npj Computational Materials [electronic resource] / edited by Long-Qing Chen, Lidong Chen.

Інтелектуальна відповідальність: Вид матеріалу: Серіальне виданняПублікація: London : Nature Publishing Group UK : Imprint: Nature Portfolio.Опис: online resourceISSN:
  • 2057-3960
Тематика(и): Електронне місцезнаходження та доступ: Зведення: npj Computational Materials considers research that explores all aspects of applied computational approaches for the design of new materials, and for enhancing our understanding of existing materials. Topics covered by the journal include: Theoretical and simulation approaches for studying both organic and inorganic materials Emerging materials and technologies, including sustainable materials Prediction of material structure and properties Exploration of materials fundamentals, behaviors, degradation, and properties Integrative studies combining computational findings with experimental validation to examine behavior of materials High-throughput methods, both computational and experimental, for generating large datasets and materials data mining Design of materials using machine learning and artificial intelligence Development of methods and fundamental theory for materials modelling and informatics The journal offers more choice to Nature Portfolio authors who are seeking a fully open-access and more inclusive platform for publishing their work. The journal is led by computational materials experts who collaborate to cultivate high-quality research. As part of the npj Series, this journal focuses on fostering global partnerships with the research community and other Springer Nature journals.
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npj Computational Materials considers research that explores all aspects of applied computational approaches for the design of new materials, and for enhancing our understanding of existing materials. Topics covered by the journal include: Theoretical and simulation approaches for studying both organic and inorganic materials Emerging materials and technologies, including sustainable materials Prediction of material structure and properties Exploration of materials fundamentals, behaviors, degradation, and properties Integrative studies combining computational findings with experimental validation to examine behavior of materials High-throughput methods, both computational and experimental, for generating large datasets and materials data mining Design of materials using machine learning and artificial intelligence Development of methods and fundamental theory for materials modelling and informatics The journal offers more choice to Nature Portfolio authors who are seeking a fully open-access and more inclusive platform for publishing their work. The journal is led by computational materials experts who collaborate to cultivate high-quality research. As part of the npj Series, this journal focuses on fostering global partnerships with the research community and other Springer Nature journals.

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