Predicting superhard materials via a machine learning informed evolutionary structure search
نویسندگان
چکیده
منابع مشابه
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The recent development in the field of superhard materials with Vickers hardness of >40 GPa is reviewed. Two basic approaches are outlined including the intrinsic superhard materials, such as diamond, cubic boron nitride, C3N4, carbonitrides, etc. and extrinsic, nanostructured materials for which superhardness is achieved by an appropriate design of their microstructure. The theoretically predi...
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Artem R. Oganov, Department of Geosciences, Center for Materials by Design, Institute for Advanced Computational Science, Stony Brook University, Stony Brook, NY, USA; Moscow Institute of Physics and Technology, Dolgoprudny city, Moscow Region, Russian Federation; Northwestern Polytechnical University, Xi’an, China Andriy O. Lyakhov and Qiang Zhu, Department of Geosciences, Stony Brook Universi...
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ژورنال
عنوان ژورنال: npj Computational Materials
سال: 2019
ISSN: 2057-3960
DOI: 10.1038/s41524-019-0226-8