ArticleAngewandte Chemie (International ed. in English)2020
Hierarchically Structured Allotropes of Phosphorus from Data-Driven Exploration.
Article in Angewandte Chemie (International ed. in English), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- The Zintl-Klemm Concept in the Amorphous State: A Case Study of Na-P Battery Anodes.Angewandte Chemie (International ed. in English) · 2026Article
- Phosphorus-lithium double-helix nanoribbons.Science advances · 2025Article
- Toward a thermodynamic stability order of the phosphorus allotropes.RSC advances · 2025Article
- Direct synthesis of a semiconductive double-helical phosphorus allotrope in a metal-organic framework.Nature communications · 2025Article
- Photo-Assisted Bottom-Up Synthesis of Orange Phosphorus.Angewandte Chemie (International ed. in English) · 2025Article
- Recent progress in realizing novel one-dimensional polymorphs via nanotube encapsulation.Nano convergence · 2024Review
- Structure and Bonding in Amorphous Red Phosphorus.Angewandte Chemie (International ed. in English) · 2023Article
- Machine learning the metastable phase diagram of covalently bonded carbon.Nature communications · 2022Article
- Gaussian Process Regression for Materials and Molecules.Chemical reviews · 2021Article
- Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems.Chemical reviews · 2021Article
- Insertion of Phosphenium Ions into a Bicyclo[1.1.0]Tetraphosphabutane Iron Complex.Molecules (Basel, Switzerland) · 2021Article
- A general-purpose machine-learning force field for bulk and nanostructured phosphorus.Nature communications · 2020Article
- Hierarchically Structured Allotropes of Phosphorus from Data-Driven Exploration.Angewandte Chemie (International ed. in English) · 2020Article
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3 authors.
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Abstract
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.
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