skip to main content
DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Machine learning bandgaps of double perovskites

Abstract

The ability to make rapid and accurate predictions on bandgaps of double perovskites is of much practical interest for a range of applications. While quantum mechanical computations for high-fidelity bandgaps are enormously computation-time intensive and thus impractical in high throughput studies, informatics-based statistical learning approaches can be a promising alternative. Here we demonstrate a systematic feature-engineering approach and a robust learning framework for efficient and accurate predictions of electronic bandgaps of double perovskites. After evaluating a set of more than 1.2 million features, we identify lowest occupied Kohn-Sham levels and elemental electronegativities of the constituent atomic species as the most crucial and relevant predictors. As a result, the developed models are validated and tested using the best practices of data science and further analyzed to rationalize their prediction performance.

Authors:
 [1];  [2];  [1];  [2];  [1];  [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  2. Univ. of Connecticut, Storrs, CT (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1235935
Report Number(s):
LA-UR-15-23084
Journal ID: ISSN 2045-2322; srep19375
Grant/Contract Number:  
AC52-06NA25396
Resource Type:
Accepted Manuscript
Journal Name:
Scientific Reports
Additional Journal Information:
Journal Volume: 6; Journal ID: ISSN 2045-2322
Publisher:
Nature Publishing Group
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; computational methods; electronic; properties and materials; statistics

Citation Formats

Pilania, G., Mannodi-Kanakkithodi, A., Uberuaga, B. P., Ramprasad, R., Gubernatis, J. E., and Lookman, T. Machine learning bandgaps of double perovskites. United States: N. p., 2016. Web. doi:10.1038/srep19375.
Pilania, G., Mannodi-Kanakkithodi, A., Uberuaga, B. P., Ramprasad, R., Gubernatis, J. E., & Lookman, T. Machine learning bandgaps of double perovskites. United States. doi:10.1038/srep19375.
Pilania, G., Mannodi-Kanakkithodi, A., Uberuaga, B. P., Ramprasad, R., Gubernatis, J. E., and Lookman, T. Tue . "Machine learning bandgaps of double perovskites". United States. doi:10.1038/srep19375. https://www.osti.gov/servlets/purl/1235935.
@article{osti_1235935,
title = {Machine learning bandgaps of double perovskites},
author = {Pilania, G. and Mannodi-Kanakkithodi, A. and Uberuaga, B. P. and Ramprasad, R. and Gubernatis, J. E. and Lookman, T.},
abstractNote = {The ability to make rapid and accurate predictions on bandgaps of double perovskites is of much practical interest for a range of applications. While quantum mechanical computations for high-fidelity bandgaps are enormously computation-time intensive and thus impractical in high throughput studies, informatics-based statistical learning approaches can be a promising alternative. Here we demonstrate a systematic feature-engineering approach and a robust learning framework for efficient and accurate predictions of electronic bandgaps of double perovskites. After evaluating a set of more than 1.2 million features, we identify lowest occupied Kohn-Sham levels and elemental electronegativities of the constituent atomic species as the most crucial and relevant predictors. As a result, the developed models are validated and tested using the best practices of data science and further analyzed to rationalize their prediction performance.},
doi = {10.1038/srep19375},
journal = {Scientific Reports},
number = ,
volume = 6,
place = {United States},
year = {2016},
month = {1}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Citation Metrics:
Cited by: 23 works
Citation information provided by
Web of Science

Save / Share:

Works referenced in this record:

The high-throughput highway to computational materials design
journal, February 2013

  • Curtarolo, Stefano; Hart, Gus L. W.; Nardelli, Marco Buongiorno
  • Nature Materials, Vol. 12, Issue 3
  • DOI: 10.1038/nmat3568

Accelerating materials property predictions using machine learning
journal, September 2013

  • Pilania, Ghanshyam; Wang, Chenchen; Jiang, Xun
  • Scientific Reports, Vol. 3, Issue 1
  • DOI: 10.1038/srep02810

Rational design of all organic polymer dielectrics
journal, September 2014

  • Sharma, Vinit; Wang, Chenchen; Lorenzini, Robert G.
  • Nature Communications, Vol. 5, Issue 1
  • DOI: 10.1038/ncomms5845

