Alina Zhidkovskaya, Tirtha Vinchurkar, and Evgeny Blokhin
Tilde Materials Informatics and Materials Platform for Data Science LLC
The project experiments on predicting the physical properties from the crystal structure by various machine learning methods and descriptors. The properties such as Seebeck coefficient and thermal conductivity are being predicted.
The repository includes Python code for working with the online chemical databases, data processing, and generating chemical descriptors to train machine-learning models. Here one can also find the examples of using the neural networks such as GCN, GAT, PointNet, and Transformer.
The folder summary includes experiment metrics.
Training data is obtained from MPDS database and compared to Materials Project.
MIT
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