featomic

featomic is a library for the efficient computing of features for atomistic machine learning also called “representations”, “descriptors” or “fingerprints”. These features can be used for atomistic machine learning (ML) models including ML potentials, visualization or similarity analysis. You can learn how to use it reading the documentation, and get the latest version from the github repository

PCA/PCovR Visualization of a training dataset for a potential
This example uses featomic and metatensor to compute structural properties for the structures in a training dataset for a ML potential. These are then used with simple dimensionality reduction algorithms (implemented in sklearn and skmatter) to obtain a simplified description of the dataset, that is then visualized using chemiscope.
PCA/PCovR Visualization of a training dataset for a potential
Local Prediction Rigidity analysis
In this tutorial, we calculate the SOAP descriptors of an amorphous silicon dataset using featomic, then compute the local prediction rigidity (LPR) for the atoms of a "test" set before and after modifications to the "training" dataset has been made.
Local Prediction Rigidity analysis
A ML model for the electron density of states
This tutorial would go through the entire machine learning framework for the electronic density of states (DOS). It will cover the construction of the DOS and SOAP descriptors from ase Atoms and eigenenergy results. A simple neural network will then be constructed and the model parameters, along with the energy reference will be optimized during training. A total of three energy reference will be used, the average Hartree potential, the Fermi level, and an optimized energy reference starting from the Fermi level energy reference. The performance of each model is then compared.
A ML model for the electron density of states
Long-distance Equivariants: a tutorial
This tutorial explains how Long range equivariant descriptors can be constructed using featomic and the resulting descriptors be used to construct a linear model with equisolve
Long-distance Equivariants: a tutorial
Generalized Convex Hull construction for the polymorphs of ROY
This notebook analyzes the structures of 264 polymorphs of ROY, from Beran et Al, Chemical Science (2022), comparing the conventional density-energy convex hull with a Generalized Convex Hull (GCH) analysis (see Anelli et al., Phys. Rev. Materials (2018)). It uses features computed with featomic and uses the directional convex hull function from scikit-matter to make the figure.
Generalized Convex Hull construction for the polymorphs of ROY
Sample and Feature Selection with FPS and CUR
In this tutorial we generate descriptors using featomic, then select a subset of structures using both the farthest-point sampling (FPS) and CUR algorithms implemented in scikit-matter. Finally, we also generate a selection of the most important features using the same techniques.
Sample and Feature Selection with FPS and CUR
Periodic Hamiltonian learning
This tutorial explains how to train a machine learning model for the electronic Hamiltonian of a periodic system. Even though we focus on periodic systems, the code and techniques presented here can be directly transferred to molecules.
Periodic Hamiltonian learning
Rotating equivariants
This example shows how to rotate equivariant properties of atomic structures using the scipy and quaternionic libraries. The equivariant properties for this example are computed by the featomic library.
Rotating equivariants
Equivariant linear model for polarizability
In this example, we demonstrate how to construct a metatensor atomistic model for the polarizability tensor of molecular systems. This example uses the featomic library to compute equivariant descriptors, and scikit-learn to train a linear regression model. The model can then be used in an ASE calculator. You could also have a look at this recipe based on scalar/tensorial models, which provides an alternative approach for equivariant learning of tensors.
Equivariant linear model for polarizability
Equivariant model for tensorial properties based on scalar features
In this example, we demonstrate how to train a metatensor atomistic model on dipole moments and polarizabilities of small molecular systems, using a model that combines scalar descriptors with equivariant tensorial components that depend in a simple way from the molecular geometry. You may also want to read this recipe for a linear polarizability model, which provides an alternative approach for tensorial learning. The model is trained with metatrain and can then be used in an ASE calculator.
Equivariant model for tensorial properties based on scalar features