Orbital graph convolutional neural network for material property prediction

Mohammadreza Karamad, Rishikesh Magar, Yuting Shi, Samira Siahrostami, Ian D. Gates, and Amir Barati Farimani
Phys. Rev. Materials 4, 093801 – Published 8 September 2020
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Abstract

Material representations that are compatible with machine learning models play a key role in developing models that exhibit high accuracy for property prediction. Atomic orbital interactions are one of the important factors that govern the properties of crystalline materials from which the local chemical environments of atoms is inferred. Therefore, to develop robust machine learning models for material properties prediction, it is imperative to include features representing such chemical attributes. Here, we propose the orbital graph convolutional neural network (OGCNN), a crystal graph convolutional neural network framework that includes atomic orbital interaction features that learns material properties in a robust way. In addition, we embedded an encoder-decoder network into the OGCNN enabling it to learn important features among basic atomic (elemental features), orbital-orbital interactions, and topological features. We examined the performance of this model on a broad range of crystalline materials data to predict different properties. We benchmarked the performance of the OGCNN model with that of: (1) the crystal graph convolutional neural network, (2) other state-of-the-art descriptors for material representations including many-body tensor representation and the smooth overlap of atomic positions, and (3) other conventional regression machine learning algorithms where different crystal featurization methods have been used. We find that the OGCNN significantly outperforms them. The OGCNN model with high predictive accuracy can be used to discover new materials among the immense phase and compound spaces of materials.

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  • Received 12 February 2020
  • Accepted 11 August 2020

DOI:https://doi.org/10.1103/PhysRevMaterials.4.093801

©2020 American Physical Society

Physics Subject Headings (PhySH)

Condensed Matter, Materials & Applied Physics

Authors & Affiliations

Mohammadreza Karamad1,2,*, Rishikesh Magar2,*, Yuting Shi2, Samira Siahrostami3, Ian D. Gates1, and Amir Barati Farimani2,†

  • 1Department of Chemical and Petroleum Engineering, University of Calgary, 2500 University Drive NW Calgary, Alberta T2N 1N4, Canada
  • 2Department of Mechanical Engineering, Chemical Engineering, and Biomedical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
  • 3Department of Chemistry, University of Calgary, 2500 University Drive NW, Calgary, Alberta T2N 1N4, Canada

  • *These authors contributed equally to this work.
  • Corresponding author: barati@cmu.edu

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Issue

Vol. 4, Iss. 9 — September 2020

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