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For chromophores in the condensed phase, these spectra, which incorporate the coupling between electronic excitation and molecular and solvent nuclear motions, can be simulated using excitation energies obtained from molecular dynamics simulations of the chromophore and solvent. Here, we present a hybrid scheme that exploits machine learning and physically informed spectral densities to show that as few as 25 ground and excited state energetic gradient calculations can be used to construct models that accurately predict environment-influenced vibronic coupling in optical spectra. We demonstrate our approach for the green fluorescent protein chromophore in water and the cresyl violet chromophore in methanol. We show that our hybrid approach, employing a machine learning model for the high-frequency spectral density and an ab\u00a0initio parameterized Debye spectral density for the low-frequency, results in a systematic improvement of the optical absorption lineshape, leading to a simple machine learning scheme that can be used for the simulation of spectral densities and optical spectra.<\/jats:p>","DOI":"10.1063\/5.0321138","type":"journal-article","created":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T10:38:27Z","timestamp":1775039907000},"update-policy":"https:\/\/doi.org\/10.1063\/aip-crossmark-policy-page","source":"Crossref","is-referenced-by-count":0,"title":["Efficient simulation of optical spectra via machine learning and physical decomposition of environmental effects"],"prefix":"10.1063","volume":"164","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2322-3533","authenticated-orcid":false,"given":"Andrew","family":"Snider","sequence":"first","affiliation":[{"name":"Department of Chemistry and Biochemistry, University of California Merced 1 , Merced, California 95343,","place":["USA"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0767-4238","authenticated-orcid":false,"given":"Michael S.","family":"Chen","sequence":"additional","affiliation":[{"name":"Simons Center for Computational Physical Chemistry, Department of Chemistry, New York University 2 , New York, New York 10003,","place":["USA"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2747-0518","authenticated-orcid":false,"given":"Thomas E.","family":"Markland","sequence":"additional","affiliation":[{"name":"Department of Chemistry, Stanford University 3 , Stanford, California 94305,","place":["USA"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4905-9113","authenticated-orcid":false,"given":"Christine M.","family":"Isborn","sequence":"additional","affiliation":[{"name":"Department of Chemistry and Biochemistry, University of California Merced 1 , Merced, California 95343,","place":["USA"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"317","published-online":{"date-parts":[[2026,4,1]]},"reference":[{"key":"2026040106382180000_c1","doi-asserted-by":"publisher","first-page":"2072","DOI":"10.1021\/ct301107m","article-title":"Insights for an accurate comparison of computational data to experimental absorption and emission spectra: Beyond the vertical transition approximation","volume":"9","year":"2013","journal-title":"J.\u00a0Chem. 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