LEADER 05250nam 22007935 450 001 9910872195703321 005 20250408045003.0 010 $a9783031473036$b(electronic bk.) 010 $z9783031473029 024 7 $a10.1007/978-3-031-47303-6 035 $a(MiAaPQ)EBC31518235 035 $a(Au-PeEL)EBL31518235 035 $a(CKB)32658264000041 035 $a(DE-He213)978-3-031-47303-6 035 $a(MiAaPQ)EBC31521795 035 $a(Au-PeEL)EBL31521795 035 $a(EXLCZ)9932658264000041 100 $a20240704d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aComputational Design of Battery Materials /$fedited by Dorian A. H. Hanaor 205 $a1st ed. 2024. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2024. 215 $a1 online resource (589 pages) 225 1 $aTopics in Applied Physics,$x1437-0859 ;$v150 311 08$aPrint version: Hanaor, Dorian A. H. Computational Design of Battery Materials Cham : Springer International Publishing AG,c2024 9783031473029 320 $aIncludes bibliographical references. 327 $aBattery materials: Bringing it all together for tomorrow?s energy storage needs -- Atomistic Simulations of Battery Materials and Processes -- Ab Initio Interfacial Electrochemistry Applied to Understanding, Tuning and Designing Battery Chemistry -- Electrolyte-Electrode Interfaces: A Review of Computer Simulations -- Many-particle Na-ion dynamics in NaMPO4 olivine phosphates (M=Mn, Fe) -- Crystal Structure Prediction for Battery Materials -- Nanoscale Modelling of Substitutional Disorder in Battery Materials -- Machine learning methods for the design of battery manufacturing processes -- Machine learning methods for the design of battery manufacturing processes -- Applications of Ab Initio Molecular Dynamics for Modeling Batteries -- Forming a Chemically-Guided Basis for Cathode Materials with Reduced Biological Impact using Combined Density Functional Theory and Thermodynamics Modeling -- Oxygen Redox in Battery Cathodes: A Brief Overview -- Theoretical Investigation of Layered Anode Materials -- Design of Improved Cathode Materials by Intermixing Transition Metals in Sodium-Iron Sulphate and Sodium Manganate for Sodium-Ion Batteries -- Sodium Intercalation into Graphite and Graphene Complexes towards Advanced Sodium-Ion Battery Anode Materials -- Combining molecular simulations with modern experiments to design ionic liquid-based battery electrolytes -- Design of battery materials via defects and doping -- Role of Adsorption Energy in the Design of Battery Materials: A DFT Perspective. 330 $aThis book presents an essential survey of the state of the art in the application of diverse computational methods to the interpretation, prediction, and design of high-performance battery materials. Rechargeable batteries have become one of the most important technologies supporting the global transition from fossil fuels to renewable energy sources. Aided by the growth of high-performance computing and machine learning technologies, computational methods are being applied to design the battery materials of the future and pave the way to a more sustainable energy economy. In this contributed collection, leading battery material researchers from across the globe share their methods, insights, and expert knowledge in the application of computational methods for battery material design and interpretation. With chapters featuring an array of computational techniques applied to model the relevant properties of cathodes, anodes, and electrolytes, this book provides the ideal starting point for any researcher looking to integrate computational tools in the development of next-generation battery materials and processes. 410 0$aTopics in Applied Physics,$x1437-0859 ;$v150 606 $aMaterials 606 $aCatalysis 606 $aForce and energy 606 $aMaterials science$xData processing 606 $aElectric batteries 606 $aMachine learning 606 $aCondensed matter 606 $aChemistry, Physical and theoretical 606 $aMaterials for Energy and Catalysis 606 $aComputational Materials Science 606 $aBatteries 606 $aMachine Learning 606 $aTwo-dimensional Materials 606 $aTheoretical Chemistry 615 0$aMaterials. 615 0$aCatalysis. 615 0$aForce and energy. 615 0$aMaterials science$xData processing. 615 0$aElectric batteries. 615 0$aMachine learning. 615 0$aCondensed matter. 615 0$aChemistry, Physical and theoretical. 615 14$aMaterials for Energy and Catalysis. 615 24$aComputational Materials Science. 615 24$aBatteries. 615 24$aMachine Learning. 615 24$aTwo-dimensional Materials. 615 24$aTheoretical Chemistry. 676 $a621.312424 702 $aHanaor$b Dorian A. H. 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910872195703321 996 $aComputational Design of Battery Materials$94174033 997 $aUNINA