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      Structure prediction drives materials discovery

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          Machine learning for molecular and materials science

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            Crystal structure prediction using ab initio evolutionary techniques: principles and applications.

            We have developed an efficient and reliable methodology for crystal structure prediction, merging ab initio total-energy calculations and a specifically devised evolutionary algorithm. This method allows one to predict the most stable crystal structure and a number of low-energy metastable structures for a given compound at any P-T conditions without requiring any experimental input. Extremely high (nearly 100%) success rate has been observed in a few tens of tests done so far, including ionic, covalent, metallic, and molecular structures with up to 40 atoms in the unit cell. We have been able to resolve some important problems in high-pressure crystallography and report a number of new high-pressure crystal structures (stable phases: epsilon-oxygen, new phase of sulphur, new metastable phases of carbon, sulphur and nitrogen, stable and metastable phases of CaCO3). Physical reasons for the success of this methodology are discussed.
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              Synthesis of borophenes: Anisotropic, two-dimensional boron polymorphs.

              At the atomic-cluster scale, pure boron is markedly similar to carbon, forming simple planar molecules and cage-like fullerenes. Theoretical studies predict that two-dimensional (2D) boron sheets will adopt an atomic configuration similar to that of boron atomic clusters. We synthesized atomically thin, crystalline 2D boron sheets (i.e., borophene) on silver surfaces under ultrahigh-vacuum conditions. Atomic-scale characterization, supported by theoretical calculations, revealed structures reminiscent of fused boron clusters with multiple scales of anisotropic, out-of-plane buckling. Unlike bulk boron allotropes, borophene shows metallic characteristics that are consistent with predictions of a highly anisotropic, 2D metal.
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                Author and article information

                Journal
                Nature Reviews Materials
                Nat Rev Mater
                Springer Science and Business Media LLC
                2058-8437
                May 2019
                April 4 2019
                May 2019
                : 4
                : 5
                : 331-348
                Article
                10.1038/s41578-019-0101-8
                1205b351-dde6-4e32-b761-c86590337ea2
                © 2019

                http://www.springer.com/tdm

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