“pySiRC”: machine learning combined with molecular fingerprints to predict the reaction rate constant of the radical-based oxidation processes of aqueous organic contaminants

2024-03-192024-03-192021-09-02SANCHES-NETO, Flávio Olimpio et al. “pySiRC”: machine learning combined with molecular fingerprints to predict the reaction rate constant of the radical-based oxidation processes of aqueous organic contaminants. Environmental Science & Technology, Washington, v. 55, n. 18, p. 12437-12448, 2021. DOI: 10.1021/acs.est.1c04326. Disponível em: https://pubs.acs.org/doi/10.1021/acs.est.1c04326. Acesso em: 8 mar. 2024.e- 1520-58510013-936Xhttps://pubs.acs.org/doi/10.1021/acs.est.1c04326engAcesso RestritoArtificial intelligenceEmerging contaminant degradationKinetic parametersApps and web applications“pySiRC”: machine learning combined with molecular fingerprints to predict the reaction rate constant of the radical-based oxidation processes of aqueous organic contaminantsArtigo10.1021/acs.est.1c04326