sunatthegilddotcom/ai4materials

Machine learning and compressed sensing modeling of materials science data.

Welcome to ai4materials's README

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

Documentation of a previous release can be found here: https://ai4materials.readthedocs.io/en/latest/

Code authors: Angelo Ziletti, Ph.D. ([email protected]; [email protected]), Andreas Leitherer ([email protected], [email protected])

ARISE: Crystal-structure recognition via Bayesian deep learning

This repository provides code for reproducing the results of

A. Leitherer, A. Ziletti, and L.M. Ghiringhelli,
Robust recognition and exploratory analysis of crystal structures via Bayesian deep learning, arXiv:2103.09777 (2021)

You can proceed with the installation steps as described below or directly proceed to a tutorial available at

http://analytics-toolkit.nomad-coe.eu/tutorial-ARISE

within the NOMAD analytics toolkit (https://nomad-lab.eu/AItutorials) where you do not have to install any software.

The code of this branch uses functionalities of ai4materials that is currently under development.

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Installation

We recommend to create a virtual python 3.7 environment (for instance, with conda), and then execute

git clone https://github.com/angeloziletti/ai4materials.git 
cd ai4materials
git checkout ARISE
pip install -e .

To reproduce the results in arXiv:2103.09777, you need to install the quippy package (https://github.com/libAtoms/QUIP) to be able to compute the SOAP descriptor.

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ARISE - Usage

For global or local analysis of single- or polycrystalline systems, one just needs to define the corresponding geometry file and load a pretrained model for prediction:

from ai4materials.models import ARISE

geometry_files = [ file_1, file_2, ... ]

predictions, uncertainty = ARISE.analyze(geometry_files, mode='global') 

predictions, uncertainty = ARISE.analyze(geometry_files, mode='local',
                                          stride=[[4.0, 4.0, 4.0], ...], box_size=[12.0, ...])

Please refer to http://analytics-toolkit.nomad-coe.eu/tutorial-ARISE and the associated publication for more details.