ArticleNature communications2020
A general-purpose machine-learning force field for bulk and nanostructured phosphorus.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
35 citing papers in PubMed, 146 citations in OpenAlex.
- Improving Reliability of Machine Learning Interatomic Potentials with Physics-Informed Pretraining.Journal of chemical information and modeling · 2026Article
- Accurate, transferable, and verifiable machine-learned interatomic potentials for layered materials.Nature communications · 2026Article
- Spectroscopy-informed XANES-PXRD framework for multi-property prediction and structure inference.Chemical science · 2026Article
- Medium-Range Structural Order in Amorphous Arsenic.Journal of the American Chemical Society · 2026Article
- The Zintl-Klemm Concept in the Amorphous State: A Case Study of Na-P Battery Anodes.Angewandte Chemie (International ed. in English) · 2026Article
- Toward a thermodynamic stability order of the phosphorus allotropes.RSC advances · 2025Article
- Full-cycle device-scale simulations of memory materials with a tailored atomic-cluster-expansion potential.Nature communications · 2025Article
- An automated framework for exploring and learning potential-energy surfaces.Nature communications · 2025Article
- Artificial Intelligence Paradigms for Next-Generation Metal-Organic Framework Research.Journal of the American Chemical Society · 2025Review
- Opportunities and challenges in modelling ligand adsorption on semiconductor nanocrystals.Communications chemistry · 2025Review
- CaProtein science : a publication of the Protein Society · 2025Article
- Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction.npj computational materials · 2025Article
- Machine learning and data-driven methods in computational surface and interface science.npj computational materials · 2025Review
- Global Optimization of Molybdenum Subnanoclusters on Graphene: A Consistent Approach toward Catalytic Applications.ACS applied materials & interfaces · 2024Article
- Understanding Defect-Mediated Ion Migration in Semiconductors using Atomistic Simulations and Machine Learning.ACS materials Au · 2024Review
- Article
- Mechanism of Charge Transport in Lithium Thiophosphate.Chemistry of materials : a publication of the American Chemical Society · 2024Article
- DPA-2: a large atomic model as a multi-task learner.npj computational materials · 2024Article
- A unified moment tensor potential for silicon, oxygen, and silica.npj computational materials · 2024Article
- Physics-Inspired Equivariant Descriptors of Nonbonded Interactions.The journal of physical chemistry letters · 2023Article
Corrections and comments
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Authors and funding
3 authors at 3 institutions in 2 countries.
Funding
Abstract
Elemental phosphorus is attracting growing interest across fundamental and applied fields of research. However, atomistic simulations of phosphorus have remained an outstanding challenge. Here, we show that a universally applicable force field for phosphorus can be created by machine learning (ML) from a suitably chosen ensemble of quantum-mechanical results. Our model is fitted to density-functional theory plus many-body dispersion (DFT + MBD) data; its accuracy is demonstrated for the exfoliation of black and violet phosphorus (yielding monolayers of "phosphorene" and "hittorfene"); its transferability is shown for the transition between the molecular and network liquid phases. An application to a phosphorene nanoribbon on an experimentally relevant length scale exemplifies the power of accurate and flexible ML-driven force fields for next-generation materials modelling. The methodology promises new insights into phosphorus as well as other structurally complex, e.g., layered solids that are relevant in diverse areas of chemistry, physics, and materials science.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.