ArticleAngewandte Chemie (International ed. in English)2019
Quantifying Chemical Structure and Machine-Learned Atomic Energies in Amorphous and Liquid Silicon.
Article in Angewandte Chemie (International ed. in English), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 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
- Signatures of paracrystallinity in amorphous silicon from machine-learning-driven molecular dynamics.Nature communications · 2025Article
- Is the Future of Materials Amorphous? Challenges and Opportunities in Simulations of Amorphous Materials.ACS physical chemistry Au · 2025Review
- Structure, Properties, and Applications of Silica Nanoparticles: Recent Theoretical Modeling Advances, Challenges, and Future Directions.Small (Weinheim an der Bergstrasse, Germany) · 2024Review
- Understanding Defects in Amorphous Silicon with Million-Atom Simulations and Machine Learning.Angewandte Chemie (International ed. in English) · 2024Article
- Robustness of Local Predictions in Atomistic Machine Learning Models.Journal of chemical theory and computation · 2023Article
- Velocity-Gauge Real-Time Time-Dependent Density Functional Tight-Binding for Large-Scale Condensed Matter Systems.Journal of chemical theory and computation · 2023Article
- Effect of Framework Composition and NHACS central science · 2023Article
- Review: understanding the properties of amorphous materials with high-performance computing methods.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2023Review
- Quantifying disorder one atom at a time using an interpretable graph neural network paradigm.Nature communications · 2023Article
- Exploring the configurational space of amorphous graphene with machine-learned atomic energies.Chemical science · 2022Article
- Machine Learning for Electronically Excited States of Molecules.Chemical reviews · 2021Article
- Gaussian Process Regression for Materials and Molecules.Chemical reviews · 2021Article
- Machine-learned potentials for next-generation matter simulations.Nature materials · 2021Review
- Article
- Quantifying Chemical Structure and Machine-Learned Atomic Energies in Amorphous and Liquid Silicon.Angewandte Chemie (International ed. in English) · 2019Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Amorphous materials are being described by increasingly powerful computer simulations, but new approaches are still needed to fully understand their intricate atomic structures. Here, we show how machine-learning-based techniques can give new, quantitative chemical insight into the atomic-scale structure of amorphous silicon (a-Si). We combine a quantitative description of the nearest- and next-nearest-neighbor structure with a quantitative description of local stability. The analysis is applied to an ensemble of a-Si networks in which we tailor the degree of ordering by varying the quench rates down to 10
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