About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
|
AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
|
Presentation Title |
Applications of Machine Learning Techniques for Materials Discovery |
Author(s) |
Suchismita Goswami, Ichiro Takeuchi |
On-Site Speaker (Planned) |
Suchismita Goswami |
Abstract Scope |
Considerable efforts have been made to discover novel materials using machine learning techniques, including feature extraction and visualization for identifying similar potential material with known properties. For the identification of novel materials around a user defined compound, neighborhood maps of materials are usually generated employing the dimensionality reduction algorithms, which map the high dimensional features onto two-dimensions. However, a significant difference after the dimensionality reduction in representing the novel materials around a user defined material as compared to the high dimensional featurized space has been observed. Here we implement a different approach that will reduce the observed difference in the featurized and the reduced dimensional space. We employ Matminer and DScribe libraries to featurize crystallographic information files of the ICSD database into numerical feature vectors with JarvisCFID and Sine-Matrix methods, respectively. In this presentation, we will discuss results on the neighborhood maps on transition-metal based ferromagnetic compounds. |
Proceedings Inclusion? |
Planned: |
Keywords |
Machine Learning, |