Materials Science Powered by Machine Learning
Errea, Ion (1); Garcia-Lekue, Aran (2)
- Publication year:
- 2022
- Publication place:
- Donostia
- Characteristics:
- BIBLID [0212-7016 (2022), 67, 2] - Recep.: 2022-06-21 ; Acept.: 2022-10-27
- ISSN:
- 0212-7016; eISSN: 2952-4180
Summary
Considering the large impact that artificial intelligence has had in several disciplines, materials scientists have started incorporating machine learning techniques into their everyday research. Currently, machine learning techniques are used to predict the properties of materials employing regression models based on datasets, to create accurate interatomic potentials, and also to solve the most basic equations of materials. In this manuscript we discuss the main techniques used in machine learning models in materials science, the most important applications, and the future prospects.
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