Welcome to ai4materials’s documentation!

The current documentation is not actively mantained and thus might not be up-to-date. For the most recent documentation, please visit ai4materials github repository https://github.com/angeloziletti/ai4materials.

ai4materials allows to perform complex analysis of materials science data using machine learning. 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.

ai4materials allows perform crystal-structure classification and analysis, as introduced in:

[1]A. Leitherer, A. Ziletti, and L. M. Ghiringhelli, “Robust recognition and exploratory analysis of crystal structures via Bayesian deep learning”, https://arxiv.org/abs/2103.09777 (2021)

Installation instructions can be found in the ai4materials github repository: https://github.com/angeloziletti/ai4materials.

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

Moreover, ai4materials can also reproduce results from the following publications:

[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