Crystal Structure Representations for Machine Learning Models of Formation Energies

TL;DR

This paper introduces three novel feature vector representations of crystal structures for machine learning models predicting formation energies, comparing their effectiveness on a dataset of nearly 4000 structures.

Contribution

It proposes and evaluates three new methods to encode periodic crystal structures for ML, extending molecular representations to solids.

Findings

Representation (i) achieved the lowest generalization error at 0.49 eV/atom.

Representation (ii) had a higher error at 0.64 eV/atom.

Representation (iii) using sine functions achieved an error of 0.37 eV/atom.

Abstract

We introduce and evaluate a set of feature vector representations of crystal structures for machine learning (ML) models of formation energies of solids. ML models of atomization energies of organic molecules have been successful using a Coulomb matrix representation of the molecule. We consider three ways to generalize such representations to periodic systems: (i) a matrix where each element is related to the Ewald sum of the electrostatic interaction between two different atoms in the unit cell repeated over the lattice; (ii) an extended Coulomb-like matrix that takes into account a number of neighboring unit cells; and (iii) an Ansatz that mimics the periodicity and the basic features of the elements in the Ewald sum matrix by using a sine function of the crystal coordinates of the atoms. The representations are compared for a Laplacian kernel with Manhattan norm, trained to reproduce formation energies using a data set of 3938 crystal structures obtained from the Materials Project. For training sets consisting of 3000 crystals, the generalization error in predicting formation energies of new structures corresponds to (i) 0.49, (ii) 0.64, and (iii) 0.37 eV/atom for the respective representations.