共 50 条
- [1] Machine Learning for Predicting Chemical Potentials of Multifunctional Organic Compounds in Atmospherically Relevant Solutions JOURNAL OF PHYSICAL CHEMISTRY LETTERS, 2022, : 9928 - 9933
- [2] Machine Learning Modeling of Environmentally Relevant Chemical Reactions for Organic Compounds ACS ES&T WATER, 2022, 4 (03): : 773 - 783
- [3] Predicting Secondary Organic Aerosol Enhancement in the Presence of Atmospherically Relevant Organic Particles ACS EARTH AND SPACE CHEMISTRY, 2018, 2 (10): : 1035 - 1046
- [4] Machine learning for predicting lifespan-extending chemical compounds AGING-US, 2017, 9 (07): : 1721 - 1737
- [5] Utilizing Machine Learning Models for Predicting Diamagnetic Susceptibility of Organic Compounds ACS OMEGA, 2024, 9 (12): : 14368 - 14374
- [6] Combined experimental and theoretical studies of photolysis of atmospherically relevant organic compounds in various phases ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 2014, 247
- [7] Predicting antioxidant activity of compounds based on chemical Predicting antioxidant activity of compounds based on chemical structure using machine learning methods structure using machine learning methods KOREAN JOURNAL OF PHYSIOLOGY & PHARMACOLOGY, 2024, 28 (06): : 527 - 537
- [8] Machine Learning for Ionization Potentials and Photoionization Cross Sections of Volatile Organic Compounds ACS EARTH AND SPACE CHEMISTRY, 2023,
- [9] Machine Learning for Ionization Potentials and Photoionization Cross Sections of Volatile Organic Compounds ACS EARTH AND SPACE CHEMISTRY, 2023, : 863 - 875
- [10] Symbolic, neural, and Bayesian machine learning models for predicting carcinogenicity of chemical compounds JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES, 2000, 40 (04): : 906 - 914