03904nam 22006375 450 991048423710332120251113182940.03-030-68310-910.1007/978-3-030-68310-8(CKB)4100000011807057(MiAaPQ)EBC6531655(Au-PeEL)EBL6531655(OCoLC)1244623672(PPN)254719589(DE-He213)978-3-030-68310-8(EXLCZ)99410000001180705720210326d2021 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierArtificial Intelligence for Materials Science /edited by Yuan Cheng, Tian Wang, Gang Zhang1st ed. 2021.Cham :Springer International Publishing :Imprint: Springer,2021.1 online resource (231 pages)Springer Series in Materials Science,2196-2812 ;3123-030-68309-5 Chapter 1. Brief Introduction of the Machine Learning Method -- Chapter 2. Machine learning for high-entropy alloys -- Chapter 3. Two-way TrumpetNets and TubeNets for Identification of Material Parameters -- Chapter 4. Machine learning interatomic force fields for carbon allotropic materials -- Chapter 5. Genetic Algorithms -- Chapter 6. Accelerated Discovery of Thermoelectric Materials using Machine Learning -- Chapter 7. Thermal nanostructure design based on materials informatics. - Chapter 8. Machine Learning Accelerated Insights of Perovskite Materials.Machine learning methods have lowered the cost of exploring new structures of unknown compounds, and can be used to predict reasonable expectations and subsequently validated by experimental results. As new insights and several elaborative tools have been developed for materials science and engineering in recent years, it is an appropriate time to present a book covering recent progress in this field. Searchable and interactive databases can promote research on emerging materials. Recently, databases containing a large number of high-quality materials properties for new advanced materials discovery have been developed. These approaches are set to make a significant impact on human life and, with numerous commercial developments emerging, will become a major academic topic in the coming years. This authoritative and comprehensive book will be of interest to both existing researchers in this field as well as others in the materials science community who wish to takeadvantage of these powerful techniques. The book offers a global spread of authors, from USA, Canada, UK, Japan, France, Russia, China and Singapore, who are all world recognized experts in their separate areas. With content relevant to both academic and commercial points of view, and offering an accessible overview of recent progress and potential future directions, the book will interest graduate students, postgraduate researchers, and consultants and industrial engineers.Springer Series in Materials Science,2196-2812 ;312Materials scienceMachine learningMaterialsMaterials ScienceMachine LearningMaterials EngineeringMachine LearningMaterials science.Machine learning.Materials.Materials Science.Machine Learning.Materials Engineering.Machine Learning.620.110285Cheng YuanWang TianZhang GangMiAaPQMiAaPQMiAaPQBOOK9910484237103321Artificial intelligence for materials science1905207UNINA