A machine learning and data-oriented quest to screen the degree of long-range order/disorder in polymeric materials

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dc.contributor.authorGuleryuz, Cihat
dc.contributor.authorSumrra, Sajjad H.
dc.contributor.authorHassan, Abrar U.
dc.contributor.authorMohyuddin, Ayesha
dc.contributor.authorMahmoud, Mohamed. H. H.
dc.date.accessioned2025-02-06T17:58:23Z
dc.date.available2025-02-06T17:58:23Z
dc.date.issued2025
dc.departmentAltınbaş Üniversitesien_US
dc.description.abstractIn the realm of polymeric materials, the delicate balance between long-range order and disorder dictates crystal properties, influencing their performance in various applications. To unravel this enigma, we embarked on a machine learning (ML) and data-driven quest, compiling 2500 data points from literature. By harnessing the power of Support Vector Machines (SVM) and Radial Basis Functions (RBF), we trained our model to decipher the intricate relationships between molecular descriptors and crystal properties. Introducing a novel pass/fail system, we screened polymers based on their calculated descriptors, revealing that combining multiple descriptors significantly enhances model performance. Identifying 1200 polymers that failed to meet crystallization requirements provides valuable insights for designing materials with tailored structural features. This groundbreaking study pioneers a data-oriented approach to understanding polymeric materials, paving the way for the creation of novel crystals with optimized properties. By uncovering the hidden patterns of order and disorder, we unlock the secrets of polymeric materials, revolutionizing their applications in various fields.en_US
dc.description.sponsorshipTaif University, Saudi Arabia [TU-DSPP-2024-93]en_US
dc.description.sponsorshipThe authors extend their appreciation to Taif University, Saudi Arabia, for supporting this work through project number (TU-DSPP-2024-93) .en_US
dc.identifier.doi10.1016/j.mtcomm.2025.111624
dc.identifier.issn2352-4928
dc.identifier.scopus2-s2.0-85215435271
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1016/j.mtcomm.2025.111624
dc.identifier.urihttps://hdl.handle.net/20.500.12939/5216
dc.identifier.volume43en_US
dc.identifier.wosWOS:001402833500001
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorGüleryüz, Cihat
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofMaterials Today Communicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzKA_WOS_20250206
dc.subjectMLen_US
dc.subjectCrystal propensityen_US
dc.subjectPolymeren_US
dc.subjectt-SNEen_US
dc.subjectSupport Vector Machineen_US
dc.titleA machine learning and data-oriented quest to screen the degree of long-range order/disorder in polymeric materialsen_US
dc.typeArticleen_US

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