Informed Chemical Classification of Organophosphorus Compounds via Unsupervised Machine Learning of X-ray Absorption Spectroscopy and X-ray Emission Spectroscopy-Reference-Cited by-同舟云学术

Informed Chemical Classification of Organophosphorus Compounds via Unsupervised Machine Learning of X-ray Absorption Spectroscopy and X-ray Emission Spectroscopy

Published:2022-07-15 Issue:29 Volume:126 Page:4862-4872
ISSN:1089-5639
Container-title:The Journal of Physical Chemistry A
language:en
Short-container-title:J. Phys. Chem. A

Author:

Tetef Samantha¹^ORCID,Kashyap Vikram¹,Holden William M.¹,Velian Alexandra²^ORCID,Govind Niranjan³^ORCID,Seidler Gerald T.¹^ORCID

Affiliation:

1. Department of Physics, University of Washington, Seattle, Washington 98195, United States

2. Department of Chemistry, University of Washington, Seattle, Washington 98195, United States

3. Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States

Funder

Division of Chemistry

Washington Space Grant Consortium

Basic Energy Sciences

Research Corporation for Science Advancement

Division of Graduate Education

Publisher

American Chemical Society (ACS)

Subject

Physical and Theoretical Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpca.2c03635

Reference78 articles.

1. Machine learning for molecular and materials science

2. Rational design of chemically complex metallic glasses by hybrid modeling guided machine learning

3. Materials discovery and design using machine learning

4. Machine learning assisted materials design and discovery for rechargeable batteries

5. Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)

Cited by 11 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. Minoxidil/salicylic acid hydrogel formulated for alopecia treatment: Supramolecular interactions modulate physicochemical properties and biological activities revealed by experimental and theoretical studies;Journal of Molecular Structure;2024-06

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

3. Atomic insights into the oxidative degradation mechanisms of sulfide solid electrolytes;Cell Reports Physical Science;2024-04

4. Accurate, Uncertainty-Aware Classification of Molecular Chemical Motifs from Multimodal X-ray Absorption Spectroscopy;The Journal of Physical Chemistry A;2024-02-28

5. Spectroscopy and Machine Learning: Revolutionizing Soil Quality Monitoring for Sustainable Resource Management;Advances in Geographical and Environmental Sciences;2024