Recharging lithium battery research with first-principles methods
journal, March 2011


AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations
journal, June 2012


Materials Scientists Look to a Data-Intensive Future
journal, March 2012


Drug design by machine learning: support vector machines for pharmaceutical data analysis
journal, December 2001


Quiz-playing computer system could revolutionize research
journal, February 2011


Time to automate identification
journal, September 2010

  • MacLeod, Norman; Benfield, Mark; Culverhouse, Phil
  • Nature, Vol. 467, Issue 7312
  • DOI: 10.1038/467154a

Machines that Think for Themselves
journal, June 2012


Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
journal, January 2012


Accelerated materials property predictions and design using motif-based fingerprints
journal, July 2015

  • Huan, Tran Doan; Mannodi-Kanakkithodi, Arun; Ramprasad, Rampi
  • Physical Review B, Vol. 92, Issue 1
  • DOI: 10.1103/PhysRevB.92.014106

How to represent crystal structures for machine learning: Towards fast prediction of electronic properties
journal, May 2014


Combinatorial screening for new materials in unconstrained composition space with machine learning
journal, March 2014


Crystal structure representations for machine learning models of formation energies
journal, April 2015

  • Faber, Felix; Lindmaa, Alexander; von Lilienfeld, O. Anatole
  • International Journal of Quantum Chemistry, Vol. 115, Issue 16
  • DOI: 10.1002/qua.24917

Machine Learning Energies of 2 Million Elpasolite ( A B C 2 D 6 ) Crystals
journal, September 2016


Atom-centered symmetry functions for constructing high-dimensional neural network potentials
journal, February 2011

  • Behler, Jörg
  • The Journal of Chemical Physics, Vol. 134, Issue 7
  • DOI: 10.1063/1.3553717

Adaptive machine learning framework to accelerate ab initio molecular dynamics
journal, December 2014

  • Botu, Venkatesh; Ramprasad, Rampi
  • International Journal of Quantum Chemistry, Vol. 115, Issue 16
  • DOI: 10.1002/qua.24836

Structure classification and melting temperature prediction in octet AB solids via machine learning
journal, June 2015


Classification of octet AB-type binary compounds using dynamical charges: A materials informatics perspective
journal, December 2015

  • Pilania, G.; Gubernatis, J. E.; Lookman, T.
  • Scientific Reports, Vol. 5, Issue 1
  • DOI: 10.1038/srep17504

Classification of AB O 3 perovskite solids: a machine learning study
journal, September 2015

  • Pilania, G.; Balachandran, P. V.; Gubernatis, J. E.
  • Acta Crystallographica Section B Structural Science, Crystal Engineering and Materials, Vol. 71, Issue 5
  • DOI: 10.1107/S2052520615013979

Finding Density Functionals with Machine Learning
journal, June 2012


Informatics-aided bandgap engineering for solar materials
journal, February 2014


High-Throughput Combinatorial Database of Electronic Band Structures for Inorganic Scintillator Materials
journal, June 2011

  • Setyawan, Wahyu; Gaume, Romain M.; Lam, Stephanie
  • ACS Combinatorial Science, Vol. 13, Issue 4
  • DOI: 10.1021/co200012w

Accelerated computational discovery of high-performance materials for organic photovoltaics by means of cheminformatics
journal, January 2011

  • Olivares-Amaya, Roberto; Amador-Bedolla, Carlos; Hachmann, Johannes
  • Energy & Environmental Science, Vol. 4, Issue 12
  • DOI: 10.1039/c1ee02056k

Erratum: “Hybrid functionals based on a screened Coulomb potential” [J. Chem. Phys. 118, 8207 (2003)]
journal, June 2006

  • Heyd, Jochen; Scuseria, Gustavo E.; Ernzerhof, Matthias
  • The Journal of Chemical Physics, Vol. 124, Issue 21
  • DOI: 10.1063/1.2204597

Computational screening of perovskite metal oxides for optimal solar light capture
journal, January 2012

  • Castelli, Ivano E.; Olsen, Thomas; Datta, Soumendu
  • Energy Environ. Sci., Vol. 5, Issue 2
  • DOI: 10.1039/C1EE02717D

