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Machine learning interatomic potential with DFT accuracy for general grain boundaries in α-Fe

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摘要 To advance the development of high-strength polycrystalline metallic materials towards achieving carbon neutrality,it is essential to design materials in which the atomic level control of general grain boundaries(GGBs),which govern the material properties,is achieved.However,owing to the complex and diverse structures of GGBs,there have been no reports on interatomic potentials capable of reproducing them.
出处 《npj Computational Materials》 CSCD 2024年第1期509-524,共16页 计算材料学(英文)
基金 This work used computational resources of the Supercomputer Fugaku provided by Riken through the HPCI System Research Project(Project ID:hp230272) Thisworkwas partly supported by AccompanyingUser Support Program(【23Z-03,23Z-05,24H1-01】,Support content:【porting of application program,execution performance tuning】)performed by Research Organization for Information Science and Technology.
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