activity
20182026
most citedSingle-model uncertainty quantification in neural network potentials does not consistently outperform model ensembles

94 citations · 190 across the 20 of their papers we have counts for

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10 papers · 1 filter

cond-mat.mtrl-sci2026

XRDiff: Crystal Structure Prediction from Powder X-Ray Diffraction Data Using Diffusion Models

Nofit Segal, Mingda Li, Benjamin Kurt Miller +1

Determining the crystal structure of a material from its powder X-ray diffraction (PXRD) pattern is a central challenge in materials science. PXRD is an accessible and widely used…

cond-mat.mtrl-sci2025

High-Throughput Transition-State Searches in Zeolite Nanopores

Pau Ferri-Vicedo, Alexander J. Hoffman, Avni Singhal +1

Zeolites are important for industrial catalytic processes involving organic molecules. Understanding molecular reaction mechanisms within the confined nanoporous environment can gu…

cond-mat.mtrl-sci2025

Accelerating and enhancing thermodynamic simulations of electrochemical interfaces

Xiaochen Du, Mengren Liu, Jiayu Peng +6

Electrochemical interfaces are crucial in catalysis, energy storage, and corrosion, where their stability and reactivity depend on complex interactions between the electrode, adsor…

cond-mat.mtrl-sci2024

Efficient Generation of Molecular Clusters with Dual-Scale Equivariant Flow Matching

Akshay Subramanian, Shuhui Qu, Cheol Woo Park +3

Amorphous molecular solids offer a promising alternative to inorganic semiconductors, owing to their mechanical flexibility and solution processability. The packing structure of th…

cond-mat.mtrl-sci2024★ 6 cited

Flow Matching for Accelerated Simulation of Atomic Transport in Crystalline Materials

Juno Nam, Sulin Liu, Gavin Winter +3

Atomic transport underpins the performance of materials in technologies such as energy storage and electronics, yet its simulation remains computationally demanding. In particular,…

cond-mat.mtrl-sci2024★ 2 cited

Learning Ordering in Crystalline Materials with Symmetry-Aware Graph Neural Networks

Jiayu Peng, James Damewood, Jessica Karaguesian +2

Graph convolutional neural networks (GCNNs) have become a machine learning workhorse for screening the chemical space of crystalline materials in fields such as catalysis and energ…