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Recent advances in machine learning have enabled flexible modeling beyond conventional physical laws, but most existing strategies remain confined to surrogate models of end\u2010point yields rather than transient dynamics. Consequently, an end\u2010to\u2010end framework unifying systematic kinetic data acquisition with machine learning based modeling has been lacking. In this paper, we present a unified framework that integrates droplet microfluidics with machine learning for the systematic analysis of temperature\u2010dependent reaction kinetics. The platform is designed to enable immobilization and long\u2010term time\u2010lapse imaging of thousands of droplets under dynamic thermal gradients. This configuration yields massively parallel time\u2010resolved datasets across diverse temperature conditions capturing transient kinetics and provides suitable inputs for training machine\u2010learning models of reaction dynamics. Leveraging these datasets, we train Neural ODE models to learn nonlinear temperature\u2010dependent kinetics directly from experimental data. We demonstrate accurate prediction of enzymatic kinetics, highlighting the robustness and versatility of the approach. Our framework bridges high\u2010throughput experimental data acquisition with data\u2010driven modeling, establishing a foundation for predictive ability and rational analysis and design of temperature\u2010sensitive biochemical\u00a0processes.<\/jats:p>","DOI":"10.1002\/advs.77040","type":"journal-article","created":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T14:44:08Z","timestamp":1787582648000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning of Temperature\u2010Dependent Chemical Kinetics Using Parallel Droplet Microreactors"],"prefix":"10.1002","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-0666-5540","authenticated-orcid":false,"given":"Mamoru","family":"Saita","sequence":"first","affiliation":[{"name":"Department of Applied Physics and Physico\u2010Informatics, Faculty of Science and Technology Keio University  Kanagawa Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3253-4985","authenticated-orcid":false,"given":"Yutaka","family":"Hori","sequence":"additional","affiliation":[{"name":"Department of Applied Physics and Physico\u2010Informatics, Faculty of Science and Technology Keio University  Kanagawa Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,8,24]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.1061967"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1128\/MMBR.05018-11"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12275-023-00031-x"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev.ph.57.030195.000355"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.molcel.2010.10.006"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.pisc.2014.02.005"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1042\/bse0590001"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1038\/nrmicro2730"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.biotechadv.2022.107907"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.3390\/molecules27206798"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.3389\/fbioe.2020.00213"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tibs.2010.05.001"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-biophys-121219-081520"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1021\/ed061p494"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-021-03213-y"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cej.2019.123340"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1002\/cmtd.202000044"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.chemrev.2c00798"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-018-0307-8"},{"key":"e_1_2_9_21_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-025-58139-0"},{"key":"e_1_2_9_22_1","unstructured":"R. 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