Quantum Machine Learning Approach for Studying Atmospheric Cluster Formation

Abstract

Quantum chemical (QC) calculations can yield direct insight into an atmospheric cluster formation mechanism and cluster formation rates. However, such calculations are extremely computationally demanding as more than millions of cluster configurations might exist and need to be computed. We present an efficient approach to produce high quality QC data sets for applications in cluster formation studies and how to train an accurate quantum machine learning model on the generated data. Using the two-component sulfuric acid─water system as a proof of concept, we demonstrate that a kernel ridge regression machine learning model with Δ-learning can be trained to accurately predict the binding energies of cluster equilibrium configurations with mean absolute errors below 0.5 kcal mol–1. Additionally, we enlarge the training data set with nonequilibrium configurations and show the possibility of predicting the binding energies of new structures of clusters several molecules larger than those in the training set. Applying the trained machine learning model leads to a drastic reduction in the number of relevant clusters that need to be explicitly evaluated by QC methods. The presented approach is directly transferable to clusters of arbitrary composition and will lead to faster and more efficient exploration of the configurational space of new cluster systems.