ArticleChemphyschem : a European journal of chemical physics and physical chemistry2017
Extracting Crystal Chemistry from Amorphous Carbon Structures.
Article in Chemphyschem : a European journal of chemical physics and physical chemistry, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- A superhard incompressible carbon allotrope with deformation-induced transformation to diamond.iScience · 2024Article
- MAGUS: machine learning and graph theory assisted universal structure searcher.National science review · 2023Review
- Into the Unknown: How Computation Can Help Explore Uncharted Material Space.Journal of the American Chemical Society · 2022Review
- Machine learning the metastable phase diagram of covalently bonded carbon.Nature communications · 2022Article
- Gaussian Process Regression for Materials and Molecules.Chemical reviews · 2021Article
- Big data and machine learning for materials science.Discover materials · 2021Review
- 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
- Topology-Based Machine Learning Strategy for Cluster Structure Prediction.The journal of physical chemistry letters · 2020Article
- Reactivity of Amorphous Carbon Surfaces: Rationalizing the Role of Structural Motifs in Functionalization Using Machine Learning.Chemistry of materials : a publication of the American Chemical Society · 2018Article
- Extracting Crystal Chemistry from Amorphous Carbon Structures.Chemphyschem : a European journal of chemical physics and physical chemistry · 2017Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Carbon allotropes have been explored intensively by ab initio crystal structure prediction, but such methods are limited by the large computational cost of the underlying density functional theory (DFT). Here we show that a novel class of machine-learning-based interatomic potentials can be used for random structure searching and readily predicts several hitherto unknown carbon allotropes. Remarkably, our model draws structural information from liquid and amorphous carbon exclusively, and so does not have any prior knowledge of crystalline phases: it therefore demonstrates true transferability, which is a crucial prerequisite for applications in chemistry. The method is orders of magnitude faster than DFT and can, in principle, be coupled with any algorithm for structure prediction. Machine-learning models therefore seem promising to enable large-scale structure searches in the future.
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