{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T19:45:10Z","timestamp":1792784710144,"version":"4.5.0"},"reference-count":60,"publisher":"American Chemical Society (ACS)","issue":"38","license":[{"start":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T00:00:00Z","timestamp":1789430400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T00:00:00Z","timestamp":1789430400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T00:00:00Z","timestamp":1789430400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-045"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["T2441001"],"award-info":[{"award-number":["T2441001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/na","name":"Innovation Program for Quantum Science and Technology","doi-asserted-by":"publisher","award":["2021ZD0303303"],"award-info":[{"award-number":["2021ZD0303303"]}],"id":[{"id":"10.13039\/na","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/na","name":"Zhejiang Provincial Natural Science Foundation of China","doi-asserted-by":"publisher","award":["LQ24B030004"],"award-info":[{"award-number":["LQ24B030004"]}],"id":[{"id":"10.13039\/na","id-type":"DOI","asserted-by":"publisher"},{"id":"https:\/\/ror.org\/04aqat463","id-type":"ROR","asserted-by":"crossref"}]},{"DOI":"10.13039\/na","name":"Supercomputing Center of the University of Science and Technology of China","doi-asserted-by":"publisher","award":["NA"],"award-info":[{"award-number":["NA"]}],"id":[{"id":"10.13039\/na","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,9,24]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Based on the accumulation of the high-quality databases from experimental measurements and theoretical calculations, data-driven machine learning approaches has been widely applied to rate coefficient prediction. However, most existing models are confined to single-temperature predictions and rely primarily on data-driven representations, resulting in limited temperature extrapolation capability. Although Arrhenius parameters have been introduced in some studies to encode temperature dependence, their effective utilization is restricted by the scarcity of high-quality Arrhenius-parameter datasets. To address these challenges, this study proposes a Graph Convolutional Network (GCN) architecture that deeply integrates physical constraints with data-driven strategies, leveraging an atmospheric-chemistry database of reaction kinetics between Volatile Organic Compounds (VOCs) and OH radicals. Instead of directly predicting rate coefficients, the model predicts Arrhenius parameters and indirectly derives rate coefficients across multiple temperatures, thereby enforcing physics-informed regularization over a broad temperature range. Meanwhile, to overcome the scarcity of high-quality Arrhenius parameters, we employed geometric data augmentation and a self-training-based semi-supervised learning strategy. This approach successfully incorporated 821 semi-labeled samples (containing only rate coefficient data) from the dataset, maximizing the utility of available kinetic data. Evaluation results demonstrate that the proposed model achieves excellent predictive performance (R2 = 0.93), significantly outperforming baseline GCN models (R2 = 0.77), with ablation studies verifying the vital contribution of each module. Functional group analysis confirms strong generalization for H-abstraction, while applicability domain analysis shows broad utility except for polyhalogen compounds. This work provides a novel methodological framework merging physical consistency and data-driven learning, which enables rapid, wide-temperature kinetic predictions under conditions of small and incomplete datasets. Learning from this database facilitates its convenient application to atmospheric pollutant screening and environmental risk assessment.