Predicting material microstructure evolution via data-driven machine learning.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 34286300.
- Also identified by DOI 10.1016/j.patter.2021.100285 and PMC identifier 8276005.
- No licence information is recorded for this record.
- Because redistribution is not established, this page shows the abstract only. Follow the links below for the full text.
Abstract
Predicting microstructure evolution can be a formidable challenge, yet it is essential to building microstructure-processing-property relationships. Yang et al. offer a new solution to traditional partial differential equation-based simulations: a data-driven machine learning approach motivated by the practical needs to accelerate the materials design process and deal with incomplete information in the real world of microstructure simulation.