How to represent crystal structures for machine learning: Towards fast prediction of electronic properties

By Kristof T. Schütt, H. Glawe, Felix Brockherde, A. Sanna, Klaus‐Robert Müller , Eberhard K. U. Gross. 2013

375 citations recorded by the source corpus.

High-throughput density functional calculations of solids are highly time-consuming. As an alternative, we propose a machine learning approach for the fast prediction of solid-state properties. To achieve this, local spin-density approximation calculations are used as a training set. We focus on predicting the value of the density of electronic states at the Fermi energy. We find that conventional representations of the input data, such as the Coulomb matrix, are not suitable for the training of learning machines in the case of periodic solids. We propose a novel crystal structure representation for which learning and competitive prediction accuracies become possible within an unrestricted class of spd systems of arbitrary unit-cell size.

Subfields: Atomic and Molecular Physics, and Optics, Materials Chemistry

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atomic and Molecular Physics, and Optics

In Materials Chemistry

Citations in the corpus

Predicting the thermodynamic stability of perovskite oxides using machine learning models (2018, background): “Examples of properties predicted using machine learning approaches include: relative permittivity and oxygen diffusion properties of ceramic materials,[11] band gap of inorganic materials,[12] formation energy of elpasolite structures,[13] molecular electronic properties in chemical compound space,[”