<\/jats:p>","DOI":"10.1021\/acs.jpca.6c03636","type":"journal-article","created":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T04:01:46Z","timestamp":1789531306000},"page":"7559-7569","source":"Crossref","is-referenced-by-count":0,"title":["Multi-Temperature\nVOCs + OH Rate Coefficients Prediction\nvia GCN Constrained by the Arrhenius Equation"],"prefix":"10.1021","volume":"130","author":[{"given":"Hanchong","family":"Yu","sequence":"first","affiliation":[{"name":"University of Science and Technology of China , , , ,","place":["Hefei, Anhui, P. R. China, 230026"]},{"name":"University of Science and Technology of China , , , ,","place":["Hefei, Anhui, P. R. China, 230026"]},{"name":"Hefei National Laboratory , , ,","place":["Hefei, Anhui, P. R. China, 230088"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenjing","family":"Liu","sequence":"additional","affiliation":[{"name":"China Jiliang University , , , ,","place":["Hangzhou, Zhejiang, P. R. China, 310018"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangda","family":"Luo","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China , , , ,","place":["Hefei, Anhui, P. R. China, 230026"]},{"name":"University of Science and Technology of China , , , ,","place":["Hefei, Anhui, P. R. China, 230026"]},{"name":"Hefei National Laboratory , , ,","place":["Hefei, Anhui, P. R. China, 230088"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-8136-4911","authenticated-orcid":true,"given":"Dongyang","family":"Li","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China , , , ,","place":["Hefei, Anhui, P. R. China, 230026"]},{"name":"University of Science and Technology of China , , , ,","place":["Hefei, Anhui, P. R. China, 230026"]},{"name":"Hefei National Laboratory , , ,","place":["Hefei, Anhui, P. R. China, 230088"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sheng","family":"Huang","sequence":"additional","affiliation":[{"name":"Sichuan University , , , ,","place":["Chengdu, Sichuan, PR China, 610065"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8651-7833","authenticated-orcid":true,"given":"Xiaoqing","family":"Wu","sequence":"additional","affiliation":[{"name":"China Jiliang University , , , ,","place":["Hangzhou, Zhejiang, P. R. China, 310018"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9730-8487","authenticated-orcid":true,"given":"Feng","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China , , , ,","place":["Hefei, Anhui, P. R. China, 230026"]},{"name":"Hefei National Laboratory , , ,","place":["Hefei, Anhui, P. R. China, 230088"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2026,9,15]]},"reference":[{"issue":"2","key":"2026092408075786600_cit1","doi-asserted-by":"publisher","first-page":"1203","DOI":"10.5194\/essd-12-1203-2020","article-title":"Database for the kinetics of the gas-phase atmospheric\nreactions of organic compounds[J]","volume":"12","author":"McGillen","year":"2020","journal-title":"Earth Syst.\nSci. Data"},{"issue":"20","key":"2026092408075786600_cit2","doi-asserted-by":"publisher","first-page":"13379","DOI":"10.5194\/acp-25-13379-2025","article-title":"Implications of VOC oxidation in\natmospheric chemistry: development of a comprehensive AI model for\npredicting reaction rate constants[J]","volume":"25","author":"Zhang","year":"2025","journal-title":"Atmos.\nChem. Phys."},{"issue":"1","key":"2026092408075786600_cit3","doi-asserted-by":"publisher","first-page":"199","DOI":"10.5194\/acp-25-199-2025","article-title":"Derivation of atmospheric\nreaction mechanisms for volatile organic compounds by the SAPRC mechanism\ngeneration system (MechGen)[J]","volume":"25","author":"Carter","year":"2025","journal-title":"Atmos. Chem.\nPhys."},{"issue":"23","key":"2026092408075786600_cit4","doi-asserted-by":"publisher","first-page":"13317","DOI":"10.5194\/acp-24-13317-2024","article-title":"Opinion: Challenges\nand needs of tropospheric chemical mechanism development[J]","volume":"24","author":"Ervens","year":"2024","journal-title":"Atmos. Chem. Phys."