2 citations · 3 across the 11 of their papers we have counts for
13 papers
Machine learning magnetic interactions from neutron powder diffraction data
Adit S. Desai, Yongqiang Cheng, Joseph A. M. Paddison
Neutron diffraction is a versatile experimental technique capable of probing a material's magnetic properties. While diffraction is typically used to determine the magnetic structu…
Posterior Inference of Hamiltonian Parameters from RIXS Spectroscopy
Samuel Klein, Thomas M. Linker, Louis Conreux +14
We present the first application of simulation-based inference to resonant inelastic X-ray scattering spectroscopy. Using truncated marginal neural ratio estimation to efficiently…
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…
A Foundation Model for Non-Destructive Defect Identification from Vibrational Spectra
Mouyang Cheng, Chu-Liang Fu, Bowen Yu +5
Defects are ubiquitous in solids and strongly influence materials' mechanical and functional properties. However, non-destructive characterization and quantification of defects, es…
AI-Driven Defect Engineering for Advanced Thermoelectric Materials
Chu-Liang Fu, Mouyang Cheng, Nguyen Tuan Hung +7
Thermoelectric materials offer a promising pathway to directly convert waste heat to electricity. However, achieving high performance remains challenging due to intrinsic trade-off…
Real-time interpretation of neutron vibrational spectra with symmetry-equivariant Hessian matrix prediction
Bowen Han, Pei Zhang, Kshitij Mehta +3
The vibrational behavior of molecules serves as a crucial fingerprint of their structure, chemical state, and surrounding environment. Neutron vibrational spectroscopy provides com…
Structural Constraint Integration in Generative Model for Discovery of Quantum Material Candidates
Ryotaro Okabe, Mouyang Cheng, Abhijatmedhi Chotrattanapituk +9
Billions of organic molecules are known, but only a tiny fraction of the functional inorganic materials have been discovered, a particularly relevant problem to the community searc…
Virtual Node Graph Neural Network for Full Phonon Prediction
Ryotaro Okabe, Abhijatmedhi Chotrattanapituk, Artittaya Boonkird +10
The structure-property relationship plays a central role in materials science. Understanding the structure-property relationship in solid-state materials is crucial for structure d…