LEADER 04238nam 22006855 450 001 9910993940403321 005 20250407170314.0 010 $a3-031-78736-6 024 7 $a10.1007/978-3-031-78736-2 035 $a(CKB)38251636300041 035 $a(DE-He213)978-3-031-78736-2 035 $a(MiAaPQ)EBC31986401 035 $a(Au-PeEL)EBL31986401 035 $a(OCoLC)1523373298 035 $a(EXLCZ)9938251636300041 100 $a20250407d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMaterials Informatics I $eMethods /$fedited by Kunal Roy, Arkaprava Banerjee 205 $a1st ed. 2025. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2025. 215 $a1 online resource (XVII, 288 p. 66 illus., 53 illus. in color.) 225 1 $aChallenges and Advances in Computational Chemistry and Physics,$x2542-4483 ;$v39 311 08$a3-031-78735-8 327 $aPart 1. Introduction -- Introduction to Materials Informatics -- Introduction to Cheminformatics for Predictive Modeling -- Introduction to machine learning for predictive modeling of organic materials -- Quantitative Structure-Property Relationships (QSPR) for Materials Science -- Part 2. Methods and Tools -- Quantitative Structure-Property Relationships (QSPR) and Machine Learning (ML) Models for Materials Science -- Optimising Materials Properties with Minimal Data: Lessons from Vanadium Catalyst Modelling -- In silico QSPR studies based on CDFT and IT descriptors -- Applications of quantitative read-across structure-property relationship (q-RASPR) modeling in the field of materials science -- Machine Learning algorithms for applications in Materials Science I -- Machine Learning algorithms for applications in Materials Science II -- Structure-property modeling of quantum-theoretic properties of benzenoid hydrocarbons by means of connection-related graphical descriptors -- Machine learning tools and Web services for Materials Science modelling. 330 $aThis contributed volume explores the integration of machine learning and cheminformatics within materials science, focusing on predictive modeling techniques. It begins with foundational concepts in materials informatics and cheminformatics, emphasizing quantitative structure-property relationships (QSPR). The volume then presents various methods and tools, including advanced QSPR models, quantitative read-across structure-property relationship (q-RASPR) models, optimization strategies with minimal data, and in silico studies using different descriptors. Additionally, it explores machine learning algorithms and their applications in materials science, alongside innovative modeling approaches for quantum-theoretic properties. Overall, the book serves as a comprehensive resource for understanding and applying machine learning in the study and development of advanced materials and is a useful tool for students, researchers and professionals working in these areas. 410 0$aChallenges and Advances in Computational Chemistry and Physics,$x2542-4483 ;$v39 606 $aCheminformatics 606 $aMaterials 606 $aChemistry 606 $aComputer simulation 606 $aMachine learning 606 $aArtificial intelligence 606 $aCheminformatics 606 $aComputational Design Of Materials 606 $aMachine Learning 606 $aArtificial Intelligence 615 0$aCheminformatics. 615 0$aMaterials. 615 0$aChemistry. 615 0$aComputer simulation. 615 0$aMachine learning. 615 0$aArtificial intelligence. 615 14$aCheminformatics. 615 24$aComputational Design Of Materials. 615 24$aMachine Learning. 615 24$aArtificial Intelligence. 676 $a542.85 702 $aRoy$b Kunal$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aBanerjee$b Arkaprava$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910993940403321 996 $aMaterials Informatics I$94375216 997 $aUNINA