Fast and Interpretable Machine Learning Modeling of Atmospheric Molecular Clusters.

ABSTRACT: Understanding how atmospheric molecular clusters form and grow is key to resolving one of the biggest uncertainties in climate modeling: the formation of new aerosol particles. While quantum chemistry offers accurate insights into these early-stage clusters, its steep computational costs limit large-scale exploration. In this work, we present a fast, interpretable, and surprisingly powerful alternative: the k-nearest neighbor (k-NN) regression model. By leveraging chemically informed distance metrics, including a kernel-induced metric and one learned via metric learning for kernel regression (MLKR), we show that simple k-NN models can rival more complex kernel ridge regression (KRR) models in accuracy while reducing computational time by orders of magnitude. We perform

SUBMITTER: Seppalainen L 

PROVIDER: S-EPMC12862803 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature