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. Machine learning (ML) is rapidly revolutionizing many fields and is starting to change landscapes for physics, chemistry, materials science, and structural research. With its ability to solve complex tasks autonomously, ML is being exploited as a radically new way to help find material correlations, understand materials chemistry, analyze crystal structures and related properties, and generally accelerate the discovery of new materials. Thus, one goal of this special issue is to demonstrate available, practical machine learning techniques that can be used to study crystalline materials today by means of the application of different ML techniques (including deep learning), as well as by the demonstration of best practices.
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.