A2B′B″O6 perovskites: A review
journal, May 2015


Real-space grid implementation of the projector augmented wave method
journal, January 2005


Self-consistent approximation to the Kohn-Sham exchange potential
journal, March 1995

  • Gritsenko, Oleg; van Leeuwen, Robert; van Lenthe, Erik
  • Physical Review A, Vol. 51, Issue 3
  • DOI: 10.1103/PhysRevA.51.1944

Kohn-Sham potential with discontinuity for band gap materials
journal, September 2010


Optimized effective atomic central potential
journal, July 1976


New Light-Harvesting Materials Using Accurate and Efficient Bandgap Calculations
journal, August 2014

  • Castelli, Ivano E.; Hüser, Falco; Pandey, Mohnish
  • Advanced Energy Materials, Vol. 5, Issue 2
  • DOI: 10.1002/aenm.201400915

Informatics guided discovery of surface structure-chemistry relationships in catalytic nanoparticles
journal, March 2014

  • Andriotis, Antonis N.; Mpourmpakis, Giannis; Broderick, Scott
  • The Journal of Chemical Physics, Vol. 140, Issue 9
  • DOI: 10.1063/1.4867010

Data mining for materials design: A computational study of single molecule magnet
journal, January 2014

  • Dam, Hieu Chi; Pham, Tien Lam; Ho, Tu Bao
  • The Journal of Chemical Physics, Vol. 140, Issue 4
  • DOI: 10.1063/1.4862156

The Information Content of 2D and 3D Structural Descriptors Relevant to Ligand-Receptor Binding
journal, January 1997

  • Brown, Robert D.; Martin, Yvonne C.
  • Journal of Chemical Information and Computer Sciences, Vol. 37, Issue 1
  • DOI: 10.1021/ci960373c

Big Data of Materials Science: Critical Role of the Descriptor
journal, March 2015


Compressive sensing as a paradigm for building physics models
journal, January 2013


An introduction to kernel-based learning algorithms
journal, March 2001

  • Muller, K. -R.; Mika, S.; Ratsch, G.
  • IEEE Transactions on Neural Networks, Vol. 12, Issue 2
  • DOI: 10.1109/72.914517

Transferable Atomic Multipole Machine Learning Models for Small Organic Molecules
journal, June 2015

  • Bereau, Tristan; Andrienko, Denis; von Lilienfeld, O. Anatole
  • Journal of Chemical Theory and Computation, Vol. 11, Issue 7
  • DOI: 10.1021/acs.jctc.5b00301

Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies
journal, July 2013

  • Hansen, Katja; Montavon, Grégoire; Biegler, Franziska
  • Journal of Chemical Theory and Computation, Vol. 9, Issue 8
  • DOI: 10.1021/ct400195d

Modeling electronic quantum transport with machine learning
journal, June 2014


Generalized Gradient Approximation Made Simple
journal, October 1996

  • Perdew, John P.; Burke, Kieron; Ernzerhof, Matthias
  • Physical Review Letters, Vol. 77, Issue 18, p. 3865-3868
  • DOI: 10.1103/PhysRevLett.77.3865

    Works referencing / citing this record:

    A hybrid organic-inorganic perovskite dataset
    journal, May 2017

    • Kim, Chiho; Huan, Tran Doan; Krishnan, Sridevi
    • Scientific Data, Vol. 4, Issue 1
    • DOI: 10.1038/sdata.2017.57

    Interaction trends between single metal atoms and oxide supports identified with density functional theory and statistical learning
    journal, July 2018

    • O’Connor, Nolan J.; Jonayat, A. S. M.; Janik, Michael J.
    • Nature Catalysis, Vol. 1, Issue 7
    • DOI: 10.1038/s41929-018-0094-5

    Deep neural networks for accurate predictions of crystal stability
    journal, September 2018


    Recent advances and applications of machine learning in solid-state materials science
    journal, August 2019

    • Schmidt, Jonathan; Marques, Mário R. G.; Botti, Silvana
    • npj Computational Materials, Vol. 5, Issue 1
    • DOI: 10.1038/s41524-019-0221-0

