Skip to main content
OSTI.GOVU.S. Department of Energy Office of Scientific and Technical Information
U.S. Department of Energy
Office of Scientific and Technical Information
 

Machine learning bandgaps of double perovskites

Journal Article · · Scientific Reports
DOI:https://doi.org/10.1038/srep19375· OSTI ID:1235935
 [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)
+ Show Author Affiliations
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.
Research Organization:
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Sponsoring Organization:
USDOE
Grant/Contract Number:
AC52-06NA25396
OSTI ID:
1235935
Report Number(s):
LA-UR--15-23084; srep19375
Journal Information:
Scientific Reports, Journal Name: Scientific Reports Vol. 6; ISSN 2045-2322
Publisher:
Nature Publishing GroupCopyright Statement
Country of Publication:
United States
Language:
English

References (70)

High-Throughput Combinatorial Database of Electronic Band Structures for Inorganic Scintillator Materials journal June 2011
Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies journal July 2013
Time to automate identification journal September 2010
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
Atom-centered symmetry functions for constructing high-dimensional neural network potentials journal February 2011
Optimizing transition states via kernel-based machine learning journal May 2012
Informatics guided discovery of surface structure-chemistry relationships in catalytic nanoparticles journal March 2014
New Method for Calculating the One-Particle Green's Function with Application to the Electron-Gas Problem journal August 1965
Systematization of the stable crystal structure of all AB -type binary compounds: A pseudopotential orbital-radii approach journal December 1980
Kohn-Sham potential with discontinuity for band gap materials journal September 2010
Finding Density Functionals with Machine Learning journal June 2012
Generalized Gradient Approximation Made Simple journal October 1996
Bandgap Engineering of Double Perovskites for One- and Two-photon Water Splitting journal January 2013
Modeling Electronic Quantum Transport with Machine Learning text January 2014
Big Data of Materials Science - Critical Role of the Descriptor text January 2014
Crystal Structure Representations for Machine Learning Models of Formation Energies preprint January 2015
Accelerated materials property predictions and design using motif-based fingerprints text January 2015
New Light-Harvesting Materials Using Accurate and Efficient Bandgap Calculations journal August 2014
Adaptive machine learning framework to accelerate ab initio molecular dynamics journal December 2014
Crystal structure representations for machine learning models of formation energies journal April 2015
Materials Informatics journal April 2009
The Elements of Statistical Learning book January 2009
Drug design by machine learning: support vector machines for pharmaceutical data analysis journal December 2001
AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations journal June 2012
Informatics-aided bandgap engineering for solar materials journal February 2014
A2B′B″O6 perovskites: A review journal May 2015
Materials informatics journal October 2005
Electronic Structure book January 2004
Transferable Atomic Multipole Machine Learning Models for Small Organic Molecules journal June 2015
The Information Content of 2D and 3D Structural Descriptors Relevant to Ligand-Receptor Binding journal January 1997
Rational design of all organic polymer dielectrics journal September 2014
Quiz-playing computer system could revolutionize research journal February 2011
The high-throughput highway to computational materials design journal February 2013
Machines that Think for Themselves journal June 2012
Computational screening of perovskite metal oxides for optimal solar light capture journal January 2012
Accelerated computational discovery of high-performance materials for organic photovoltaics by means of cheminformatics journal January 2011
Erratum: “Hybrid functionals based on a screened Coulomb potential” [J. Chem. Phys. 118, 8207 (2003)] journal June 2006
Data mining for materials design: A computational study of single molecule magnet journal January 2014
New Method for Calculating the One-Particle Green's Function with Application to the Electron-Gas Problem journal August 1965
Optimized effective atomic central potential journal July 1976
Self-consistent approximation to the Kohn-Sham exchange potential journal March 1995
Systematization of the stable crystal structure of all AB -type binary compounds: A pseudopotential orbital-radii approach journal December 1980
Real-space grid implementation of the projector augmented wave method journal January 2005
Kohn-Sham potential with discontinuity for band gap materials journal September 2010
Compressive sensing as a paradigm for building physics models journal January 2013
Combinatorial screening for new materials in unconstrained composition space with machine learning journal March 2014
How to represent crystal structures for machine learning: Towards fast prediction of electronic properties journal May 2014
Modeling electronic quantum transport with machine learning journal June 2014
Structure classification and melting temperature prediction in octet AB solids via machine learning journal June 2015
Accelerated materials property predictions and design using motif-based fingerprints journal July 2015
Prediction model of band gap for inorganic compounds by combination of density functional theory calculations and machine learning techniques journal March 2016
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
Machine Learning Energies of 2 Million Elpasolite ( A B C 2 D 6 ) Crystals journal September 2016
Generalized Gradient Approximation Made Simple journal October 1996
Classification of AB O 3 perovskite solids: a machine learning study journal September 2015
An introduction to kernel-based learning algorithms journal March 2001
Materials Scientists Look to a Data-Intensive Future journal March 2012
Materials informatics journal April 2009
Recharging lithium battery research with first-principles methods journal March 2011
Computational screening of perovskite metal oxides for optimal solar light capture dataset January 2011
Kohn-Sham potential with discontinuity for band gap materials text January 2010
Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning text January 2011
Prediction model of band-gap for AX binary compounds by combination of density functional theory calculations and machine learning techniques text January 2015
Transferable Atomic Multipole Machine Learning Models for Small Organic Molecules text January 2015
Fast and accurate modeling of molecular atomization energies with machine learning text January 2012
Modeling electronic quantum transport with machine learning text January 2014
Crystal structure representations for machine learning models of formation energies text January 2015

