Neural network ensemble for computing cross sections for rotational transitions in H₂O+H₂O collisions

Rotational transitions in H₂O+H₂O collisions are important for modeling astrophysical environments rich in water molecules but they are computationally intractable using quantum mechanical methods. Here, we present a machine learning (ML) tool using an ensemble of neural networks (NNs) to predict cross sections to construct a database of rate coefficients for rotationally inelastic transitions in collisions of complex molecules such as water. The proposed methodology utilizes data computed with a mixed quantum-classical theory. We illustrate that efficient ML models using NN can be built to accurately interpolate in the space of 12 quantum numbers for rotational transitions in two asymmetric top molecules, spanning both initial and final states. The methodology is robust, and thus, applicable to other complex molecular systems.