124 citations · 371 across the 17 of their papers we have counts for
18 papers
Strategies for Deploying AI Agents in Production at Scientific User Facilities
Ming Du, Xiangyu Yin, Michael Prince +40
Agentic artificial intelligence (AI) is moving beyond research demonstrations toward production use at scientific user facilities, including light sources, neutron sources, nanosci…
An End-to-End Differentiable Forward Model for High-Energy Diffraction Microscopy
Hemant Sharma, Nina Andrejevic, Simon Zhang +1
High-Energy Diffraction Microscopy (HEDM) recovers crystallographic orientation, strain, and grain position from rotating-crystal X-ray diffraction patterns. Existing forward model…
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…
AlphaDiffract: Automated Crystallographic Analysis of Powder X-ray Diffraction Data
Nina Andrejevic, Ming Du, Hemant Sharma +6
Materials identification and structural understanding from powder X-ray diffraction (PXRD) data is a long-standing challenge in materials science, fundamental to discovering and ch…
Data-driven discovery of dynamics from time-resolved coherent scattering
Nina Andrejevic, Tao Zhou, Qingteng Zhang +3
Coherent X-ray scattering (CXS) techniques are capable of interrogating dynamics of nano- to mesoscale materials systems at time scales spanning several orders of magnitude. Howeve…
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…
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…
Topological Signatures in Nodal Semimetals through Neutron Scattering
Thanh Nguyen, Yoichiro Tsurimaki, Ricardo Pablo-Pedro +5
Topological nodal semimetals are known to host a variety of fascinating electronic properties due to the topological protection of the band-touching nodes. Neutron scattering, desp…
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…
Signature of Many-Body Localization of Phonons in Strongly Disordered Superlattices
Thanh Nguyen, Nina Andrejevic, Hoi Chun Po +17
Many-body localization (MBL) has attracted significant attention due to its immunity to thermalization, role in logarithmic entanglement entropy growth, and opportunities to reach…
Machine learning spectral indicators of topology
Nina Andrejevic, Jovana Andrejevic, B. Andrei Bernevig +7
Topological materials discovery has emerged as an important frontier in condensed matter physics. While theoretical classification frameworks have been used to identify thousands o…
Spectroscopic Signatures of Nonlocal Interfacial Coupling in Superconducting FeSe/SrTiO3 Heterostructures
Nina Andrejevic, Fei Han, Thanh Nguyen +10
The mechanism of enhanced superconductivity in the one unit-cell (1UC) FeSe film on a SrTiO3 (STO) substrate has stimulated significant research interest but remains elusive. Using…
Quantized Thermoelectric Hall Effect Induces Giant Power Factor in a Topological Semimetal
Fei Han, Nina Andrejevic, Thanh Nguyen +14
Thermoelectrics are promising by directly generating electricity from waste heat. However, (sub-)room-temperature thermoelectrics have been a long-standing challenge due to vanishi…
Phonon Quantum Phase Transition
Ricardo Pablo-Pedro, Nina Andrejevic, Yoichiro Tsurimaki +5
We show that a quantum phase transition can occur in a phonon system in the presence of dislocations. Due to the competing nature between the topological protection of the dislocat…
Theory of Electron-Phonon-Dislon Interacting System - Toward a Quantized Theory of Dislocations
Mingda Li, Yoichiro Tsurimaki, Qingping Meng +4
We provide a comprehensive theoretical framework to study how crystal dislocations influence the functional properties of materials, based on the idea of quantized dislocation, nam…