},{"issue":"1","key":"2026092408075786600_cit5","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.proci.2016.07.100","article-title":"From theoretical\nreaction dynamics to chemical modeling of combustion[J]","volume":"36","author":"Klippenstein","year":"2017","journal-title":"Proc. Combust. Inst."},{"issue":"17","key":"2026092408075786600_cit6","doi-asserted-by":"publisher","first-page":"3449","DOI":"10.1021\/acs.jpca.4c00750","article-title":"Quasi-classical trajectory calculation of rate constants using an\nab initio trained machine learning model (aML-MD) with multifidelity\ndata[J]","volume":"128","author":"Shi","year":"2024","journal-title":"J. Phys. Chem. A"},{"issue":"2","key":"2026092408075786600_cit7","doi-asserted-by":"publisher","first-page":"1049","DOI":"10.1021\/acs.chemrev.0c00394","article-title":"Microkinetic modeling: a tool for\nrational catalyst\ndesign[J]","volume":"121","author":"Motagamwala","year":"2021","journal-title":"Chem. Rev."},{"issue":"6","key":"2026092408075786600_cit8","doi-asserted-by":"publisher","DOI":"10.1002\/wcms.1372","article-title":"Microkinetic modeling\nin homogeneous catalysis[J]","volume":"8","author":"Besora","year":"2018","journal-title":"Wiley Interdiscip.\nRev.:Comput. Mol. Sci."},{"issue":"9","key":"2026092408075786600_cit9","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1002\/biot.201400522","article-title":"Constructing kinetic models of metabolism\nat genome-scales: a review[J]","volume":"10","author":"Srinivasan","year":"2015","journal-title":"Biotechnol. J."},{"key":"2026092408075786600_cit10","doi-asserted-by":"publisher","DOI":"10.1016\/j.mec.2024.e00234","article-title":"Bottom-up parameterization of enzyme rate constants:\nReconciling inconsistent data[J]","volume":"18","author":"Zielinski","year":"2024","journal-title":"Metab. Eng.\nCommun."},{"issue":"28","key":"2026092408075786600_cit11","doi-asserted-by":"publisher","first-page":"4617","DOI":"10.1021\/acs.jpca.2c00713","article-title":"Transfer Learning Approach to Multitarget\nTemperature-Dependent Reaction Rate Prediction[J]","volume":"126","author":"Al Ibrahim","year":"2022","journal-title":"J. Phys. Chem. A"},{"key":"2026092408075786600_cit12","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.pecs.2014.05.001","article-title":"Recent advances in laser absorption\nand shock tube\nmethods for studies of combustion chemistry[J]","volume":"44","author":"Hanson","year":"2014","journal-title":"Prog. Energy Combust. Sci."},{"issue":"12","key":"2026092408075786600_cit13","doi-asserted-by":"publisher","first-page":"4549","DOI":"10.1021\/cr020512r","article-title":"Laboratory\nstudies of atmospheric reactions at low temperatures[J]","volume":"103","author":"Smith","year":"2003","journal-title":"Chem. Rev."},{"key":"2026092408075786600_cit14","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.combustflame.2017.05.006","article-title":"Measurement of reaction\nrate constants using RCM: A case study of decomposition of dimethyl\ncarbonate to dimethyl ether[J]","volume":"183","author":"Zhang","year":"2017","journal-title":"Combust. Flame"},{"issue":"2","key":"2026092408075786600_cit15","doi-asserted-by":"publisher","first-page":"528","DOI":"10.1021\/ja408723a","article-title":"Chemical\nkinetics and mechanisms\nof complex systems: A perspective on recent theoretical advances[J]","volume":"136","author":"Klippenstein","year":"2014","journal-title":"J. Am. Chem. Soc."