Formation enthalpies for transition metal alloys using machine learning

Shashanka Ubaru, Agnieszka Międlar, Yousef Saad, and James R. Chelikowsky
Phys. Rev. B 95, 214102 – Published 1 June 2017

Abstract

The enthalpy of formation is an important thermodynamic property. Developing fast and accurate methods for its prediction is of practical interest in a variety of applications. Material informatics techniques based on machine learning have recently been introduced in the literature as an inexpensive means of exploiting materials data, and can be used to examine a variety of thermodynamics properties. We investigate the use of such machine learning tools for predicting the formation enthalpies of binary intermetallic compounds that contain at least one transition metal. We consider certain easily available properties of the constituting elements complemented by some basic properties of the compounds, to predict the formation enthalpies. We show how choosing these properties (input features) based on a literature study (using prior physics knowledge) seems to outperform machine learning based feature selection methods such as sensitivity analysis and LASSO (least absolute shrinkage and selection operator) based methods. A nonlinear kernel based support vector regression method is employed to perform the predictions. The predictive ability of our model is illustrated via several experiments on a dataset containing 648 binary alloys. We train and validate the model using the formation enthalpies calculated using a model by Miedema, which is a popular semiempirical model used for the prediction of formation enthalpies of metal alloys.

  • Figure
  • Figure
  • Figure
  • Received 18 June 2016
  • Revised 14 February 2017

DOI:https://doi.org/10.1103/PhysRevB.95.214102

©2017 American Physical Society

Physics Subject Headings (PhySH)

Condensed Matter, Materials & Applied Physics

Authors & Affiliations

Shashanka Ubaru1,*, Agnieszka Międlar2,†, Yousef Saad1,‡, and James R. Chelikowsky3,§

  • 1Department of Computer Science and Engineering, University of Minnesota, Twin Cities, Minnesota 55455, USA
  • 2Department of Mathematics, University of Kansas, Lawrence, Kansas 66045-7594, USA
  • 3Center for Computational Materials, Institute for Computational Engineering and Science, and Departments of Physics and Chemical Engineering, University of Texas, Austin, Texas 78712, USA

  • *ubaru001@umn.edu
  • amiedlar@ku.edu
  • saad@umn.edu
  • §jrc@utexas.edu

Article Text (Subscription Required)

Click to Expand

References (Subscription Required)

Click to Expand
Issue

Vol. 95, Iss. 21 — 1 June 2017

Reuse & Permissions
Access Options
CHORUS

Article Available via CHORUS

Download Accepted Manuscript
Author publication services for translation and copyediting assistance advertisement

Authorization Required


×
×

Images

×

Sign up to receive regular email alerts from Physical Review B

Log In

Cancel
×

Search


Article Lookup

Paste a citation or DOI

Enter a citation
×