Titlebook: Artificial Intelligence for Materials Science; Yuan Cheng,Tian Wang,Gang Zhang Book 2021 The Editor(s) (if applicable) and The Author(s),

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Brief Introduction of the Machine Learning Method,operties, which are critical for developing advanced materials. As big data involved in the simulations and the experiment, the understanding of the MGI remains challenging. The machine learning methods, which have been adopted in the MGI, developed with big data and artificial intelligence. This ch

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Machine Learning for High-Entropy Alloys,04, tremendous progresses and profound developments have been made in both the fundamental investigations and engineering applications. Unlike the conventional metallic alloys that typically only consist of one or two principal elements, HEA is composed of multi-principal elements in equimolar or ne

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Two-Way TrumpetNets and TubeNets for Identification of Material Parameters,r identification of material constants. An idealized case of laminated composites is considered that may have a large number of material constants need to be determined, including Young’s modulus, Poisson’s ratio, and shear modulus for different plies in the laminate. The TrumpetNets (or TubeNets) c

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Machine Learning Interatomic Force Fields for Carbon Allotropic Materials,ional cost, and transferability. In this mini review, we first summarize the disadvantages of traditional force field and the unique advantages of ML-based force field for molecular dynamics simulations. Then the basic workflow to develop the ML atomic force field is discussed in each step. Furtherm

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Thermal Nanostructure Design by Materials Informatics,of great use in a wide range of applications like thermal management, thermal barriers, and thermoelectrics. Due to the superhigh degree of freedoms in terms of atom types and structural configurations, traditional searching algorithm may be powerless to find the optimal nanostructures with limited