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This study demonstrates a novel methodology for efficiently predicting phase behavior in Lennard-Jones mixtures by leveraging machine learning. Here, we train a Gaussian process (GP) model on Kirkwood\u2013Buff Integrals (KBIs) to establish a predictive link between KBIs and activity coefficients\u2014a key thermodynamic quantity encoding deviations from ideality. Through the incorporation of KBI trends, the GP model leads to the prediction of the activity coefficients of two new systems without prior knowledge of their phase behavior, eliminating the need for direct coexistence simulations and significantly reducing computational cost. This framework has broad applicability in computational thermodynamics, offering a scalable strategy for studying complex mixtures with tunable interactions.<\/jats:p>","DOI":"10.1063\/5.0286520","type":"journal-article","created":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T10:44:00Z","timestamp":1761302640000},"update-policy":"https:\/\/doi.org\/10.1063\/aip-crossmark-policy-page","source":"Crossref","is-referenced-by-count":1,"title":["Accelerating phase diagram construction through activity coefficient prediction"],"prefix":"10.1063","volume":"163","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5095-4361","authenticated-orcid":false,"given":"Mohsen","family":"Farshad","sequence":"first","affiliation":[{"name":"Department of Chemical and Biomolecular Engineering, University of Notre Dame 1 , Notre Dame, Indiana 46556,","place":["USA"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8731-9393","authenticated-orcid":false,"given":"Fathya Y. 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