Machine Learning Techniques in Materials Science

Organizers:

Mohsen Mirkhalaf, University of Gothenburg, Gothenburg, Sweden
Email: mohsen.mirkhalaf@physics.gu.se

Marcus Liwicki, Luleå University of Technology, Luleå, Sweden
Email: marcus.liwicki@ltu.se

Rina Komatsu, Sophia University, Tokyo, Japan
Email: r_komatsu@sophia.ac.jp

Keywords:
Materials science, machine learning

Short workshop abstract:

Machine Learning (ML) techniques have gained significant traction in materials science in recent years, revolutionizing various aspects of the field, including materials discovery and design, prediction of properties, materials characterization, multi-scale modelling, optimization of manufacturing processes, structure-property relations, and materials informatics. The movement toward usage of ML techniques is facilitated by availability of unprecedented amount of data from experiments and simulations, quick growth of computer power, and availability of advanced open-source libraries. As the field continues to advance, machine learning techniques will likely play an increasingly prominent role in shaping the future of materials science and engineering.

Topics of interest in this workshop include (not limited to):

·         Possible applications of ML in different materials science areas;

·         Machine learning with few data;

·         Machine Learning, Deep Learning, and Computer Vision;

·         Natural and technical language processing.

State-of-the-art developments will be discussed which hopefully trigger new ideas for further enhancements and joint collaborative projects.

Contact: Mohsen Mirkhalaf, University of Gothenburg https://www.gu.se/en/research/mechanics-of-materialsEmail: mohsen.mirkhalaf@physics.gu.se

Motivate how the workshop will contribute to increased Japan-Sweden collaboration and why a Japanese-Swedish perspective is relevant for the workshop topic

This workshop on the usage of machine learning techniques in materials science can contribute to increased Japan-Sweden collaboration and benefit from a Japanese-Swedish perspective for several reasons:

1. Complementary Expertise: Japan and Sweden have strong expertise in both materials science and machine learning.

2. Technological Advancements: Both Japan and Sweden are at the forefront of technological advancements in materials science and machine learning.

3. Data Sharing and Resources: Japan and Sweden possess valuable datasets and resources in materials science that can be shared and utilized in the workshop.

4. Cross-Cultural Perspectives: A Japanese-Swedish perspective brings valuable cross-cultural insights to the workshop. Different cultures often approach problems and research questions from distinct angles, leading to diverse perspectives and solutions

In summary, this workshop with a Japanese-Swedish perspective can capitalize on complementary expertise, leverage technological advancements, facilitate data sharing, and provide diverse viewpoints. It creates an opportunity for knowledge exchange, promotes research advancements, and contributes to the growth of both countries' scientific communities, while also addressing global challenges in materials science and technology.