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.

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Big-Data Analytics Toolkit



 

 

 

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

Archive Query
Atomic properties
Crystal structure prediction

created by: Ziletti, Angelo | Ahmetcik, Emre | Ouyang, Runhai | Kariryaa, Ankit | Mohamed, Fawzi | Ghiringhelli, Luca | Scheffler, Matthias

A tool for predicting the difference in the total energy between different polymorphs for 82 octet binary compounds, which gives an indication of the stability of the material. This is accomplished by identifying a set of descriptive parameters (a descriptor) from the free-atom data for the binary atomic species comprising the material using LASSO + l0-norm minimization approach.

Keywords: Octet binaries

Method: Compressed Sensing | LASSO+l0

Language(s): python | javascript

Encyclopedia
Error estimates
Materials property prediction
Organic molecules
Predicting ground-states of alloys (convex hull construction)

created by: Rigamonti, Santiago | Troppenz, Maria | Draxl, Claudia

A tool for predicting ground state configurations of binary alloys. In this tutorial, the ground state configurations of a AlNa surface alloy are found. Starting from a set of ab initio data for random configurations of the alloy, a cluster expansion is performed and the ground states are found through a configurational sampling.

Keywords: Alloys | Surfaces

Method: Cluster Expansion

Language(s): python

created by: Strange, Mikkel | Thygesen, Kristian S.

Developed by .A tool that produces compositional phase diagrams. As an example we consider materials made of Li-Fe-P-O. Phase diagrams are generally useful to determine if a given material is thermodynamically stable under certain conditions such as temperature, pressure etc. In this tutorial, we consider only a compositional phase diagram at zero temperature and pressure.

Keywords: Alloys

Language(s): python | javascript

Solids
Structure classification

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