Unsupervised machine learning for unbiased chemical classification in X-ray absorption spectroscopy and X-ray emission spectroscopy-Reference-Cited by-同舟云学术

Unsupervised machine learning for unbiased chemical classification in X-ray absorption spectroscopy and X-ray emission spectroscopy

Published:2021 Issue:41 Volume:23 Page:23586-23601
ISSN:1463-9076
Container-title:Physical Chemistry Chemical Physics
language:en
Short-container-title:Phys. Chem. Chem. Phys.

Author:

Tetef Samantha¹^ORCID,Govind Niranjan²^ORCID,Seidler Gerald T.¹^ORCID

Affiliation:

1. Department of Physics, University of Washington, Seattle, WA 98195, USA

2. Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA 99352, USA

Abstract

We utilize unsupervised machine learning to extract chemically relevant information in X-ray absorption near-edge structure (XANES) and in valence-to-core X-ray emission spectra (VtC-XES) for classification of an ensemble of sulphorganic molecules.

Funder

Basic Energy Sciences

Division of Graduate Education

U.S. Department of Energy

Division of Chemistry

Publisher

Royal Society of Chemistry (RSC)

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

http://pubs.rsc.org/en/content/articlepdf/2021/CP/D1CP02903G

Reference101 articles.

1. Data science: Accelerating innovation and discovery in chemical engineering

2. Data Science in Chemical Engineering: Applications to Molecular Science

3. Commentary: The Materials Project: A materials genome approach to accelerating materials innovation

4. The inorganic crystal structure data base

5. New developments in the Inorganic Crystal Structure Database (ICSD): accessibility in support of materials research and design

Cited by 29 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Real-time tracking of structural evolution in 2D MXenes using theory-enhanced machine learning;Scientific Reports;2024-08-02

2. Emerging Atomistic Modeling Methods for Heterogeneous Electrocatalysis;Chemical Reviews;2024-07-11

3. Machine learning for analyses and automation of structural characterization of polymer materials;Progress in Polymer Science;2024-06

4. Machine-learning strategies for the accurate and efficient analysis of x-ray spectroscopy;Machine Learning: Science and Technology;2024-06-01

5. Robust Machine Learning Inference from X-ray Absorption Near Edge Spectra through Featurization;Chemistry of Materials;2024-03-01