},{"issue":"1","key":"2026092408075786600_cit16","doi-asserted-by":"publisher","first-page":"1021","DOI":"10.1038\/s41597-025-05272-6","article-title":"ReSpecTh:\nReaction kinetics, spectroscopy, and thermochemical datasets[J]","volume":"12","author":"Tura\u0301nyi","year":"2025","journal-title":"Sci. Data"},{"key":"2026092408075786600_cit17","doi-asserted-by":"publisher","first-page":"A63","DOI":"10.1051\/0004-6361\/202450606","article-title":"The 2024 KIDA\nnetwork for interstellar chemistry[J]","volume":"689","author":"Wakelam","year":"2024","journal-title":"Astron.\nAstrophys."},{"key":"2026092408075786600_cit18","doi-asserted-by":"publisher","first-page":"113375","DOI":"10.1016\/j.combustflame.2024.113375","article-title":"A machine\nlearning method to predict rate constants\nfor various reactions in combustion kinetic models[J]","volume":"263","author":"Li","year":"2024","journal-title":"Combust. Flame"},{"key":"2026092408075786600_cit19","doi-asserted-by":"publisher","first-page":"126627","DOI":"10.1016\/j.cej.2020.126627","article-title":"Shedding light on \u201cBlack\nBox\u201d machine learning models for predicting the reactivity\nof HO radicals toward organic compounds[J]","volume":"405","author":"Zhong","year":"2021","journal-title":"Chem. Eng. J."},{"issue":"5","key":"2026092408075786600_cit20","doi-asserted-by":"publisher","first-page":"2692","DOI":"10.1039\/D1CP04422B","article-title":"Progress towards machine learning\nreaction rate constants[J]","volume":"24","author":"Komp","year":"2022","journal-title":"Phys. Chem. Chem.\nPhys."},{"issue":"7","key":"2026092408075786600_cit21","doi-asserted-by":"publisher","first-page":"2410","DOI":"10.1039\/D3SC05353A","article-title":"Machine learning\nfrom quantum chemistry to predict\nexperimental solvent effects on reaction rates[J]","volume":"15","author":"Chung","year":"2024","journal-title":"Chem. Sci."},{"issue":"10","key":"2026092408075786600_cit22","doi-asserted-by":"publisher","first-page":"5200","DOI":"10.1021\/acs.iecr.4c03938","article-title":"Machine Learning Accelerated Analysis of Chemical Reaction\nNetworks for Gas-Phase Reaction Systems[J]","volume":"64","author":"Liu","year":"2025","journal-title":"Ind. Eng. Chem. Res."},{"key":"2026092408075786600_cit23","doi-asserted-by":"publisher","first-page":"138871","DOI":"10.1016\/j.scitotenv.2020.138871","article-title":"Quantitative structure activity relationship (QSAR)\nmodelling of the degradability rate constant of volatile organic compounds\n(VOCs) by OH radicals in atmosphere[J]","volume":"729","author":"Liu","year":"2020","journal-title":"Sci.\nTotal Environ."},{"key":"2026092408075786600_cit24","doi-asserted-by":"publisher","first-page":"120775","DOI":"10.1016\/j.atmosenv.2024.120775","article-title":"Predicting reaction\nrate constants of organic compounds with oxidants\nin the atmospheric aqueous-phase through multi-task learning[J]","volume":"337","author":"Gu","year":"2024","journal-title":"Atmos. Environ."},{"issue":"11","key":"2026092408075786600_cit25","doi-asserted-by":"publisher","first-page":"2829","DOI":"10.1021\/acsestengg.4c00389","article-title":"Graph Neural\nNetwork Integrating Self-Supervised Pretraining for Precise and Interpretable\nPrediction of Micropollutant Treatability by HO\u2022-Based Advanced\nOxidation Processes[J]","volume":"4","author":"Zhu","year":"2024","journal-title":"ACS ES&T Eng."},{"issue":"8","key":"2026092408075786600_cit26","doi-asserted-by":"publisher","first-page":"1995","DOI":"10.1021\/acsestengg.5c00133","article-title":"Innovative\nFeature Engineering Driven\nby Chemical Category in Machine Learning for Optimizing the Prediction\nof Hydroxyl Radical Reaction Rate Constants[J]","volume":"5","author":"Zhang","year":"2025","journal-title":"ACS ES&T Eng."},{"issue":"1","key":"2026092408075786600_cit27","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.proci.2018.06.054","article-title":"Developing\ndetailed chemical kinetic mechanisms for fuel combustion[J]","volume":"37","author":"Curran","year":"2019","journal-title":"Proc. Combust. Inst."