Every discipline of physics, including materials science, has been profoundly influenced by the arrival of algorithmic breakthroughs in the domain of machine learning. Many important advances have been made by combining materials data (computed and measured) with different machine learning approaches to solve difficult problems like, creating effectual and extrapolative surrogate prototypes for a wide variety of material parameters, down-selecting and screening novel candidate materials for particular application, and structuring novel approaches to accelerate and enhance atomistic and molecular simulations. Although current implementations have shown some of the promise of data-enabled pathways, it has become evident that success in this area will depend on our capacity to interpret, explain, and justify the results of a machine learning approach on the basis of our professional knowledge in the field. This article reviews the most important machine learning applications in materials engineering. In addition, we present a short overview of a number of methods that have proven useful in deriving physically relevant insights, design-centric knowledge, and causal links from materials engineering. Last but not least, we highlight some of the next prospects and obstacles that the materials community will encounter in this dynamic and fast developing industry.
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Gunnar Lorna
Gunnar Lorna
Department of Physics, Roskilde University, 4000 Roskilde, Denmark.
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Birgir Guomundsson and Gunnar Lorna, “Automated Design Using Machine Learning in Materials Engineering - An Explicit Forecasts”, Journal of Computational Intelligence in Materials Science, vol.1, pp. 056-066, 2023. doi: 10.53759/832X/JCIMS202301006.
