Gaussian process model for extrapolation of scattering observables for complex molecules: From benzene to benzonitrile

Abstract

We consider a problem of extrapolating the collision properties of a large polyatomic molecule A-H to make predictions of the dynamical properties for another molecule related to A-H by the substitution of the H atom with a small molecular group X, without explicitly computing the potential energy surface for A-X. We assume that the effect of the −H → −X substitution is embodied in a multidimensional function with unknown parameters characterizing the change of the potential energy surface. We propose to apply the Gaussian Process model to determine the dependence of the dynamical observables on the unknown parameters. This can be used to produce an interval of the observable values that corresponds to physical variations of the potential parameters. We show that the Gaussian Process model combined with classical trajectory calculations can be used to obtain the dependence of the cross sections for collisions of C 6 H 5 CN with He on the unknown parameters describing the interaction of the He atom with the CN fragment of the molecule. The unknown parameters are then varied within physically reasonable ranges to produce a prediction uncertainty of the cross sections. The results are normalized to the cross sections for He -C 6 H 6 collisions obtained from quantum scattering calculations in order to provide a prediction interval of the thermally averaged cross sections for collisions of C 6 H 5 CN with He.

Rate Constants for Fine-structure Excitations in O–H Collisions with Error Bars Obtained by Machine Learning

“…The application of GP regressions to molecular physics problems has been described in our recent work , Cui, Li & Krems 2015, Cui & Krems 2016, where more details on the implementation of the method can be found. In brief, the interpolation starts by estimating the statistical correlations between rate constants corresponding to different points x in the five-dimensional space considered here.…”

Perspective: Ultracold molecules and the dawn of cold controlled chemistry

“…In ultracold collisional studies, it is often customary to explore the sensitivity of the computed observables to small variations of the interaction potential through a scaling factor and varying this factor within the uncertainty of the potential. A recent method based on the Gaussian process model proposed by Krems and coworkers 284,285 allows efficient mapping of the sensitivity of computed results to all parameters that define the potential. Averaging the scattering observables over variations of these parameters offers an efficient means of providing realistic error bars on the computed results.…”

Use and limitations of offsite consequence analysis tools from South Korea and the United States in hydrogen fluoride accidental release

“…The RMP*Comp model calculates the leak rate according to the formula given in the US Code of Federal Regulations (40 CFR part 68; Chemical…2016) and calculates the range of impact according to leak rate and safety‐level concentration. The KORA model calculates the range of impact using the Gaussian (Cui et al ) and SLAB (Ermak ) models (Lim et al ). The ALOHA model calculates the range of impact using the Gaussian (Cui et al ) and DEGADIS (Spicer and Havens ) models (Thoman et al ).…”

“…The KORA model calculates the range of impact using the Gaussian (Cui et al ) and SLAB (Ermak ) models (Lim et al ). The ALOHA model calculates the range of impact using the Gaussian (Cui et al ) and DEGADIS (Spicer and Havens ) models (Thoman et al ). To calculate the offsite impact range, KORA and ALOHA use the Gaussian model for light gases such as HF.…”