Recent advances and applications of machine learning in solid-state materials science
Martin Luther University Halle-Wittenberg · Friedrich Schiller University Jena
Abstract
Abstract One of the most exciting tools that have entered the material science toolbox in recent years is machine learning. This collection of statistical methods has already proved to be capable of considerably speeding up both fundamental and applied research. At present, we are witnessing an explosion of works that develop and apply machine learning to solid-state systems. We provide a comprehensive overview and analysis of the most recent research in this topic. As a starting point, we introduce machine learning principles, algorithms, descriptors, and databases in materials science. We continue with the description of different machine learning approaches for the discovery of stable materials and the…
Citation impact
- FWCI
- 99.75
- Percentile
- 100%
- References
- 518
Authors
4Topics & keywords
- Interpretability
- Toolbox
- Machine learning
- Artificial intelligence
- Computer science
- Process (computing)
- Property (philosophy)
- Point (geometry)