ArticlePatterns (New York, N.Y.)2021
Self-supervised learning and prediction of microstructure evolution with convolutional recurrent neural networks.
Article in Patterns (New York, N.Y.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed, 95 citations in OpenAlex.
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- Ultrafast and accurate prediction of polycrystalline hafnium oxide phase-field ferroelectric hysteresis using graph neural networks.Nanoscale advances · 2024Article
- Score Dynamics: Scaling Molecular Dynamics with Picoseconds Time Steps via Conditional Diffusion Model.Journal of chemical theory and computation · 2024Article
- Deep Learning Model to Predict Ice Crystal Growth.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- Machine learning unifies flexibility and efficiency of spinodal structure generation for stochastic biomaterial design.Scientific reports · 2023Article
- New Platforms Based on Frontal Cellular Automata and Lattice Boltzmann Method for Modeling the Forming and Additive Manufacturing.Materials (Basel, Switzerland) · 2022Article
- Forecasting molecular dynamics energetics of polymers in solution from supervised machine learning.Chemical science · 2022Article
- Multi-input convolutional network for ultrafast simulation of field evolvement.Patterns (New York, N.Y.) · 2022Article
- Comparing transfer learning to feature optimization in microstructure classification.iScience · 2022Article
- Predicting material microstructure evolution via data-driven machine learning.Patterns (New York, N.Y.) · 2021Article
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
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Abstract
Microstructural evolution is a key aspect of understanding and exploiting the processing-structure-property relationship of materials. Modeling microstructure evolution usually relies on coarse-grained simulations with evolution principles described by partial differential equations (PDEs). Here we demonstrate that convolutional recurrent neural networks can learn the underlying physical rules and replace PDE-based simulations in the prediction of microstructure phenomena. Neural nets are trained by self-supervised learning with image sequences from simulations of several common processes, including plane-wave propagation, grain growth, spinodal decomposition, and dendritic crystal growth. The trained networks can accurately predict both short-term local dynamics and long-term statistical properties of microstructures assessed herein and are capable of extrapolating beyond the training datasets in spatiotemporal domains and configurational and parametric spaces. Such a data-driven approach offers significant advantages over PDE-based simulations in time-stepping efficiency and offers a useful alternative, especially when the material parameters or governing PDEs are not well determined.
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