TY - JOUR

T1 - Efficient quasi-classical trajectory calculations by means of neural operator architectures

AU - Priyadarshini, Maitreyee Sharma

AU - Venturi, Simone

AU - Zanardi, Ivan

AU - Panesi, Marco

N1 - Funding Information:
The work was supported in part by NASA's ESI grant no. 80NSSC19K0218 and in part by the Vannevar Bush Faculty Fellowship OUSD(RE) Grant No: N00014-21-1-295 with Prof. Marco Panesi as the Principal Investigator. The quasi-classical trajectory calculations are run on the Pleiades Supercomputer.
Publisher Copyright:
© 2023 The Royal Society of Chemistry.

PY - 2023/5/9

Y1 - 2023/5/9

N2 - An accurate description of non-equilibrium chemistry relies on rovibrational state-to-state (StS) kinetics data, which can be obtained through the quasi-classical trajectory (QCT) method for high-energy collisions. However, these calculations still represent one of the major computational bottlenecks in predictive simulations of non-equilibrium reacting gases. This work addresses this limitation by proposing SurQCT, a novel machine learning-based surrogate for efficiently and accurately predicting StS chemical reaction rate coefficients. The QCT emulator is constructed using three independent components: two deep operator networks (DeepONets) for inelastic and exchange processes and a feed-forward neural network (FNN) for the dissociation reactions. SurQCT is tested on the O2 + O system, showing a computational speed-up of 85%. Furthermore, we carry out a StS master equation analysis of an isochoric, isothermal heat bath simulation at various temperatures to study how the predicted rate coefficients impact the accuracy of multiple quantities of interest (QoIs) at the kinetics level (e.g., global quasi-steady state (QSS) dissociation rate coefficients and energy relaxation times). For all these QoIs, the master equation analysis relying on SurQCT data shows an accuracy within 15% across the entire temperature regime.

AB - An accurate description of non-equilibrium chemistry relies on rovibrational state-to-state (StS) kinetics data, which can be obtained through the quasi-classical trajectory (QCT) method for high-energy collisions. However, these calculations still represent one of the major computational bottlenecks in predictive simulations of non-equilibrium reacting gases. This work addresses this limitation by proposing SurQCT, a novel machine learning-based surrogate for efficiently and accurately predicting StS chemical reaction rate coefficients. The QCT emulator is constructed using three independent components: two deep operator networks (DeepONets) for inelastic and exchange processes and a feed-forward neural network (FNN) for the dissociation reactions. SurQCT is tested on the O2 + O system, showing a computational speed-up of 85%. Furthermore, we carry out a StS master equation analysis of an isochoric, isothermal heat bath simulation at various temperatures to study how the predicted rate coefficients impact the accuracy of multiple quantities of interest (QoIs) at the kinetics level (e.g., global quasi-steady state (QSS) dissociation rate coefficients and energy relaxation times). For all these QoIs, the master equation analysis relying on SurQCT data shows an accuracy within 15% across the entire temperature regime.

UR - http://www.scopus.com/inward/record.url?scp=85159647077&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85159647077&partnerID=8YFLogxK

U2 - 10.1039/d2cp05506f

DO - 10.1039/d2cp05506f

M3 - Article

C2 - 37183638

AN - SCOPUS:85159647077

SN - 1463-9076

VL - 25

SP - 13902

EP - 13912

JO - Physical Chemistry Chemical Physics

JF - Physical Chemistry Chemical Physics

IS - 20

ER -