Opportunities and Challenges for Machine Learning in Materials Science
By Dane Morgan, Ryan Jacobs. Annual review of materials research (Print), 2020
444 citations recorded by the source corpus.
Advances in machine learning have impacted myriad areas of materials science, such as the discovery of novel materials and the improvement of molecular simulations, with likely many more important developments to come. Given the rapid changes in this field, it is challenging to understand both the breadth of opportunities and the best practices for their use. In this review, we address aspects of both problems by providing an overview of the areas in which machine learning has recently had significant impact in materials science, and then we provide a more detailed discussion on determining the accuracy and domain of applicability of some common types of machine learning models. Finally, we discuss some opportunities and challenges for the materials community to fully utilize the capabilities of machine learning.
View Paper: https://www.semanticscholar.org/paper/ede72940ae0246a292d644bd3c7e0ebf1e12a01a
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Citations in the corpus
- Machine Learning for High-entropy Alloys: Progress, Challenges and Opportunities (2022, background): “While several excellent reviews have already been available on the broader topic of ML for materials science [75, 76, 77, 78, 79, 80, 67, 66], there is a lack of comprehensive reviews for the exciting progress and huge potential of ML for HEAs.”
- Phase-field modeling of microstructure evolution: Recent applications, perspectives and challenges (2021, methodology): “Materials science applications of ML have seen an exponential growth over the past years [331], and they have an extremely wide range of applications, from text mining to solving partial differential equations (PDEs) [331–333].”