Combining Machine Learning and Spectroscopy to Model Reactive Atom + Diatom Collisions-Reference-Cited by-同舟云学术

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Combining Machine Learning and Spectroscopy to Model Reactive Atom + Diatom Collisions

Published:2022-10-19 Issue:43 Volume:126 Page:7971-7980
ISSN:1089-5639
Container-title:The Journal of Physical Chemistry A
language:en
Short-container-title:J. Phys. Chem. A

Author:

San Vicente Veliz Juan Carlos¹^ORCID,Arnold Julian²^ORCID,Bemish Raymond J.³^ORCID,Meuwly Markus¹^ORCID

Affiliation:

1. Department of Chemistry, University of Basel, Klingelbergstrasse 80, CH-4056Basel, Switzerland

2. Department of Physics, University of Basel, Klingelbergstrasse 82, CH-4056Basel, Switzerland

3. Air Force Research Laboratory, Space Vehicles Directorate, Kirtland AFB, Albuquerque, New Mexico87117, United States

Funder

Schweizerischer Nationalfonds zur F?rderung der Wissenschaftlichen Forschung

Air Force Office of Scientific Research

Universit?t Basel

NCCR MUST

Publisher

American Chemical Society (ACS)

Subject

Physical and Theoretical Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpca.2c06267

Reference37 articles.

1. Assessment of CFD capability for prediction of hypersonic shock interactions

2. Nonequilibrium Gas Dynamics and Molecular Simulation

3. Computation of Hypersonic Flows Using the Direct Simulation Monte Carlo Method

4. Monte Carlo modeling of nitric oxide formation based on quasi-classical trajectory calculations

Cited by 4 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. High-Energy Reaction Dynamics of N₃;The Journal of Physical Chemistry A;2024-07-25

2. State-to-state dynamics and machine learning predictions of inelastic and reactive O(3P) + CO(1∑+) collisions relevant to hypersonic flows;The Journal of Chemical Physics;2024-05-02

3. Dissociation cross sections and rates in O₂ + N collisions: molecular dynamics simulations combined with machine learning;Physical Chemistry Chemical Physics;2023

4. Efficient quasi-classical trajectory calculations by means of neural operator architectures;Physical Chemistry Chemical Physics;2023

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