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This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact

Energy storage --- hydrogen storage --- electrochemistry --- hierarchical structure --- nanostructure

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Systems studied in environmental science, due to their structure and the heterogeneity of the entities composing them, often exhibit complex dynamics that can only be captured by hybrid modeling approaches. While several concurrent definitions of “hybrid modeling” can be found in the literature, it is defined here broadly as the approach consisting in coupling existing modelling paradigms to achieve a more accurate or efficient representation of systems. The need for hybrid models generally arises from the necessity to overcome the limitation of a single modeling technique in terms of structural flexibility, capabilities, or computational efficiency. This book brings together experts in the field of hybrid modelling to demonstrate how this approach can address the challenge of representing the complexity of natural systems. Chapters cover applied examples as well as modeling methodology.Systems studied in environmental science, due to their structure and the heterogeneity of the entities composing them, often exhibit complex dynamics that can only be captured by hybrid modeling approaches. While several concurrent definitions of “hybrid modeling” can be found in the literature, it is defined here broadly as the approach consisting in coupling existing modelling paradigms to achieve a more accurate or efficient representation of systems. The need for hybrid models generally arises from the necessity to overcome the limitation of a single modeling technique in terms of structural flexibility, capabilities, or computational efficiency. This book brings together experts in the field of hybrid modelling to demonstrate how this approach can address the challenge of representing the complexity of natural systems. Chapters cover applied examples as well as modeling methodology.

system dynamics --- Multiscale integration --- simulation --- Mixed model --- hierarchical structure --- Combined approach --- machine learning --- agent-based modelling --- paradigm shift --- network

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Machine learning. --- Diagnostic imaging. --- Artificial intelligence --- Diagnostic Imaging. --- Deep Learning. --- Medical applications. --- Hierarchical Learning --- Learning, Deep --- Learning, Hierarchical --- Imaging, Diagnostic --- Imaging, Medical --- Medical Imaging --- Radiologic and Imaging Nursing --- Image Processing, Computer-Assisted --- Medicine --- Clinical imaging --- Medical diagnostic imaging --- Medical imaging --- Noninvasive medical imaging --- Diagnosis, Noninvasive --- Imaging systems in medicine --- Learning, Machine --- Machine theory --- Data processing

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Atomistic simulations, based on ab-initio and semi-empirical approaches, are nowadays widespread in many areas of physics, chemistry and, more recently, biology. Improved algorithms and increased computational power widened the areas of application of these computational methods to extended materials of technological interest, in particular allowing unprecedented access to the first-principles investigation of their electronic, optical, thermodynamical and mechanical properties, even where experiments are not available. However, for a big impact on the society, this rapidly growing field of computational approaches to materials science has to face the unfavourable scaling with the system size, and to beat the time-scale bottleneck. Indeed, many phenomena, such as crystal growth or protein folding for example, occur in a space/time scale which is normally out of reach of present simulations. Multi-scale approaches try to combine different scale algorithms along with matching procedures in order to bridge the gap between first-principles and continuum-level simulations. This Research Topic aims at the description of recent advances and applications in these two emerging fields of ab-inito and multi-scale materials modelling for both ground and excited states. A variety of theoretical and computational techniques are included along with the application of these methods to systems at increasing level of complexity, from nano to micro. Crossing the borders between several computational, theoretical and experimental techniques, this Research Topic aims to be of interest to a broad community, including experimental and theoretical physicists, chemists and engineers interested in materials research in a broad sense.

molecular dynamics simulations --- Classical and Quantum Monte Carlo methods --- ab-initio --- macromolecular complex --- Materials characterization --- Multiscale and Hierarchical modeling --- mechanical --- Electronic and optical properties of solids --- Carbon-based systems --- materials growth --- Density-functional

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Client - server systemen --- Computer architecture. Operating systems --- Traitement réparti --- Distributed processing. --- Traitement réparti --- 681.3*D47 Organization and design: batch processing systems; distributed systems; hierarchical design; interactive systems; real-time systems (Operating systems) --- Organization and design: batch processing systems; distributed systems; hierarchical design; interactive systems; real-time systems (Operating systems) --- Electronic data processing --- 681.3*D47 --- Distributed computer systems in electronic data processing --- Distributed computing --- Distributed processing in electronic data processing --- Computer networks --- Distributed processing --- Programming --- Electronic data processing - Distributed processing.