ArticleChemistry of materials : a publication of the American Chemical Society2018
Reactivity of Amorphous Carbon Surfaces: Rationalizing the Role of Structural Motifs in Functionalization Using Machine Learning.
Article in Chemistry of materials : a publication of the American Chemical Society, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Efficient Machine Learning Prediction of Solvent-DependentJournal of computational chemistry · 2026Article
- Experiment-Driven Atomistic Materials Modeling: A Case Study Combining X-Ray Photoelectron Spectroscopy and Machine Learning Potentials to Infer the Structure of Oxygen-Rich Amorphous Carbon.Journal of the American Chemical Society · 2024Article
- Modelling amorphous materialsChemical science · 2023Article
- Evaluation of Machine Learning Interatomic Potentials for the Properties of Gold Nanoparticles.Nanomaterials (Basel, Switzerland) · 2022Article
- Unraveling the origin of reductive stability of super-concentrated electrolytes from first principles and unsupervised machine learning.Chemical science · 2022Article
- UV Sterilization Effects and Osteoblast Proliferation on Amorphous Carbon Films Classified Based on Optical Constants.Bioengineering (Basel, Switzerland) · 2022Article
- Accurate Computational Prediction of Core-Electron Binding Energies in Carbon-Based Materials: A Machine-Learning Model Combining Density-Functional Theory andChemistry of materials : a publication of the American Chemical Society · 2022Article
- Correlation of Cell Proliferation with Surface Properties of Polymer-like Carbon Films of Different Thicknesses Prepared by a Radio-Frequency Plasma CVD Process.Materials (Basel, Switzerland) · 2022Article
- X-ray Spectroscopy Fingerprints of Pristine and Functionalized Graphene.The journal of physical chemistry. C, Nanomaterials and interfaces · 2021Article
- Gaussian Process Regression for Materials and Molecules.Chemical reviews · 2021Article
- Ab Initio Machine Learning in Chemical Compound Space.Chemical reviews · 2021Review
- Extracting interface correlations from the pair distribution function of composite materials.Nanoscale · 2021Article
- Efficient Machine-Learning-Aided Screening of Hydrogen Adsorption on Bimetallic Nanoclusters.ACS combinatorial science · 2020Article
- Understanding the geometric diversity of inorganic and hybrid frameworks through structural coarse-graining.Chemical science · 2020Article
- Quantitative Effects of Disorder on Chemically Modified Amorphous Carbon Electrodes.ACS applied energy materials · 2020Article
- Exploring chemical compound space with quantum-based machine learning.Nature reviews. Chemistry · 2020Review
- Integrating Carbon Nanomaterials with Metals for Bio-sensing Applications.Molecular neurobiology · 2020Article
- Quantifying Chemical Structure and Machine-Learned Atomic Energies in Amorphous and Liquid Silicon.Angewandte Chemie (International ed. in English) · 2019Article
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5 authors.
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Abstract
Systematic atomistic studies of surface reactivity for amorphous materials have not been possible in the past because of the complexity of these materials and the lack of the computer power necessary to draw representative statistics. With the emergence and popularization of machine learning (ML) approaches in materials science, systematic (and accurate) studies of the surface chemistry of disordered materials are now coming within reach. In this paper, we show how the reactivity of amorphous carbon (a-C) surfaces can be systematically quantified and understood by a combination of ML interatomic potentials, ML clustering techniques, and density functional theory calculations. This methodology allows us to process large amounts of atomic data to classify carbon atomic motifs on the basis of their geometry and quantify their reactivity toward hydrogen- and oxygen-containing functionalities. For instance, we identify subdivisions of sp and sp
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