ArticleProceedings of the National Academy of Sciences of the United States of America2018
Machine learning determination of atomic dynamics at grain boundaries.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed.
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- Layer-by-layer unsupervised clustering of statistically relevant fluctuations in noisy time-series data of complex dynamical systems.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Neural network kinetics for exploring diffusion multiplicity and chemical ordering in compositionally complex materials.Nature communications · 2024Article
- Information decomposition in complex systems via machine learning.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Detecting dynamic domains and local fluctuations in complex molecular systems via timelapse neighbors shuffling.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Direct Imaging of the Kinetic Crystallization Pathway: Simulation and Liquid-Phase Transmission Electron Microscopy Observations.Materials (Basel, Switzerland) · 2023Review
- Correlation of plastic events with local structure in jammed packings across spatial dimensions.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Materials Informatics for Mechanical Deformation: A Review of Applications and Challenges.Materials (Basel, Switzerland) · 2021Review
- From predictive modelling to machine learning and reverse engineering of colloidal self-assembly.Nature materials · 2021Review
- Crystalline shielding mitigates structural rearrangement and localizes memory in jammed systems under oscillatory shear.Science advances · 2021Article
- Reinforcing materials modelling by encoding the structures of defects in crystalline solids into distortion scores.Nature communications · 2020Article
- A model for grain boundary thermodynamics.RSC advances · 2020Article
- Uncovering the effects of interface-induced ordering of liquid on crystal growth using machine learning.Nature communications · 2020Article
- Quantitative prediction of grain boundary thermal conductivities from local atomic environments.Nature communications · 2020Article
- A generalized deep learning approach for local structure identification in molecular simulations.Chemical science · 2019Article
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6 authors.
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Abstract
In polycrystalline materials, grain boundaries are sites of enhanced atomic motion, but the complexity of the atomic structures within a grain boundary network makes it difficult to link the structure and atomic dynamics. Here, we use a machine learning technique to establish a connection between local structure and dynamics of these materials. Following previous work on bulk glassy materials, we define a purely structural quantity (softness) that captures the propensity of an atom to rearrange. This approach correctly identifies crystalline regions, stacking faults, and twin boundaries as having low likelihood of atomic rearrangements while finding a large variability within high-energy grain boundaries. As has been found in glasses, the probability that atoms of a given softness will rearrange is nearly Arrhenius. This indicates a well-defined energy barrier as well as a well-defined prefactor for the Arrhenius form for atoms of a given softness. The decrease in the prefactor for low-softness atoms indicates that variations in entropy exhibit a dominant influence on the atomic dynamics in grain boundaries.
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