activity
20192026
most citedMachine-Learning X-ray Absorption Spectra to Quantitative Accuracy

119 citations · 380 across the 26 of their papers we have counts for

collaborators
Showing 2023Show all

10 papers · 1 filter

cond-mat.mtrl-sci2023★ 1 cited

Spectroscopy-Guided Discovery of Three-Dimensional Structures of Disordered Materials with Diffusion Models

Hyuna Kwon, Tim Hsu, Wenyu Sun +8

The ability to rapidly develop materials with desired properties has a transformative impact on a broad range of emerging technologies. In this work, we introduce a new framework b…

cond-mat.mtrl-sci2023

Atomic Insights into the Oxidative Degradation Mechanisms of Sulfide Solid Electrolytes

Chuntian Cao, Matthew R. Carbone, Cem Komurcuoglu +15

Electrochemical degradation of solid electrolytes is a major roadblock in the development of solid-state batteries, and the formed solid-solid interphase (SSI) plays a key role in…

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★ 1 cited

Emulating Expert Insight: A Robust Strategy for Optimal Experimental Design

Matthew R. Carbone, Hyeong Jin Kim, Chandima Fernando +7

The challenge of optimal design of experiments (DOE) pervades materials science, physics, chemistry, and biology. Bayesian optimization has been used to address this challenge in v…

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.stat-mech2023★ 2 cited

Physically interpretable approximations of many-body spectral functions

Shubhang Goswami, Kipton Barros, Matthew R. Carbone

The rational function approximation provides a natural and interpretable representation of response functions such as the many-body spectral functions. We apply the Vector Fitting…

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★ 2 cited

Self-driving Multimodal Studies at User Facilities

Phillip M. Maffettone, Daniel B. Allan, Stuart I. Campbell +11

Multimodal characterization is commonly required for understanding materials. User facilities possess the infrastructure to perform these measurements, albeit in serial over days t…

cond-mat.str-el2023★ 5 cited

Machine-learning the spectral function of a hole in a quantum antiferromagnet

Jackson Lee, Matthew R. Carbone, Weiguo Yin

Understanding charge motion in a background of interacting quantum spins is a fundamental problem in quantum many-body physics. The most extensively studied model for this problem…

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…