| About this Abstract |
| Meeting |
MS&T22: Materials Science & Technology
|
| Symposium
|
Materials Informatics for Images and Multi-dimensional Datasets
|
| Presentation Title |
Neighborhood Maps for Discovery of Novel Materials in Reduced Dimensions Using Machine Learning |
| Author(s) |
Suchismita Goswami, V. Stanev, H. Liang, I. Takeuchi |
| On-Site Speaker (Planned) |
Suchismita Goswami |
| Abstract Scope |
Machine learning techniques are being used to discover novel materials, compounds and molecules. The mapping of atomistic materials into feature vectors is an important step prior to implementation of any machine learning algorithms, consisting of both the unsupervised tasks for underlying patterns and the supervised learning tasks for prediction. Here we implement Python based libraries to featurize crystallographic information files (CIFs) into numerical descriptors with JarvisCFID and Sine Matrix methods. The Sine Matrix descriptor mostly calculates Columb interactions between atoms in a periodic system with reduced computational cost. We then project the high dimensional featurized data into a two-dimensional space using the t-Stochastic Neighbor Embedding and the Uniform Manifold Approximation and Projection methods. Such projected data usually create maps of neighbors for visualization around a user defined compound for prediction novel compounds. Here we present neighborhood maps for identifying similar novel materials of magnetic materials and Li-based compounds. |