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Updated: Dec 19, 2025

Author Spotlight: Accelerating Discovery in Microporous Material Chemistry

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Published on: October 6, 2023

Hierarchically Structured Allotropes of Phosphorus from Data-Driven Exploration.

Volker L Deringer¹, Chris J Pickard^2,3, Davide M Proserpio^4,5

¹Department of Chemistry, Inorganic Chemistry Laboratory, University of Oxford, Oxford, OX1 3QR, UK.

Angewandte Chemie (International Ed. in English)

|June 5, 2020

Summary

This summary is machine-generated.

This study accelerates the discovery of complex material structures using machine learning (ML) and fragment-based searching. Researchers identified novel hierarchical phosphorus allotropes, including 1D helices and 2D phosphorene, advancing materials science.

Keywords:

crystal-structure prediction machine learning nanowires phosphorene

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Area of Science:

Materials Science
Computational Chemistry
Nanotechnology

Background:

Crystal-structure prediction is crucial for materials discovery but hindered by structural complexity.
Existing methods struggle with the vast structural diversity in bulk and nanoscale materials.

Purpose of the Study:

To accelerate the search for complex material structures using data-driven approaches.
To explore the structural chemistry of phosphorus and discover new allotropes.

Main Methods:

Combined a machine-learning (ML) model for potential-energy surface calculations with fragment-based searching.
Utilized characteristic building units from known phosphorus structures to seed stochastic searches.
Performed hundreds of thousands of structure searches to identify novel configurations.

Main Results:

Identified a family of hierarchically structured phosphorus allotropes.
Discovered new one-dimensional (1D) single and double helix structures and nanowires.
Found new two-dimensional (2D) phosphorene allotropes with square-lattice and kagome topologies.

Conclusions:

The study reveals the diverse structural chemistry of phosphorus.
Demonstrates the potential of ML methods to accelerate the discovery of complex hierarchical nanostructures.
Provides a new framework for exploring complex material structures.