Diabatic potential energy surfaces and semiclassical multi-state dynamics for fourteen coupled 3 A′ states of O3

Zoltan Varga, Yinan Shu, Jiaxin Ning, Donald G. Truhlar

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Dissociation and energy transfer in high-energy collisions of O2 play important roles in simulating thermal energy content and heat flux in flows around hypersonic vehicles. Furthermore, atomic oxygen reactions on the vehicle surface are an important contributor to heat shield erosion. Molecular dynamics modeling is needed to better understand the relevant rate processes. Because it is necessary to model the gas flows in high-temperature shock waves, electronically excited states of O2 and O can be populated, and molecular dynamics simulations should include collisions of electronically excited species and electronically nonadiabatic collisions. This requires potential energy surfaces and state couplings for many energetically accessible electronic states. Here we report a systematic strategy to calculate such surfaces and couplings. We have applied this method to the fourteen lowest-energy potential energy surfaces in the 3 A′ manifold of O3, and we report a neural-network fit to diabatic potential energy matrix (DPEM). We illustrate the use of the resulting DPEM by carrying out semiclassical dynamics calculations of cross sections for excitation of O2 in 3 A′ collisions with O at two collision energies; these dynamics calculations are carried out by the curvature-driven coherent switching with decay of mixing method.

Original languageEnglish (US)
Article number047002
JournalElectronic Structure
Issue number4
StatePublished - Dec 1 2022

Bibliographical note

Funding Information:
This work was supported in part by the Air Force Office of Scientific Research by Grant FA9550-19-1-0219.

Publisher Copyright:
© 2022 The Author(s). Published by IOP Publishing Ltd.


  • adiabatic
  • coherent switching with decay of mixing
  • diabatic
  • dynamics
  • multi-state
  • neural network
  • potential energy surfaces and couplings


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