{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T20:16:27Z","timestamp":1788466587540,"version":"build-2803163510"},"reference-count":23,"publisher":"American Chemical Society (ACS)","issue":"35","license":[{"start":{"date-parts":[[2026,8,22]],"date-time":"2026-08-22T00:00:00Z","timestamp":1787356800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,22]],"date-time":"2026-08-22T00:00:00Z","timestamp":1787356800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,22]],"date-time":"2026-08-22T00:00:00Z","timestamp":1787356800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-045"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,9,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Screening carbon-dioxide capture media requires models that examine diverse molecular environments while retaining interpretable host\u2013CO2 descriptions. This work presents a nonreactive finite-complex workflow in which 11,053 parent environments, indexed by the Kulkarni Universal Interaction Descriptor (K-UID), a scale-invariant topological address system, were expanded into 975,578 encounter geometries by appending rigid CO2 at directed and random positions without changing parent coordinates. A reference subset of 149,935 complexes was relaxed using Geometry, Frequency, and Noncovalent eXtended Tight Binding (GFN2-xTB) and processed using an in-house implementation of real-space noncovalent interaction analysis and the published Kulkarni noncovalent interaction fingerprint (KNF) framework. The calculations provided the nine KNF components, the Non-Covalent Interaction Score (SNCI), measuring cumulative attractive interaction load, and the area-weighted COSMO surface-charge variance (VCDI), from which the plotted interaction-character coordinate SCDI = 1 \u2013 VCDI was derived. A machine-learning surrogate combined a multioutput multilayer perceptron with a Light Gradient Boosting Machine residual correction to learn the 11 quantities from unoptimized Cartesian structures and placement metadata. On host-grouped evaluation, the model achieved R2 = 0.990 for SNCI and a seven-split mean R2 of 0.8485 \u00b1 0.0127. It reproduced the two-dimensional SNCI\u2013SCDI screening map with 0.8935 region accuracy and 0.8932 macro-averaged F1 score across 149,856 matched perturbations. The map separated high-interaction-load systems by interaction character and revealed chemically organized host families. The workflow prioritizes noncovalent molecular encounters for subsequent condensed-phase simulation or experiment; it does not model covalent CO2 chemistry or directly predict bulk absorption capacity.<\/jats:p>","DOI":"10.1021\/acs.jpcb.6c03503","type":"journal-article","created":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T23:55:11Z","timestamp":1787529311000},"page":"8916-8931","source":"Crossref","is-referenced-by-count":0,"title":["Beyond the Single\nPose: Perturbation-Resolved Mapping\nof Host\u2013CO2 Interaction Landscapes with Interpretable\nMachine Learning"],"prefix":"10.1021","volume":"130","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-2931-1461","authenticated-orcid":true,"given":"Prasanna P.","family":"Kulkarni","sequence":"first","affiliation":[{"name":"Institute of Chemical Technology , Mumbai, Marathwada Campus , , ,","place":["Jalna, Maharashtra, India, 431203"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2026,8,22]]},"reference":[{"key":"2026090315290344600_cit1","unstructured":"Climate Change 2023: Synthesis Report (Full Volume) Contribution\nof Working Groups I, II and III to the Sixth Assessment Report of\nthe Intergovernmental Panel on Climate Change; Intergovernmental Panel on Climate Change, 2023."},{"key":"2026090315290344600_cit2","unstructured":"Carbon Capture, Utilisation\nand Storage. https:\/\/www.iea.org\/energy-system\/carbon-capture-utilisation-and-storage, Accessed July 22, 2026."},{"key":"2026090315290344600_cit3","doi-asserted-by":"publisher","first-page":"1652","DOI":"10.1126\/science.1176731","article-title":"Amine Scrubbing\nfor CO2 Capture","volume":"325","author":"Rochelle","year":"2009","journal-title":"Science"},{"key":"2026090315290344600_cit4","doi-asserted-by":"publisher","first-page":"109299","DOI":"10.1016\/j.rser.2019.109299","article-title":"Role of Solvents in CO2 Capture Processes: The Review\nof Selection and Design Methods","volume":"114","author":"N Borhani","year":"2019","journal-title":"Renew. 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Eng."},{"key":"2026090315290344600_cit12","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1038\/s41586-018-0337-2","article-title":"Machine Learning\nfor Molecular and Materials Science","volume":"559","author":"Butler","year":"2018","journal-title":"Nature"},{"key":"2026090315290344600_cit13","article-title":"SchNet: A Continuous-Filter Convolutional\nNeural Network for Modeling\nQuantum Interactions","author":"Schu\u0308tt","year":"2017","journal-title":"Adv. Neural Inf. Process.\nSyst."},{"key":"2026090315290344600_cit14","doi-asserted-by":"publisher","first-page":"6498","DOI":"10.1021\/ja100936w","article-title":"Revealing Noncovalent Interactions","volume":"132","author":"Johnson","year":"2010","journal-title":"J. Am. Chem. 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