11 citations as recorded by crossref.

1. Current and future machine learning approaches for modeling atmospheric cluster formation J. Kubečka et al. 10.1038/s43588-023-00435-0
2. Predict Ionization Energy of Molecules Using Conventional and Graph-Based Machine Learning Models Y. Liu & Z. Li 10.1021/acs.jcim.2c01321
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4. Accelerating models for multiphase chemical kinetics through machine learning with polynomial chaos expansion and neural networks T. Berkemeier et al. 10.5194/gmd-16-2037-2023
5. Unified representation of molecules and crystals for machine learning H. Huo & M. Rupp 10.1088/2632-2153/aca005
6. Atomic structures, conformers and thermodynamic properties of 32k atmospheric molecules V. Besel et al. 10.1038/s41597-023-02366-x
7. Quantum Machine Learning Approach for Studying Atmospheric Cluster Formation J. Kubečka et al. 10.1021/acs.estlett.1c00997
8. Machine Learning for Predicting Chemical Potentials of Multifunctional Organic Compounds in Atmospherically Relevant Solutions N. Hyttinen et al. 10.1021/acs.jpclett.2c02612
9. Evaluating the Impact of Vehicular Aerosol Emissions on Particulate Matter (PM2.5) Formation Using Modeling Study O. Sánchez-Ccoyllo et al. 10.3390/atmos13111816
10. Atmospheric Sulfuric Acid–Multi-Base New Particle Formation Revealed through Quantum Chemistry Enhanced by Machine Learning J. Kubečka et al. 10.1021/acs.jpca.3c00068
11. Gas-to-Particle Partitioning of Cyclohexene- and α-Pinene-Derived Highly Oxygenated Dimers Evaluated Using COSMOtherm N. Hyttinen et al. 10.1021/acs.jpca.0c11328