Experiment-Driven Atomistic Materials Modeling: A Case Study Combining X-Ray Photoelectron Spectroscopy and Machine Learning Potentials to Infer the Structure of Oxygen-Rich Amorphous Carbon-Reference-Cited by-同舟云学术

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Experiment-Driven Atomistic Materials Modeling: A Case Study Combining X-Ray Photoelectron Spectroscopy and Machine Learning Potentials to Infer the Structure of Oxygen-Rich Amorphous Carbon

Published:2024-05-15 Issue:21 Volume:146 Page:14645-14659
ISSN:0002-7863
Container-title:Journal of the American Chemical Society
language:en
Short-container-title:J. Am. Chem. Soc.

Author:

Zarrouk Tigany¹,Ibragimova Rina¹^ORCID,Bartók Albert P.²³^ORCID,Caro Miguel A.¹^ORCID

Affiliation:

1. Department of Chemistry and Materials Science, Aalto University, Espoo 02150, Finland

2. Department of Physics, University of Warwick, Coventry CV4 7AL, U.K.

3. Warwick Centre for Predictive Modelling, School of Engineering, University of Warwick, Coventry CV4 7AL, U.K.

Funder

Engineering and Physical Sciences Research Council

Research Council of Finland

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/jacs.4c01897

Reference90 articles.

1. Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces

2. First Principles Neural Network Potentials for Reactive Simulations of Large Molecular and Condensed Systems

3. Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons

4. Gaussian Process Regression for Materials and Molecules

5. Machine learning for molecular and materials science

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