},{"issue":"1","key":"2026092408075786600_cit28","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1021\/acs.jpca.4c07426","article-title":"Predicting\nRate Constants of Hydrogen Abstraction Reactions between OH\/HO2 and\nAlkanes by Machine Learning Models[J]","volume":"129","author":"Xia","year":"2025","journal-title":"J. Phys.\nChem. A"},{"key":"2026092408075786600_cit29","unstructured":"Burkholder, J.; Sander, S.; Abbatt, J.; Barker, J.; Huie, R.; Kolb, C.; Kurylo, M.; Orkin, V.; Wilmouth, D.; Wine, P.\n          Chemical Kinetics and Photochemical\nData for Use in Atmospheric\nStudies: Evaluation Number 18[J]. NASA Panel for Data Evaluation Technical\nReport; NIST, 2015."},{"issue":"16","key":"2026092408075786600_cit30","doi-asserted-by":"publisher","first-page":"8045","DOI":"10.5194\/acp-13-8045-2013","article-title":"Evaluated\nkinetic and photochemical data for atmospheric chemistry: Volume VI\u2013heterogeneous\nreactions with liquid substrates[J]","volume":"13","author":"Ammann","year":"2013","journal-title":"Atmos.\nChem. Phys."},{"issue":"6","key":"2026092408075786600_cit31","doi-asserted-by":"publisher","first-page":"1461","DOI":"10.5194\/acp-4-1461-2004","article-title":"Evaluated\nkinetic and photochemical data for atmospheric chemistry: Volume I-gas\nphase reactions of Ox, HOx, NOx and SOx species[J]","volume":"4","author":"Atkinson","year":"2004","journal-title":"Atmos. Chem. Phys."},{"issue":"11","key":"2026092408075786600_cit32","doi-asserted-by":"publisher","first-page":"3625","DOI":"10.5194\/acp-6-3625-2006","article-title":"Evaluated\nkinetic and photochemical data for atmospheric chemistry: Volume II\u2013gas\nphase reactions of organic species[J]","volume":"6","author":"Atkinson","year":"2006","journal-title":"Atmos.\nChem. Phys."},{"issue":"4","key":"2026092408075786600_cit33","doi-asserted-by":"publisher","first-page":"981","DOI":"10.5194\/acp-7-981-2007","article-title":"Evaluated\nkinetic and photochemical data for atmospheric chemistry: Volume III\u2013gas\nphase reactions of inorganic halogens[J]","volume":"7","author":"Atkinson","year":"2007","journal-title":"Atmos.\nChem. Phys."},{"issue":"15","key":"2026092408075786600_cit34","doi-asserted-by":"publisher","first-page":"4141","DOI":"10.5194\/acp-8-4141-2008","article-title":"Evaluated kinetic and photochemical data for atmospheric\nchemistry: Volume IV\u2013gas phase reactions of organic halogen\nspecies[J]","volume":"8","author":"Atkinson","year":"2008","journal-title":"Atmos. Chem. Phys."},{"issue":"18","key":"2026092408075786600_cit35","doi-asserted-by":"publisher","first-page":"9059","DOI":"10.5194\/acp-10-9059-2010","article-title":"Evaluated\nkinetic and photochemical data for atmospheric chemistry: Volume V\u2013heterogeneous\nreactions on solid substrates[J]","volume":"10","author":"Crowley","year":"2010","journal-title":"Atmos. Chem.\nPhys."},{"key":"2026092408075786600_cit36","doi-asserted-by":"crossref","DOI":"10.1093\/oso\/9780195131772.001.0001","volume-title":"The Mechanisms of Atmospheric Oxidation\nof the Alkenes [M]","author":"Calvert","year":"2000"},{"key":"2026092408075786600_cit37","doi-asserted-by":"crossref","DOI":"10.1093\/oso\/9780195146288.001.0001","volume-title":"The Mechanisms Of Atmospheric\nOxidation Of Aromatic Hydrocarbons [M]","author":"Calvert","year":"2002"},{"key":"2026092408075786600_cit38","doi-asserted-by":"crossref","DOI":"10.1093\/oso\/9780195365818.001.0001","volume-title":"Mechanisms of Atmospheric\nOxidation of the Alkanes [M]","author":"Calvert","year":"2008"},{"key":"2026092408075786600_cit39","doi-asserted-by":"crossref","DOI":"10.1093\/oso\/9780199767076.001.0001","volume-title":"Mechanisms of Atmospheric\nOxidation of the Oxygenates [M]","author":"Calvert","year":"2011"},{"key":"2026092408075786600_cit40","doi-asserted-by":"crossref","DOI":"10.1093\/oso\/9780190233020.001.0001","volume-title":"The Mechanisms of\nReactions Influencing Atmospheric Ozone [M]","author":"Calvert","year":"2015"},{"issue":"6\u20137","key":"2026092408075786600_cit41","doi-asserted-by":"publisher","first-page":"476","DOI":"10.1002\/minf.201000061","article-title":"Best practices\nfor QSAR model development, validation, and exploitation","volume":"29","author":"Tropsha","year":"2010","journal-title":"Mol. Inf."