Despite the huge number of possible materials (e.g. GaAs, Al2O3, etc.), we note that “the chemical compound space” is sparsely populated when the focus is on selected properties or functions (e.g. structure: rock salt vs. zinkblende, electrical conductivity, etc.). NOMAD offers big-data analytics tools that will help to sort all of the available materials data to identify trends and anomalies. For more information click the "INTRODUCTION TO" button above.

The following tutorials are designed to get started with the Analytics Toolkit (click title to show/hide details of the selected tutorial):

*created by:* Sbailo, Luigi | Matthias Scheffler | Ghiringhelli, Luca M.

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.

*Keywords:* Materials properties prediction | Data visualization

*Method:* Clustering | Dimension reduction | Random forest

*created by:* Liu, Xiangyue | Sutton, Christopher | Yamamoto, Takenori | Lysogorskiy, Yury | Blumenthal, Lars | Hammerschmidt, Thomas | Golebiowski, Jacek | Ziletti, Angelo | Scheffler, Matthias | Ghiringhelli, Luca M.

In this tutorial, we will explore the best results of the NOMAD 2018 Kaggle research competition. The goal of this competition was to develop machine-learning models for the prediction of two target properties: the formation energy and the bandgap energy of transparent semiconducting oxides. The purpose of the modelling is to facilitate the discovery of new such materials and allow for advancements in (opto)electronic technologies

*Keywords:* Formation energy prediction | Band gap energy prediction

*Method:* Kernel ridge regression | Neural networks | SOAP | n-gram

*created by:* Regler, Benjamin | Scheffler, Matthias | Ghiringhelli, Luca M.

This interactive notebook includes the original implementation of total cumulative mutual information (TCMI) to reproduce the main results presented in the publication.

*Keywords:* information theory | mutual information | cumulative entropy | feature selection

*Method:* Clustering | TCMI

*Language(s):* python | javascript

*created by:* Fekete, Ádám | Stella, Martina | Lambert, Henry | De Vita, Alessandro | Csányi, Gábor

In this tutorial we will be using a machine learning method (clustering) to analyse results of Grain Boundary (GB) calculations of alpha-iron. Along the way we will learn about different methods to describe local atomic environment in order to calculate properties of GBs. We will use these properties to separate the different regions of the GB using clustering methods. Finally we will determine how the energy of the GB is changing according to the angle difference of the regions.

*Keywords:* Grain boundaries

*Method:* Clustering | K-means | Gaussian mixture

*created by:* Csányi, Gábor | Kermode, James R.

In this tutorial, we will use Gaussian process regression, GPR (or equivalently, Kernel Ridge Regression, KRR) to train and predict charges of atoms in small organic molecules.

*Keywords:* GDB molecular database

*Method:* Gaussian-process regression | Kernel ridge regression

*created by:* Fekete, Ádám | Stella, Martina | Lambert, Henry | De Vita, Alessandro | Csányi, Gábor

In this tutorial we will be using a Gaussian Approximation Potentials to analyse results of TB DFT calculations of Si surface. Along the way we will learn about different descriptors (2b, 3b, soap) to describe local atomic environment in order to predict energies and forces of Si surface.

*Keywords:* SOAP descriptor | Gaussian Approximation Potentials (GAP)

*Method:* Gaussian-process regression | Kernel ridge regression

*created by:* Ahmetcik, Emre | Ziletti, Angelo | Ouyang, Runhai | Scheffler, Matthias | Ghiringhelli, Luca M.

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.

*Keywords:* Compressed sensing | Symbolic regression | Descriptors

*Method:* LASSO | SISSO | Kernel ridge regression

*created by:* Ziletti, Angelo | Leitherer, Andreas | Ghiringhelli, Luca M.

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.

*Keywords:* Classification | Neural Networks

*Method:* Convolutional Neural networks | Attentive response map

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