A Formation Energy Predictor for Crystalline Materials Using Ensemble Data Mining

Ankit Agrawal*, Bryce Meredig, Chris Wolverton, Alok Choudhary

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

Formation energy is one of the most important properties of a compound that is directly related to its stability. More negative the formation energy, the more stable the compound is likely to be. Here we describe the development and deployment of predictive models for formation energy, given the chemical composition of the material. The data-driven models described here are built using nearly 100,000 Density Functional Theory (DFT) calculations, which is a quantum mechanical simulation technique based on the electron density within the crystal structure of the material. These models are deployed in an online web-Tool that takes a list of material compositions as input, generates over hundred composition-based attributes for each material and feeds them into the predictive models to obtain the predictions of formation energy. The online formation energy predictor is available at http://info.eecs.northwestern.edu/FEpredictor.

Original languageEnglish (US)
Title of host publicationProceedings - 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016
EditorsCarlotta Domeniconi, Francesco Gullo, Francesco Bonchi, Francesco Bonchi, Josep Domingo-Ferrer, Ricardo Baeza-Yates, Ricardo Baeza-Yates, Ricardo Baeza-Yates, Zhi-Hua Zhou, Xindong Wu
PublisherIEEE Computer Society
Pages1276-1279
Number of pages4
ISBN (Electronic)9781509054725
DOIs
StatePublished - Jul 2 2016
Event16th IEEE International Conference on Data Mining Workshops, ICDMW 2016 - Barcelona, Spain
Duration: Dec 12 2016Dec 15 2016

Publication series

NameIEEE International Conference on Data Mining Workshops, ICDMW
Volume0
ISSN (Print)2375-9232
ISSN (Electronic)2375-9259

Other

Other16th IEEE International Conference on Data Mining Workshops, ICDMW 2016
CountrySpain
CityBarcelona
Period12/12/1612/15/16

Keywords

  • Density functional theory
  • Ensemble learning
  • Formation energy
  • Materials informatics
  • Supervised learning

ASJC Scopus subject areas

  • Computer Science Applications
  • Software

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