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Multi-fidelity machine learning models for accurate bandgap predictions of solids

Journal Article · · Computational Materials Science
 [1];  [2];  [2]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States). Materials Science and Technology Division
  2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States). Theoretical Division
+ Show Author Affiliations
Here, we present a multi-fidelity co-kriging statistical learning framework that combines variable-fidelity quantum mechanical calculations of bandgaps to generate a machine-learned model that enables low-cost accurate predictions of the bandgaps at the highest fidelity level. Additionally, the adopted Gaussian process regression formulation allows us to predict the underlying uncertainties as a measure of our confidence in the predictions. In using a set of 600 elpasolite compounds as an example dataset and using semi-local and hybrid exchange correlation functionals within density functional theory as two levels of fidelities, we demonstrate the excellent learning performance of the method against actual high fidelity quantum mechanical calculations of the bandgaps. The presented statistical learning method is not restricted to bandgaps or electronic structure methods and extends the utility of high throughput property predictions in a significant way.
Research Organization:
Los Alamos National Laboratory (LANL)
Sponsoring Organization:
USDOE
Grant/Contract Number:
AC52-06NA25396
OSTI ID:
1352377
Alternate ID(s):
OSTI ID: 1397619
Report Number(s):
LA-UR-16-29228
Journal Information:
Computational Materials Science, Journal Name: Computational Materials Science Journal Issue: C Vol. 129; ISSN 0927-0256
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English

References (64)

Adaptive machine learning framework to accelerate ab initio molecular dynamics journal December 2014
Materials informatics: An emerging technology for materials development journal June 2009
Random-phase approximation and its applications in computational chemistry and materials science journal June 2012
Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set journal July 1996
Materials informatics journal October 2005
Informatics-aided bandgap engineering for solar materials journal February 2014
Multi-objective optimization techniques to design the Pareto front of organic dielectric polymers journal December 2016
Critical assessment of regression-based machine learning methods for polymer dielectrics journal December 2016
Using support vector regression for the prediction of the band gap and melting point of binary and ternary compound semiconductors journal February 2006
From Organized High-Throughput Data to Phenomenological Theory using Machine Learning: The Example of Dielectric Breakdown journal February 2016
Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach journal April 2015
Computational 2D Materials Database: Electronic Structure of Transition-Metal Dichalcogenides and Oxides journal June 2015
Machine Learning Assisted Predictions of Intrinsic Dielectric Breakdown Strength of ABX 3 Perovskites journal June 2016
High-Throughput Combinatorial Database of Electronic Band Structures for Inorganic Scintillator Materials journal June 2011
Computational Screening of 2D Materials for Photocatalysis journal March 2015
Computational predictions of energy materials using density functional theory journal January 2016
Prediction and accelerated laboratory discovery of previously unknown 18-electron ABX compounds journal March 2015
Accelerating materials property predictions using machine learning journal September 2013
Classification of octet AB-type binary compounds using dynamical charges: A materials informatics perspective journal December 2015
Machine learning bandgaps of double perovskites journal January 2016
Machine Learning Strategy for Accelerated Design of Polymer Dielectrics journal February 2016
Computational screening of perovskite metal oxides for optimal solar light capture journal January 2012
Calculated optical absorption of different perovskite phases journal January 2015
Accelerated computational discovery of high-performance materials for organic photovoltaics by means of cheminformatics journal January 2011
New cubic perovskites for one- and two-photon water splitting using the computational materials repository journal January 2012
Hybrid functionals based on a screened Coulomb potential journal May 2003
Atomic Radii in Crystals journal November 1964
Prescription for the design and selection of density functional approximations: More constraint satisfaction with fewer fits journal August 2005
Quadratic configuration interaction. A general technique for determining electron correlation energies journal November 1987
The GW method journal March 1998
First-principles calculations of the electronic structure and spectra of strongly correlated systems: the LDA + U method journal January 1997
Machine learning of molecular electronic properties in chemical compound space journal September 2013
Predicting the output from a complex computer code when fast approximations are available journal March 2000
Multi-fidelity optimization via surrogate modelling
  • Forrester, Alexander I. J.; Sóbester, András; Keane, Andy J.
  • Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 463, Issue 2088 https://doi.org/10.1098/rspa.2007.1900
journal September 2007
New Method for Calculating the One-Particle Green's Function with Application to the Electron-Gas Problem journal August 1965
Self-Consistent Equations Including Exchange and Correlation Effects journal November 1965
Note on an Approximation Treatment for Many-Electron Systems journal October 1934
Special points for Brillouin-zone integrations journal June 1976
Proof that ∂ E ∂ n i = ε in density-functional theory journal December 1978
Projector augmented-wave method journal December 1994
Generalized Kohn-Sham schemes and the band-gap problem journal February 1996
Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set journal October 1996
From ultrasoft pseudopotentials to the projector augmented-wave method journal January 1999
Quasiparticle band structure based on a generalized Kohn-Sham scheme journal September 2007
Fractional charge perspective on the band gap in density-functional theory journal March 2008
Screening for high-performance piezoelectrics using high-throughput density functional theory journal July 2011
Computational design of low-band-gap double perovskites journal October 2012
Accelerated materials property predictions and design using motif-based fingerprints journal July 2015
Learning scheme to predict atomic forces and accelerate materials simulations journal September 2015
Prediction model of band gap for inorganic compounds by combination of density functional theory calculations and machine learning techniques journal March 2016
Localization and Delocalization Errors in Density Functional Theory and Implications for Band-Gap Prediction journal April 2008
Accurate Band Gaps of Semiconductors and Insulators with a Semilocal Exchange-Correlation Potential journal June 2009
Efficient Band Gap Prediction for Solids journal November 2010
Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning journal January 2012
Finding Density Functionals with Machine Learning journal June 2012
Big Data of Materials Science: Critical Role of the Descriptor journal March 2015
Strongly Constrained and Appropriately Normed Semilocal Density Functional journal July 2015
Physical Content of the Exact Kohn-Sham Orbital Energies: Band Gaps and Derivative Discontinuities journal November 1983
Density-Functional Theory of the Energy Gap journal November 1983
Generalized Gradient Approximation Made Simple journal October 1996
Density functional theory: Its origins, rise to prominence, and future journal August 2015
Classification of AB O 3 perovskite solids: a machine learning study journal September 2015
Bayesian Analysis of Hierarchical Multifidelity Codes journal January 2013
Recursive Co-Kriging Model for Design of Computer Experiments with Multiple Levels of Fidelity journal January 2014

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Chemical diversity in molecular orbital energy predictions with kernel ridge regression journal May 2019
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Machine learning in materials design and discovery: Examples from the present and suggestions for the future journal December 2018
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Efficient construction method for phase diagrams using uncertainty sampling journal March 2019
Bayesian optimization of chemical composition: A comprehensive framework and its application to R Fe 12 -type magnet compounds journal May 2019
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Predicting Electronic Structure Properties of Transition Metal Complexes with Neural Networks text January 2017
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Related Subjects

36 MATERIALS SCIENCE
Double perovskites
Elpasolites
Information fusion
Materials informatics