LEADER 04455nam 22008655 450 001 9910726286403321 005 20230522211103.0 010 $a981-9903-93-9 024 7 $a10.1007/978-981-99-0393-1 035 $a(MiAaPQ)EBC30550686 035 $a(Au-PeEL)EBL30550686 035 $a(OCoLC)1380464879 035 $a(DE-He213)978-981-99-0393-1 035 $a(BIP)087519975 035 $a(PPN)270618864 035 $a(EXLCZ)9926760996400041 100 $a20230522d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine Learning for Advanced Functional Materials$b[electronic resource] /$fedited by Nirav Joshi, Vinod Kushvaha, Priyanka Madhushri 205 $a1st ed. 2023. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2023. 215 $a1 online resource (306 pages) 311 08$aPrint version: Joshi, Nirav Machine Learning for Advanced Functional Materials Singapore : Springer,c2023 9789819903924 327 $aSolar Cells and Relevant Machine Learning -- Machine learning-driven gas identification in gas sensors -- Recent advances in Machine Learning for electrochemical, optical, and gas sensors -- Machine Learning in Wearable Healthcare Devices -- A Machine Learning approach in wearable Technologies -- The application of novel functional materials to machine learning -- Potential of Machine Learning Algorithms in Material Science: Predictions in design, properties and applications of novel functional materials -- Perovskite Based Materials for Photovoltaic Applications: A Machine Learning Approach -- A review of the high-performance gas sensors using machine learning -- Machine Learning For Next?Generation Functional Materials -- Contemplation of Photocatalysis Through Machine Learning -- Discovery of Novel Photocatalysts using Machine Learning Approach -- Machine Learning In Impedance Based Sensors. 330 $aThis book presents recent advancements of machine learning methods and their applications in material science and nanotechnologies. It provides an introduction to the field and for those who wish to explore machine learning in modeling as well as conduct data analyses of material characteristics. The book discusses ways to enhance the material?s electrical and mechanical properties based on available regression methods for supervised learning and optimization of material attributes. In summary, the growing interest among academics and professionals in the field of machine learning methods in functional nanomaterials such as sensors, solar cells, and photocatalysis is the driving force for behind this book. This is a comprehensive scientific reference book on machine learning for advanced functional materials and provides an in-depth examination of recent achievements in material science by focusing on topical issues using machine learning methods. 606 $aOptics 606 $aMachine learning 606 $aMaterials 606 $aDetectors 606 $aTumor markers 606 $aPhotonics 606 $aOptical engineering 606 $aOptics and Photonics 606 $aMachine Learning 606 $aSensors and biosensors 606 $aTumour Biomarkers 606 $aPhotonics and Optical Engineering 606 $aPhotonic Devices 610 $aArtificial Intelligence 610 $aMaterials 610 $aOncology 610 $aMicrowaves 610 $aOptics 610 $aComputers 610 $aTechnology & Engineering 610 $aMedical 610 $aScience 615 0$aOptics. 615 0$aMachine learning. 615 0$aMaterials. 615 0$aDetectors. 615 0$aTumor markers. 615 0$aPhotonics. 615 0$aOptical engineering. 615 14$aOptics and Photonics. 615 24$aMachine Learning. 615 24$aSensors and biosensors. 615 24$aTumour Biomarkers. 615 24$aPhotonics and Optical Engineering. 615 24$aPhotonic Devices. 676 $a620.110285631 700 $aJoshi$b Nirav$01343562 701 $aKushvaha$b Vinod$01359352 701 $aMadhushri$b Priyanka$01359353 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910726286403321 996 $aMachine Learning for Advanced Functional Materials$93373789 997 $aUNINA