    Alternative materials for perovskite solar cells from materials informatics
    journal, July 2019


    Deep neural networks for accurate predictions of crystal stability
    journal, September 2018


    Recent advances and applications of machine learning in solid-state materials science
    journal, August 2019

    • Schmidt, Jonathan; Marques, Mário R. G.; Botti, Silvana
    • npj Computational Materials, Vol. 5, Issue 1
    • DOI: 10.1038/s41524-019-0221-0

    Interaction trends between single metal atoms and oxide supports identified with density functional theory and statistical learning
    journal, July 2018

    • O’Connor, Nolan J.; Jonayat, A. S. M.; Janik, Michael J.
    • Nature Catalysis, Vol. 1, Issue 7
    • DOI: 10.1038/s41929-018-0094-5

    A hybrid organic-inorganic perovskite dataset
    journal, May 2017

    • Kim, Chiho; Huan, Tran Doan; Krishnan, Sridevi
    • Scientific Data, Vol. 4, Issue 1
    • DOI: 10.1038/sdata.2017.57

    Predicting electronic structure properties of transition metal complexes with neural networks
    journal, January 2017

    • Janet, Jon Paul; Kulik, Heather J.
    • Chemical Science, Vol. 8, Issue 7
    • DOI: 10.1039/c7sc01247k

    Alternative materials for perovskite solar cells from materials informatics
    journal, July 2019


    Thermodynamic Stability Landscape of Halide Double Perovskites via High-Throughput Computing and Machine Learning
    journal, January 2019

    • Li, Zhenzhu; Xu, Qichen; Sun, Qingde
    • Advanced Functional Materials, Vol. 29, Issue 9
    • DOI: 10.1002/adfm.201807280

    Machine Learning Augmented Discovery of Chalcogenide Double Perovskites for Photovoltaics
    journal, March 2019

    • L. Agiorgousis, Michael; Sun, Yi‐Yang; Choe, Duk‐Hyun
    • Advanced Theory and Simulations, Vol. 2, Issue 5
    • DOI: 10.1002/adts.201800173

    Machine Learning Stability and Bandgaps of Lead‐Free Perovskites for Photovoltaics
    journal, November 2019

    • Stanley, Jared C.; Mayr, Felix; Gagliardi, Alessio
    • Advanced Theory and Simulations, Vol. 3, Issue 1
    • DOI: 10.1002/adts.201900178

    Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra
    journal, January 2019

    • Ghosh, Kunal; Stuke, Annika; Todorović, Milica
    • Advanced Science, Vol. 6, Issue 9
    • DOI: 10.1002/advs.201801367

    Data‐Driven Materials Science: Status, Challenges, and Perspectives
    journal, September 2019

    • Himanen, Lauri; Geurts, Amber; Foster, Adam Stuart
    • Advanced Science, Vol. 6, Issue 21
    • DOI: 10.1002/advs.201900808

    Predictions and Strategies Learned from Machine Learning to Develop High‐Performing Perovskite Solar Cells
    journal, October 2019

    • Li, Jinxin; Pradhan, Basudev; Gaur, Surya
    • Advanced Energy Materials, Vol. 9, Issue 46
    • DOI: 10.1002/aenm.201901891

    A Critical Review of Machine Learning of Energy Materials
    journal, January 2020


    Data Mining the C−C Cross‐Coupling Genome
    journal, May 2019

    • Sawatlon, Boodsarin; Wodrich, Matthew D.; Meyer, Benjamin
    • ChemCatChem, Vol. 11, Issue 16
    • DOI: 10.1002/cctc.201900597

    Statistical Analysis and Discovery of Heterogeneous Catalysts Based on Machine Learning from Diverse Published Data
    journal, August 2019


    Machine learning in materials science
    journal, August 2019


    Low‐dimensional metal halide perovskites and related optoelectronic applications
    journal, February 2020


    Investigation of structural, magneto-electronic, and thermoelectric response of ductile SnAlO 3 from high-throughput DFT calculations: KHANDY and GUPTA
    journal, February 2017

