{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,5]],"date-time":"2026-09-05T07:11:39Z","timestamp":1788592299275,"version":"build-2803163510"},"reference-count":42,"publisher":"Wiley","issue":"23","license":[{"start":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T00:00:00Z","timestamp":1787097600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T00:00:00Z","timestamp":1787097600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Comput Chem"],"published-print":{"date-parts":[[2026,9,5]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Thermodynamic quantities in the hydrated state provide essential reference information for understanding hydrothermal biomass decomposition. However, accurately estimating thermodynamic quantities in the hydrated state using computational methods remains challenging. In this study, we develop a general computational framework to evaluate accurate solution\u2010phase thermodynamics in the standard state for compounds involved in the decomposition of xylose and glucose in water by combining gas\u2010phase free\u2010energy calculations with machine\u2010learning prediction of hydration free energies. Gas\u2010phase thermodynamic quantities are evaluated by a composite method designed to reproduce coupled\u2010cluster theory with singles, doubles, and perturbative triples in the complete basis set limit (CCSD(T)\/CBS) accuracy. Hydration free energies are predicted using molecular descriptors and machine learning. The resulting approach enables efficient evaluation of large reaction networks and is demonstrated for a dataset comprising 84 molecules and 73 reactions. Comparison with experimentally\u2010derived free\u2010energy differences for sugar isomerization indicates that the method achieves an accuracy within 3.7\u2009kcal\/mol in aqueous solution. The computed thermodynamic data further identify furfural and 5\u2010hydroxymethylfurfural as thermodynamically favorable products in the xylose and glucose systems, respectively. This framework provides a practical route to accurate standard\u2010state solution\u2010phase thermodynamics by combining high\u2010accuracy gas\u2010phase thermochemistry with machine\u2010learning prediction of hydration free energies.<\/jats:p>","DOI":"10.1002\/jcc.70483","type":"journal-article","created":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T11:04:29Z","timestamp":1787137469000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Accurate Solution\u2010Phase Thermodynamics via Composite Quantum Chemistry and Machine Learning: Application to Biomass Decomposition"],"prefix":"10.1002","volume":"47","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6242-0586","authenticated-orcid":false,"given":"Mikito","family":"Fujinami","sequence":"first","affiliation":[{"name":"Waseda Research Institute for Science and Engineering Waseda University  Tokyo 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