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We present an algorithm that combines quantum scattering calculations with probabilistic machine-learning models to predict quantum dynamics rate coefficients for a large number of state-to-state transitions in molecule–molecule collisions much faster than with direct solutions of the Schrödinger equation. By utilizing the predictive power of Gaussian process regression with kernels, optimized to make accurate predictions outside of the input parameter space, the present strategy reduces the computational cost by about 75%, with an accuracy within 5%. Our method uses temperature dependences of rate coefficients for transitions from the isolated states of initial rotational angular momentum j, determined via explicit calculations, to predict the temperature dependences of rate coefficients for other values of j. The approach, demonstrated here for rovibrational transitions of SiO due to thermal collisions with H2, uses different prediction models and is thus adaptive to various time and accuracy requirements. The procedure outlined in this work can be used to extend multiple inelastic molecular collision databases without exponentially large computational resources required for conventional rigorous quantum dynamics calculations. 

ABSTRACT Rate coefficients for rotational transitions in HD induced by H2 impact for rotational levels of HD j ≤ 8 and temperatures 10 K ≤ T ≤ 5000 K are reported. The quantum mechanical close-coupling (CC) method and the coupled-states (CS) decoupling approximation are used to obtain the cross-sections employing the most recent highly accurate H2–H2 potential energy surface (PES). Our results are in good agreement with previous calculations for low-lying rotational transitions The cooling efficiency of HD compared with H2 and astrophysical applications are briefly discussed.