{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T12:13:15Z","timestamp":1789042395328,"version":"build-2803163510"},"reference-count":28,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T00:00:00Z","timestamp":1788998400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Shaoguan Nanling Team Project"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Polymers"],"abstract":"<jats:p>MQ silicone resins are widely used reinforcing fillers for addition-curing liquid silicone rubber (LSR); however, establishing a quantitative composition\u2013property relationship remains challenging because published data are fragmented across matrix chemistries, crosslinkers, catalyst systems and testing standards. Here we present a machine-learning-guided workflow integrating literature data mining, interpretable random-forest (RF) modelling and independent experimental validation for the design of MQ-reinforced LSR. An RF model trained on 55 curated literature points spanning RTV and LSR systems, using four physically motivated descriptors (MQ content, M\/Q ratio, curing system and vinyl content), yielded leave-one-out coefficient of determination (R2) values of 0.741 for tensile strength (TS) and 0.730 for Shore A hardness (HA), with mean absolute errors of 0.63 MPa and 8.95 ShA, respectively. Feature-importance and partial-dependence analyses identified MQ content as the dominant descriptor. Guided by the model, seven LSR formulations (vinyl content 4 wt%, M\/Q = 0.8, loading 5\u201335 wt%) were designed and fully characterised: the model reproduced the measured TS and HA for all seven formulations within the corresponding training mean-absolute-error tolerance, whereas elongation at break (EB), whose prediction is substantially weaker (LOO R2 \u2248 0), was captured only as a qualitative trend with respect to MQ loading. This workflow demonstrates that a modest, curated literature dataset, mined by an interpretable ML model, can support formulation design and independent experimental validation\u2014an efficient, low-cost alternative to trial-and-error optimisation.<\/jats:p>","DOI":"10.3390\/polym18182204","type":"journal-article","created":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T11:38:39Z","timestamp":1789040319000},"page":"2204","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning-Guided Design of MQ Silicone Resin Reinforced Addition-Curing Silicone Rubber: From Literature Data Mining to Experimental Validation"],"prefix":"10.3390","volume":"18","author":[{"given":"Tianyi","family":"Xu","sequence":"first","affiliation":[{"name":"Guangzhou Institute of Chemistry, Chinese Academy of Sciences, Guangzhou 510650, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuewen","family":"Huang","sequence":"additional","affiliation":[{"name":"Guangzhou Institute of Chemistry, Chinese Academy of Sciences, Guangzhou 510650, China"},{"name":"CAS Engineering Laboratory for Special Fine Chemicals, Guangzhou 510650, China"},{"name":"CASH GCC Fine Chemicals Incubator (Nanxiong) Co., Ltd., Nanxiong 512400, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Liu","sequence":"additional","affiliation":[{"name":"Guangzhou Institute of Chemistry, Chinese Academy of Sciences, Guangzhou 510650, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dan","family":"Qiu","sequence":"additional","affiliation":[{"name":"Guangzhou Institute of Chemistry, Chinese Academy of Sciences, Guangzhou 510650, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan","family":"Yuan","sequence":"additional","affiliation":[{"name":"Guangzhou Institute of Chemistry, Chinese Academy of Sciences, Guangzhou 510650, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuaitao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Guangzhou Institute of Chemistry, Chinese Academy of Sciences, Guangzhou 510650, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Wang","sequence":"additional","affiliation":[{"name":"Guangzhou Institute of Chemistry, Chinese Academy of Sciences, Guangzhou 510650, China"},{"name":"CAS Engineering Laboratory for Special Fine Chemicals, Guangzhou 510650, China"},{"name":"CASH GCC Shaoguan Research Institute of Advanced Materials, Shaoguan 512400, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1007\/s40009-013-0150-2","article-title":"A review on silicone rubber","volume":"36","author":"Shit","year":"2013","journal-title":"Natl. 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