},{"key":"2026092408075786600_cit42","unstructured":"Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., Dahl, G. E.\n          Neural message\npassing for quantum chemistry. In  Proceedings\nof the International conference on machine learning, F; PMLR, 2017."},{"key":"2026092408075786600_cit43","unstructured":"Duvenaud, D. K.; Maclaurin, D.; Iparraguirre, J.; Bombarell, R.; Hirzel, T.; Aspuru-Guzik, A.; Adams, R. P.\n          Convolutional networks\non graphs for learning molecular fingerprints. In  NIPS\u201915: Proceedings of the 29th International Conference\non Neural Information Processing Systems - Volume 2; MIT Press, 2015."},{"key":"2026092408075786600_cit44","doi-asserted-by":"publisher","first-page":"arXiv.1609.02907","DOI":"10.48550\/arXiv.1609.02907","article-title":"Semi-supervised classification with graph convolutional\nnetworks[J]","author":"Kipf","year":"2016","journal-title":"arXiv"},{"key":"2026092408075786600_cit45","doi-asserted-by":"publisher","first-page":"arXiv:1511.07289","DOI":"10.48550\/arXiv.1511.07289","article-title":"Fast and accurate deep network learning\nby exponential linear units (elus)[J]","author":"Clevert","year":"2015","journal-title":"arXiv"},{"key":"2026092408075786600_cit46","doi-asserted-by":"crossref","unstructured":"Zhao, T., Liu, Y., Neves, L., Woodford, O., Jiang, M., Shah, N.\n          Data augmentation for graph neural networks. In  Proceedings of the AAAI conference on artificial intelligence; Springer, 2021.","DOI":"10.1609\/aaai.v35i12.17315"},{"key":"2026092408075786600_cit47","doi-asserted-by":"publisher","first-page":"arXiv:2110.07728","DOI":"10.48550\/arXiv.2110.07728","article-title":"Pre-training molecular graph representation with 3d geometry[J]","author":"Liu","year":"2021","journal-title":"arXiv"},{"issue":"2","key":"2026092408075786600_cit48","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1007\/s10115-013-0706-y","article-title":"Self-labeled techniques for semi-supervised\nlearning: taxonomy, software and empirical study","volume":"42","author":"Triguero","year":"2015","journal-title":"Knowl. Inf. Syst."},{"issue":"5","key":"2026092408075786600_cit49","doi-asserted-by":"publisher","first-page":"694","DOI":"10.1002\/qsar.200610151","article-title":"Principles\nof QSAR models validation: internal and external[J]","volume":"26","author":"Gramatica","year":"2007","journal-title":"QSAR Comb. Sci."},{"issue":"4","key":"2026092408075786600_cit50","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1002\/wics.101","article-title":"Principal component analysis[J]","volume":"2","author":"Abdi","year":"2010","journal-title":"Wiley Interdiscip. Rev. Comput. Stat."},{"issue":"1","key":"2026092408075786600_cit51","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/S0169-7439(99)00047-7","article-title":"The mahalanobis\ndistance[J]","volume":"50","author":"De Maesschalck","year":"2000","journal-title":"Chemom. Intell. Lab. Syst."},{"issue":"14","key":"2026092408075786600_cit52","doi-asserted-by":"publisher","first-page":"14034","DOI":"10.1007\/s11356-016-6527-2","article-title":"Modeling the reactivities\nof hydroxyl radical and ozone towards atmospheric organic chemicals\nusing quantitative structure-reactivity relationship approaches[J]","volume":"23","author":"Gupta","year":"2016","journal-title":"Environ. Sci. Pollut. Res."