119 citations · 380 across the 26 of their papers we have counts for
12 papers · 1 filter
OmniXAS: A Universal Deep-Learning Framework for Materials X-ray Absorption Spectra
Shubha R. Kharel, Fanchen Meng, Xiaohui Qu +2
X-ray absorption spectroscopy (XAS) is a powerful characterization technique for probing the local chemical environment of absorbing atoms. However, analyzing XAS data presents sig…
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…
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…
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…
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…
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…
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…
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…
Lightshow: a Python package for generating computational x-ray absorption spectroscopy input files
Matthew R. Carbone, Fanchen Meng, Christian Vorwerk +7
First-principles computational spectroscopy is a critical tool for interpreting experiment, performing structure refinement, and developing new physical understanding. Systematical…
Uncertainty-aware predictions of molecular X-ray absorption spectra using neural network ensembles
Animesh Ghose, Mikhail Segal, Fanchen Meng +7
As machine learning (ML) methods continue to be applied to a broad scope of problems in the physical sciences, uncertainty quantification is becoming correspondingly more important…
Machine-Learning X-ray Absorption Spectra to Quantitative Accuracy
Matthew R. Carbone, Mehmet Topsakal, Deyu Lu +1
The advent of massive data repositories has propelled machine learning techniques to the front lines of many scientific fields, and exploring new frontiers by leveraging the predic…
Classification of Local Chemical Environments from X-ray Absorption Spectra using Supervised Machine Learning
Matthew R. Carbone, Shinjae Yoo, Mehmet Topsakal +1
X-ray absorption spectroscopy is a premier element-specific technique for materials characterization. Specifically, the x-ray absorption near-edge structure (XANES) encodes importa…