AI Toolkit tutorials
Exploratory analysis of octet-binary compounds
Authors: Luigi Sbailò and Luca M. Ghiringhelli
Exploratory analyses make use of unsupervised learning techniques to extract information from unknown datasets. In this tutorial, we make use of some of the most popular clustering and dimension reduction algorithms to analyze a dataset composed of 82 octet-binary compounds.
Symbolic regression via compressed sensing: a tutorial
Authors: Emre Ahmetcik, Angelo Ziletti, Runhai Ouyang, Luigi Sbailò, Matthias Scheffler and Luca M. Ghiringhelli
In this tutorial we will show how to find descriptive parameters to predict materials properties using symbolic regrression combined with compressed sensing tools. The relative stability of the zincblende (ZB) versus rocksalt (RS) structure of binary materials is predicted and compared against a model trained with kernel ridge regression.
Introduction to clustering
Authors: Luigi Sbailò and Luca M. Ghiringhelli
In this tutorial, we introduce to the most popular clustering algorithms. We focus on partitioning, hierarchical and density-based clustering algorithms. The methods are tested on synthetic datasets of increasing complexity.
Decision tree tutorial
Authors: Daniel Speckhard, Andreas Leitherer and Luca M. Ghiringhelli
In this tutorial we will introduce decision trees. We go through a toy model introducing the SKLearn API. We then discuss piece by piece the different theoretical aspects of trees. We then move to training a regression tree and classification tree on different datasets related to materials science. We end the tutorial by covering random forests and bagging classifiers.
Kernel Ridge Regression for Materials Property Prediction: A Tutorial Introduction
Authors: Marcel F. Langer
In this tutorial, we'll explore the application of kernel ridge regression to the prediction of materials properties. We will begin with a largely informal, pragmatic introduction to kernel ridge regression, including a rudimentary implementation, in order to become familiar with the basic terminology and considerations. We will then discuss representations, and re-trace the NOMAD 2018 Kaggle challenge.
Querying the Archive and performing Artificial Intelligence modeling
Authors: Luigi Sbailò, Matthias Scheffler and Luca M. Ghiringhelli
In this tutorial, we demonstrate how to query the NOMAD Archive from the NOMAD Analytics toolkit. We then show examples of machine learning analysis performed on the retrieved data set.
Electronic density-of-states similarity search
Authors: Šimon Gabaj, Martin Kuban, Santiago Rigamonti and Claudia Draxl
This notebook shows how to compute the similarity of materials in terms of their electronic density-of-states (DOS), from data retrieved from the NOMAD Archive.
Introduction to convolutional neural networks
Authors: Angelo Ziletti, Andreas Leitherer and Luca M. Ghiringhelli
In this tutorial, we briefly introduce the main ideas behind convolutional neural networks, build a neural network model with Keras, and explain the classification decision process using attentive response maps.