activity
20022025
most citedMachine-Learning X-ray Absorption Spectra to Quantitative Accuracy

119 citations · 463 across the 22 of their papers we have counts for

collaborators

22 papers

cond-mat.supr-con2025

Two-dimensional helical superconductivity and gapless superconducting edge modes in the 1T-WS/2H-WS heterophase bilayer

Xuance Jiang, Jennifer Cano, Yuan Ping +2

We propose a material platform comprised of transition metal dichalcogenide (TMDC) heterostructures to realize the two-dimensional (2D) helical superconductivity with an intrinsic…

q-bio.BM2023

Transferable Graph Neural Fingerprint Models for Quick Response to Future Bio-Threats

Wei Chen, Yihui Ren, Ai Kagawa +9

Fast screening of drug molecules based on the ligand binding affinity is an important step in the drug discovery pipeline. Graph neural fingerprint is a promising method for develo…

cond-mat.mtrl-sci2023

Accurate, uncertainty-aware classification of molecular chemical motifs from multi-modal X-ray absorption spectroscopy

Matthew R. Carbone, Phillip M. Maffettone, Xiaohui Qu +2

Accurate classification of molecular chemical motifs from experimental measurement is an important problem in molecular physics, chemistry and biology. In this work, we present neu…

cond-mat.mtrl-sci2023★ 20 cited

Multi-code Benchmark on Simulated Ti K-edge X-ray Absorption Spectra of Ti-O Compounds

Fanchen Meng, Benedikt Maurer, Fabian Peschel +7

X-ray absorption spectroscopy (XAS) is an element-specific materials characterization technique that is sensitive to structural and electronic properties. First-principles simulate…

cond-mat.mtrl-sci2023

Simulated sulfur K-edge X-ray absorption spectroscopy database of lithium thiophosphate solid electrolytes

Haoyue Guo, Matthew R. Carbone, Chuntian Cao +9

X-ray absorption spectroscopy (XAS) is a premier technique for materials characterization, providing key information about the local chemical environment of the absorber atom. In t…

cond-mat.mtrl-sci2023★ 15 cited

Decoding Structure-Spectrum Relationships with Physically Organized Latent Spaces

Zhu Liang, Matthew R. Carbone, Wei Chen +5

A new semi-supervised machine learning method for the discovery of structure-spectrum relationships is developed and demonstrated using the specific example of interpreting X-ray a…