124 citations · 225 across the 9 of their papers we have counts for
9 papers
Spatiotemporal Mapping of Anisotropic Thermal Transport in GaN Thin Films via Ultrafast X-ray Diffraction
Thanh Nguyen, Chuliang Fu, Mouyang Cheng +17
Efficient thermal management is essential for the reliability of modern power electronics, where increasing device density leads to severe heat dissipation challenges. However, in…
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…
Machine learning enabled experimental design and parameter estimation for ultrafast spin dynamics
Zhantao Chen, Cheng Peng, Alexander N. Petsch +5
Advanced experimental measurements are crucial for driving theoretical developments and unveiling novel phenomena in condensed matter and material physics, which often suffer from…
Capturing dynamical correlations using implicit neural representations
Sathya Chitturi, Zhurun Ji, Alexander Petsch +16
The observation and description of collective excitations in solids is a fundamental issue when seeking to understand the physics of a many-body system. Analysis of these excitatio…
Panoramic mapping of phonon transport from ultrafast electron diffraction and machine learning
Zhantao Chen, Xiaozhe Shen, Nina Andrejevic +11
One central challenge in understanding phonon thermal transport is a lack of experimental tools to investigate mode-based transport information. Although recent advances in computa…
Elucidating proximity magnetism through polarized neutron reflectometry and machine learning
Nina Andrejevic, Zhantao Chen, Thanh Nguyen +7
Polarized neutron reflectometry is a powerful technique to interrogate the structures of multilayered magnetic materials with depth sensitivity and nanometer resolution. However, r…
Fluctuation-driven, topology-stabilized order in a correlated nodal semimetal
Nathan C. Drucker, Thanh Nguyen, Fei Han +19
The interplay between strong electron correlation and band topology is at the forefront of condensed matter research. As a direct consequence of correlation, magnetism enriches top…
Machine Learning on Neutron and X-Ray Scattering
Zhantao Chen, Nina Andrejevic, Nathan Drucker +8
Neutron and X-ray scattering represent two state-of-the-art materials characterization techniques that measure materials' structural and dynamical properties with high precision. T…
Direct prediction of phonon density of states with Euclidean neural networks
Zhantao Chen, Nina Andrejevic, Tess Smidt +6
Machine learning has demonstrated great power in materials design, discovery, and property prediction. However, despite the success of machine learning in predicting discrete prope…