Welcome to ai4materials’s documentation!

ai4materials allows to perform complex analysis of materials science data, using machine learning and compressed sensing techniques. It also provide functions to pre-process (on parallel processors), save and subsequently load materials science datasets, thus easing the traceability, reproducibility, and prototyping of new models.

On the left panel, you can find a few examples that showcase what ai4materials can do.

Finally, with ai4materials you can reproduce results from the following publications:

[1]A. Ziletti, A. Leitherer, M. Scheffler, and L. M. Ghiringhelli, “Crystal-structure classification via Bayesian deep learning: towards superhuman performance”, in preparation (2018)
[2]A. Ziletti, D. Kumar, M. Scheffler, and L. M. Ghiringhelli, “Insightful classification of crystal structures using deep learning,” Nature Communications, vol. 9, pp. 2775, 2018. [Link to article]
[3]L. M. Ghiringhelli, J. Vybiral, S. V. Levchenko, C. Draxl, and M. Scheffler, “Big Data of Materials Science: Critical Role of the Descriptor,” Physical Review Letters, vol. 114, no. 10, p. 105503 . [Link to article]

Code author: Angelo Ziletti <angelo.ziletti@gmail.com>


Module contents

Indices and tables