Physics Maths Engineering
Applications of Machine Learning to the Study of Crystalline Materials
Hartmut Schlenz,
Stefan Sandfeld
Peer Reviewed
Related Subjects
Abstract
This Special Issue, “Applications of Machine Learning to the Study of Crystalline Materials”, is a collection of seven original articles published in 2021 and 2022 and dedicated to applications of machine learning in materials research [...]
Key Questions
1. How does machine learning enhance the study of crystalline materials?
ML techniques speed up the identification of material properties and allow for more accurate predictions based on large datasets.
2. What are the potential applications of ML in material science?
ML aids in discovering new materials, optimizing manufacturing processes, and predicting physical properties.
3. What challenges exist when applying ML to crystallography?
The challenges include data quality, model complexity, and the need for sufficient labeled data.
4. How can machine learning techniques predict material properties?
By analyzing data patterns, ML models can predict how materials will behave under various conditions.
