Dynamics on Multiple Potential Energy Surfaces: Quantitative Studies of Elementary Processes Relevant to Hypersonics
arXiv:2002.05087 · doi:10.1021/acs.jpca.0c01870
Abstract
The determination of thermal and vibrational relaxation rates of triatomic systems suitable for application in hypersonic model calculations is discussed. For this, potential energy surfaces for ground and electronically excited state species need to be computed and represented with high accuracy and quasiclassical or quantum nuclear dynamics simulations provide the basis for determining the relevant rates. These include thermal reaction rates, state-to-state cross-sections, or vibrational relaxation rates. For exemplary systems - [NNO], [NOO], and [CNO] - all individual steps are described and a literature overview for them is provided. Finally, as some of these quantities involve considerable computational expense, for the example of state-to-state cross sections the construction of an efficient model based on neural networks is discussed. All such data is required and being used in more coarse-grained computational fluid dynamics simulations.
arXiv commentReview article, 46 pages, 8 figures
References in corpus (9)
- Kernel methods in machine learning
- PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments and Partial Charges
- MOLSCAT: a program for non-reactive quantum scattering calculations on atomic and molecular collisions
- Landau-Zener type surface hopping algorithms
- Bayesian optimization for the inverse scattering problem in quantum reaction dynamics
- The Reaction: Thermal and Vibrational Relaxation Rates for the A, A and A States
- Near dissociation states for H-He on MRCI and FCI potential energy surfaces
- Formation and Stabilization of Ground and Excited State Singlet O upon Recombination of P Oxygen on Amorphous Solid Water
- Coupling of state-resolved rovibrational coarse-grain model for nitrogen to stochastic particle method for simulating internal energy excitation and dissociation
Cited by in corpus (8)
- Neural Network Potentials for Chemistry: Concepts, Applications and Prospects
- Reconciling Experimental and Theoretical Vibrational Deactivation in Low-Energy O+N Collisions
- Machine Learning Product State Distributions from Initial Reactant States for a Reactive Atom-Diatom Collision System
- Machine Learning for Observables: Reactant to Product State Distributions for Atom-Diatom Collisions
- Quantum and Quasi-classical Dynamics of the C(P) + O() CO()+ O(D) Reaction on Its Electronic Ground State
- Transformative Applications of Machine Learning for Chemical Reactions
- Transfer Learned Potential Energy Surfaces: Accurate Anharmonic Vibrational Dynamics and Dissociation Energies for the Formic Acid Monomer and Dimer
- Quantitative Molecular Simulations