9 papers
Observation geometry for uncertainty-aware Hamiltonian inference and experimental design in quantum magnets
Roy Liu, Venugopal Ranganathan, David Dahlbom +12
Determining microscopic interactions from spectroscopic and scattering measurements is central to understanding quantum materials, yet it often remains unclear which interactions c…
Universal Magnetic Structure Prediction from Atomic Coordinates with Near-Experimental Accuracy
Abhijatmedhi Chotrattanapituk, Ryotaro Okabe, Eunbi Rha +6
Magnetic order is a fundamental property of materials, governing collective behavior and enabling a broad range of functionalities. Yet magnetic structure remains difficult to dete…
Automated multiphase identification and refinement in powder diffraction using mismatch-tolerant machine learning
Lalit Yadav, Yongqiang Cheng, Mathieu Doucet
Powder diffraction is a primary structural characterization tool in materials science, yet automated phase identification remains a major bottleneck for autonomous discovery. Exist…
Probing Non-Equilibrium Grain Boundary Dynamics with XPCS and Domain-Adaptive Machine Learning
Mouyang Cheng, Bowen Yu, Chu-Liang Fu +14
Grain-boundary (GB) dynamics control the stability, mechanical, and functional response of nanocrystalline materials, but direct experimental access to their slow non-equilibrium m…
Enhancing Materials Discovery with Valence Constrained Design in Generative Modeling
Mouyang Cheng, Weiliang Luo, Hao Tang +6
Diffusion-based deep generative models have emerged as powerful tools for inverse materials design. Yet, many existing approaches overlook essential chemical constraints such as ox…
Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data
Bowen Han, Yongqiang Cheng
The accurate calculation of phonons and vibrational spectra remains a significant challenge, requiring highly precise evaluations of interatomic forces. Traditional methods based o…