    • Khandy, Shakeel Ahmad; Gupta, Dinesh C.
    • International Journal of Quantum Chemistry, Vol. 117, Issue 8
    • DOI: 10.1002/qua.25351

    Machine learning properties of binary wurtzite superlattices
    journal, January 2018


    A Statistical Learning Framework for Accelerated Bandgap Prediction of Inorganic Compounds
    journal, November 2019

    • Chaube, Suryanaman; Khullar, Prerna; Goverapet Srinivasan, Sriram
    • Journal of Electronic Materials, Vol. 49, Issue 1
    • DOI: 10.1007/s11664-019-07779-2

    Universal fragment descriptors for predicting properties of inorganic crystals
    journal, June 2017

    • Isayev, Olexandr; Oses, Corey; Toher, Cormac
    • Nature Communications, Vol. 8, Issue 1
    • DOI: 10.1038/ncomms15679

    A general-purpose machine learning framework for predicting properties of inorganic materials
    journal, August 2016


    Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning
    journal, August 2018


    Identifying an efficient, thermally robust inorganic phosphor host via machine learning
    journal, October 2018


    Machine learning in materials informatics: recent applications and prospects
    journal, December 2017

    • Ramprasad, Rampi; Batra, Rohit; Pilania, Ghanshyam
    • npj Computational Materials, Vol. 3, Issue 1
    • DOI: 10.1038/s41524-017-0056-5

    A strategy to apply machine learning to small datasets in materials science
    journal, May 2018


    Active learning for accelerated design of layered materials
    journal, December 2018

    • Bassman, Lindsay; Rajak, Pankaj; Kalia, Rajiv K.
    • npj Computational Materials, Vol. 4, Issue 1
    • DOI: 10.1038/s41524-018-0129-0

    Inverse design in search of materials with target functionalities
    journal, March 2018


    High-throughput Identification and Characterization of Two-dimensional Materials using Density functional theory
    journal, July 2017


    Computer-aided design of metal chalcohalide semiconductors: from chemical composition to crystal structure
    journal, January 2018

    • Davies, Daniel W.; Butler, Keith T.; Skelton, Jonathan M.
    • Chemical Science, Vol. 9, Issue 4
    • DOI: 10.1039/c7sc03961a

    The impact of chemical order on defect transport in mixed pyrochlores
    journal, January 2019

    • Uberuaga, Blas P.; Perriot, Romain; Pilania, Ghanshyam
    • Physical Chemistry Chemical Physics, Vol. 21, Issue 11
    • DOI: 10.1039/c8cp07597b

    Machine learning for renewable energy materials
    journal, January 2019

    • Gu, Geun Ho; Noh, Juhwan; Kim, Inkyung
    • Journal of Materials Chemistry A, Vol. 7, Issue 29
    • DOI: 10.1039/c9ta02356a

    Physics-informed machine learning for inorganic scintillator discovery
    journal, June 2018

    • Pilania, G.; McClellan, K. J.; Stanek, C. R.
    • The Journal of Chemical Physics, Vol. 148, Issue 24
    • DOI: 10.1063/1.5025819

    Self-assembly as a key player for materials nanoarchitectonics
    journal, January 2019

    • Ariga, Katsuhiko; Nishikawa, Michihiro; Mori, Taizo
    • Science and Technology of Advanced Materials, Vol. 20, Issue 1
    • DOI: 10.1080/14686996.2018.1553108

    From DFT to machine learning: recent approaches to materials science–a review
    journal, May 2019

    • Schleder, Gabriel R.; Padilha, Antonio C. M.; Acosta, Carlos Mera
    • Journal of Physics: Materials, Vol. 2, Issue 3
    • DOI: 10.1088/2515-7639/ab084b

    Representation of compounds for machine-learning prediction of physical properties
    journal, April 2017


    Formation enthalpies for transition metal alloys using machine learning
    journal, June 2017


    Including crystal structure attributes in machine learning models of formation energies via Voronoi tessellations
    journal, July 2017


    Assessment of the GLLB-SC potential for solid-state properties and attempts for improvement
    journal, February 2018


    Machine learning with force-field-inspired descriptors for materials: Fast screening and mapping energy landscape
    journal, August 2018