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Hierarchically Structured Allotropes of Phosphorus from Data-Driven Exploration

Published version
Peer-reviewed

Type

Article

Change log

Authors

Deringer, VL 
Pickard, CJ 
Proserpio, DM 

Abstract

© 2020 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA The discovery of materials is increasingly guided by quantum-mechanical crystal-structure prediction, but the structural complexity in bulk and nanoscale materials remains a bottleneck. Here we demonstrate how data-driven approaches can vastly accelerate the search for complex structures, combining a machine-learning (ML) model for the potential-energy surface with efficient, fragment-based searching. We use the characteristic building units observed in Hittorf's and fibrous phosphorus to seed stochastic (“random”) structure searches over hundreds of thousands of runs. Our study identifies a family of hierarchically structured allotropes based on a P8 cage as principal building unit, including one-dimensional (1D) single and double helix structures, nanowires, and two-dimensional (2D) phosphorene allotropes with square-lattice and kagome topologies. These findings yield new insight into the intriguingly diverse structural chemistry of phosphorus, and they provide an example for how ML methods may, in the long run, be expected to accelerate the discovery of hierarchical nanostructures.

Description

Keywords

3403 Macromolecular and Materials Chemistry, 34 Chemical Sciences, Generic health relevance

Journal Title

Angewandte Chemie

Conference Name

Journal ISSN

0044-8249
1521-3757

Volume Title

132

Publisher

Wiley
Sponsorship
Engineering and Physical Sciences Research Council (EP/P022596/1)
Royal Society (WM150023)