},{"key":"2026092408075786600_cit53","doi-asserted-by":"publisher","first-page":"117244","DOI":"10.1016\/j.ces.2021.117244","article-title":"Computer-aided estimation\nof kinetic\nrate constant for degradation of volatile organic compounds by hydroxyl\nradical: An improved model using quantum chemical and norm descriptors[J]","volume":"248","author":"Shi","year":"2022","journal-title":"Chem. Eng. Sci."},{"key":"2026092408075786600_cit54","doi-asserted-by":"publisher","first-page":"127998","DOI":"10.1016\/j.cej.2020.127998","article-title":"Molecular image-convolutional neural\nnetwork (CNN) assisted QSAR models for predicting contaminant reactivity\ntoward OH radicals: Transfer learning, data augmentation and model\ninterpretation[J]","volume":"408","author":"Zhong","year":"2021","journal-title":"Chem. Eng. J."},{"issue":"4","key":"2026092408075786600_cit55","doi-asserted-by":"publisher","first-page":"914","DOI":"10.1021\/acs.jpca.5c07873","article-title":"Automatization\nof Atmospheric OH Radical Abstraction Reactions[J]","volume":"130","author":"Ayoubi","year":"2026","journal-title":"J. Phys. Chem. A"},{"issue":"16","key":"2026092408075786600_cit56","doi-asserted-by":"publisher","first-page":"9169","DOI":"10.5194\/acp-25-9169-2025","article-title":"Atmospheric\nbreakdown kinetics and air quality impact of potential \u201cgreen\u201d\nsolvents, the oxymethylene ethers OME3 and OME4[J]","volume":"25","author":"D\u2019Souza","year":"2025","journal-title":"Atmos. Chem. Phys."},{"issue":"7","key":"2026092408075786600_cit57","doi-asserted-by":"publisher","first-page":"2383","DOI":"10.1021\/acs.jcim.3c00958","article-title":"Explainable Graph Neural\nNetworks\nwith Data Augmentation for Predicting p K a of C\u2013H Acids[J]","volume":"64","author":"An","year":"2024","journal-title":"J. Chem. Inf. Model."},{"issue":"20","key":"2026092408075786600_cit58","doi-asserted-by":"publisher","first-page":"10832","DOI":"10.1021\/acs.jcim.5c00495","article-title":"Resonance-Invariant\nGraph Representation for Molecular Property Prediction[J]","volume":"65","author":"Zalte","year":"2025","journal-title":"J. Chem. Inf. Model."},{"issue":"1","key":"2026092408075786600_cit59","doi-asserted-by":"publisher","first-page":"2233539","DOI":"10.1080\/17518253.2023.2233539","article-title":"Ab initio\nchemical\nkinetics of Isopropyl acetate oxidation with OH radicals[J]","volume":"16","author":"Abdel-Rahman","year":"2023","journal-title":"Green Chem. Lett. Rev."},{"issue":"21","key":"2026092408075786600_cit60","doi-asserted-by":"publisher","first-page":"11583","DOI":"10.1021\/acs.jcim.5c01672","article-title":"ADvisor: An\nOpen-Source Tool for Applicability Domain Definition and Optimization\nin Molecular Predictive Modeling[J]","volume":"65","author":"Piazza","year":"2025","journal-title":"J. Chem.\nInf. Model."}],"container-title":["The Journal of Physical\nChemistry A"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/pubs.acs.org\/doi\/pdf\/10.1021\/acs.jpca.6c03636","content-type":"application\/pdf","content-version":"vor","intended-application":"unspecified"},{"URL":"https:\/\/pubs.acs.org\/jpcafh\/article-pdf\/130\/38\/7559\/68525505\/acs.jpca.6c03636.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/pubs.acs.org\/jpcafh\/article-pdf\/130\/38\/7559\/68525505\/acs.jpca.6c03636.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T12:22:45Z","timestamp":1790252565000},"score":1,"resource":{"primary":{"URL":"https:\/\/pubs.acs.org\/jpcafh\/article\/130\/38\/7559\/5430884\/Multi-Temperature-VOCs-OH-Rate-Coefficients"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,15]]},"references-count":60,"journal-issue":{"issue":"38","published-online":{"date-parts":[[2026,9,15]]},"published-print":{"date-parts":[[2026,9,24]]}},"URL":"https:\/\/doi.org\/10.1021\/acs.jpca.6c03636","relation":{},"ISSN":["1089-5639","1520-5215"],"issn-type":[{"value":"1089-5639","type":"print"},{"value":"1520-5215","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,15]]}}}