001050182 001__ 1050182 001050182 005__ 20260112172432.0 001050182 020__ $$a978-3-7258-5909-2 001050182 037__ $$aFZJ-2025-05878 001050182 1001_ $$0P:(DE-Juel1)144509$$aChihaia, Viorel$$b0$$eEditor 001050182 245__ $$aMolecular Simulations of Energy Materials 001050182 260__ $$aBasel$$bMDPI$$c2025 001050182 300__ $$a134 001050182 3367_ $$2BibTeX$$aBOOK 001050182 3367_ $$0PUB:(DE-HGF)3$$2PUB:(DE-HGF)$$aBook$$bbook$$mbook$$s1768234918_24112 001050182 3367_ $$2DataCite$$aOutput Types/Book 001050182 3367_ $$2ORCID$$aBOOK 001050182 3367_ $$01$$2EndNote$$aBook 001050182 3367_ $$2DRIVER$$abook 001050182 520__ $$aThe continuous rise in global energy demand, together with the depletion of conventional resources, places increasing pressure on the scientific community to develop materials that enable clean, efficient, and sustainable energy generation, storage, and utilization. The phenomena underlying these processes are inherently complex, often occurring simultaneously across multiple spatial (from atomic to macroscopic) and temporal (from femtoseconds to years) scales. While experimental investigations remain fundamental to the study of energy and environmental systems, our understanding of material behavior under extreme conditions—particularly at the microscopic level—remains limited. Computational molecular science has therefore become an indispensable complement, offering powerful tools to analyze and describe the mechanisms governing these phenomena.Molecular simulations, including static calculations, Molecular Dynamics, and Monte Carlo methods, rely on intra- and intermolecular forces determined at quantum, classical, or coarse-grained levels. These approaches provide essential insights into the structure and dynamics of energy materials and help interpret experimental data. The integration of particle-based and continuum methods within multiscale frameworks further enhances our ability to capture the hierarchical nature of processes in energy and environmental materials. Collectively, these computational methodologies form a vital foundation for understanding, predicting, and optimizing the behavior of energy materials. 001050182 536__ $$0G:(DE-HGF)POF4-5111$$a5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0 001050182 588__ $$aDataset connected to CrossRef Book 001050182 7001_ $$0P:(DE-Juel1)132274$$aSutmann, Godehard$$b1$$eEditor$$ufzj 001050182 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)132274$$aForschungszentrum Jülich$$b1$$kFZJ 001050182 9131_ $$0G:(DE-HGF)POF4-511$$1G:(DE-HGF)POF4-510$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5111$$aDE-HGF$$bKey Technologies$$lEngineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action$$vEnabling Computational- & Data-Intensive Science and Engineering$$x0 001050182 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0 001050182 980__ $$abook 001050182 980__ $$aEDITORS 001050182 980__ $$aVDBINPRINT 001050182 980__ $$aI:(DE-Juel1)JSC-20090406 001050182 980__ $$aUNRESTRICTED