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Atomic structure of boron resolved using machine learning and global sampling

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Abstract

Boron crystals, despite their simple composition, must rank top for complexity: even the atomic structure of the ground state of β-B remains uncertain after 60 years’ study. This makes it difficult to understand the many exotic photoelectric properties of boron. The presence of self-doping atoms in the crystal interstitial sites forms an astronomical configurational space, making the determination of the real configuration virtually impossible using current techniques. Here, by combining machine learning with the latest stochastic surface walking (SSW) global optimization, we explore for the first time the potential energy surface of β-B, revealing 15 293 distinct configurations out of the 2 × 105 minima visited, and reveal the key rules governing the filling of the interstitial sites. This advance is only allowed by the construction of an accurate and efficient neural network (NN) potential using a new series of structural descriptors that can sensitively discriminate the complex boron bonding environment. We show that, in contrast to the conventional views on the numerous energy-degenerate configurations, only 40 minima of β-B are identified to be within 7 meV per atom in energy above the global minimum of β-B, most of them having been discovered for the first time. These low energy structures are classified into three types of skeletons and six patterns of doping configurations, with a clear preference for a few characteristic interstitial sites. The observed β-B and its properties are influenced strongly by a particular doping site, the B19 site that neighbors the B18 site, which has an exceptionally large vibrational entropy. The configuration with this B19 occupancy, which ranks only 15th at 0 K, turns out to be dominant at high temperatures. Our results highlight the novel SSW-NN architecture as the leading problem solver for complex material phenomena, which would then expedite substantially the building of a material genome database.

Graphical abstract: Atomic structure of boron resolved using machine learning and global sampling

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Publication details

The article was received on 02 Aug 2018, accepted on 11 Sep 2018 and first published on 11 Sep 2018


Article type: Edge Article
DOI: 10.1039/C8SC03427C
Citation: Chem. Sci., 2018, Advance Article
  • Open access: Creative Commons BY-NC license
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    Atomic structure of boron resolved using machine learning and global sampling

    S. Huang, C. Shang, P. Kang and Z. Liu, Chem. Sci., 2018, Advance Article , DOI: 10.1039/C8SC03427C

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