Machine Learning for Observables: Reactant to Product State Distributions for Atom–Diatom Collisions-Reference-Cited by-同舟云学术

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Machine Learning for Observables: Reactant to Product State Distributions for Atom–Diatom Collisions

Published:2020-07-23 Issue:35 Volume:124 Page:7177-7190
ISSN:1089-5639
Container-title:The Journal of Physical Chemistry A
language:en
Short-container-title:J. Phys. Chem. A

Author:

Arnold Julian¹^ORCID,Koner Debasish¹^ORCID,Käser Silvan¹^ORCID,Singh Narendra²,Bemish Raymond J.³^ORCID,Meuwly Markus¹^ORCID

Affiliation:

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

2. Department of Mechanical Engineering, Stanford University Stanford, California 94305, United States

3. Air Force Research Laboratory, Space Vehicles Directorate, Kirtland AFB, New Mexico 87117, United States

Funder

Schweizerischer Nationalfonds zur F?rderung der Wissenschaftlichen Forschung

Universit?t Basel

NCCRR MUST

Publisher

American Chemical Society (ACS)

Subject

Physical and Theoretical Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpca.0c05173

Reference58 articles.

1. Review of chemical-kinetic problems of future NASA missions. I - Earth entries

2. Fifty years of hypersonics: where we've been, where we're going

3. Dynamics on Multiple Potential Energy Surfaces: Quantitative Studies of Elementary Processes Relevant to Hypersonics

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

5. Direct molecular simulation of internal energy relaxation and dissociation in oxygen

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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. Neural network potentials for chemistry: concepts, applications and prospects;Digital Discovery;2023

4. Sensitivity Analysis and Uncertainty Quantification for a Three-Dimensional Ionized Hypersonic Flow;2023

5. Combining Machine Learning and Spectroscopy to Model Reactive Atom + Diatom Collisions;The Journal of Physical Chemistry A;2022-10-19

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