ArticleEnvironmental science. Advances2024
Accurate modeling of the potential energy surface of atmospheric molecular clusters boosted by neural networks.
Article in Environmental science. Advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Fast and Interpretable Machine Learning Modeling of Atmospheric Molecular Clusters.The journal of physical chemistry. A · 2026Article
- Enhanced configurational sampling methods reveal the importance of molecular stiffness for clustering of oxygenated organic molecules.Physical chemistry chemical physics : PCCP · 2025Article
- Extrapolating Local Coupled Cluster Calculations toward CCSD(T)/CBS Binding Energies of Atmospheric Molecular Clusters.ACS omega · 2025Article
- Thermodynamics of Molecular Binding and Clustering in the Atmosphere Revealed through Conventional and ML-Enhanced Umbrella Sampling.ACS omega · 2025Article
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Authors and funding
7 authors.
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Abstract
The computational cost of accurate quantum chemistry (QC) calculations of large molecular systems can often be unbearably high. Machine learning offers a lower computational cost compared to QC methods while maintaining their accuracy. In this study, we employ the polarizable atom interaction neural network (PaiNN) architecture to train and model the potential energy surface of molecular clusters relevant to atmospheric new particle formation, such as sulfuric acid-ammonia clusters. We compare the differences between PaiNN and previous kernel ridge regression modeling for the Clusteromics I-V data sets. We showcase three models capable of predicting electronic binding energies and interatomic forces with mean absolute errors of <0.3 kcal mol
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