabilized Gold Nanoclusters

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.

cond-mat.mtrl-sci

Generative artificial intelligence for computational chemistry: a roadmap to predicting emergent phenomena

The recent surge in Generative Artificial Intelligence (AI) has introduced exciting possibilities for computational chemistry. Generative AI methods have made significant progress in sampling molecular structures across chemical species, developing force fields, and speeding up simulations. This Perspective offers a structured overview, beginning with the fundamental theoretical concepts in both Generative AI and computational chemistry. It then covers widely used Generative AI methods, including autoencoders, generative adversarial networks, reinforcement learning, flow models and language models, and highlights their selected applications in diverse areas including force field development, and protein/RNA structure prediction. A key focus is on the challenges these methods face before they become truly predictive, particularly in predicting emergent chemical phenomena. We believe that the ultimate goal of a simulation method or theory is to predict phenomena not seen before, and that Generative AI should be subject to these same standards before it is deemed useful for chemistry. We suggest that to overcome these challenges, future AI models need to integrate core chemical principles, especially from statistical mechanics.

cond-mat.stat-mech