Cited By (85)

Data‐Driven Materials Science: Status, Challenges, and Perspectives journal September 2019
Emerging role of machine learning in light-matter interaction journal September 2019
Identifying an efficient, thermally robust inorganic phosphor host via machine learning journal October 2018
Machine learning modeling of superconducting critical temperature journal June 2018
Predicting superhard materials via a machine learning informed evolutionary structure search journal September 2019
Data-driven prediction of diamond-like infrared nonlinear optical crystals with targeting performances journal February 2020
Self-assembly as a key player for materials nanoarchitectonics journal January 2019
Stochastic replica voting machine prediction of stable cubic and double perovskite materials and binary alloys journal June 2019
Application of Computational Biology and Artificial Intelligence Technologies in Cancer Precision Drug Discovery journal November 2019
Reliable and Explainable Machine Learning Methods for Accelerated Material Discovery text January 2019
Universal fragment descriptors for predicting properties of inorganic crystals text January 2017
Finding New Perovskite Halides via Machine Learning journal April 2016
Machine Learning and Materials Informatics: Recent Applications and Prospects preprint January 2017
AFLOW-ML: A RESTful API for machine-learning predictions of materials properties preprint January 2017
Graph Neural Network for Hamiltonian-Based Material Property Prediction preprint January 2020
Global property prediction: A benchmark study on open source, perovskite-like datasets preprint January 2020
Thermodynamic Stability Landscape of Halide Double Perovskites via High-Throughput Computing and Machine Learning journal January 2019
Machine Learning Augmented Discovery of Chalcogenide Double Perovskites for Photovoltaics journal March 2019
Machine Learning Stability and Bandgaps of Lead‐Free Perovskites for Photovoltaics journal November 2019
Machine Learning Augmented Discovery of Chalcogenide Double Perovskites for Photovoltaics journal November 2019
Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra journal January 2019
Data‐Driven Materials Science: Status, Challenges, and Perspectives journal November 2019
Predictions and Strategies Learned from Machine Learning to Develop High‐Performing Perovskite Solar Cells journal October 2019
A Critical Review of Machine Learning of Energy Materials journal January 2020
Data Mining the C−C Cross‐Coupling Genome journal May 2019
Statistical Analysis and Discovery of Heterogeneous Catalysts Based on Machine Learning from Diverse Published Data journal August 2019
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
Machine learning properties of binary wurtzite superlattices journal January 2018
Data-enabled structure–property mappings for lanthanide-activated inorganic scintillators journal February 2019
A Statistical Learning Framework for Accelerated Bandgap Prediction of Inorganic Compounds journal November 2019
Process-Structure-Property Modeling for Severe Plastic Deformation Processes Using Orientation Imaging Microscopy and Data-Driven Techniques journal March 2019
Universal fragment descriptors for predicting properties of inorganic crystals journal June 2017
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
Deep neural networks for accurate predictions of crystal stability journal September 2018
Machine learning in materials informatics: recent applications and prospects journal December 2017
A strategy to apply machine learning to small datasets in materials science journal May 2018
Empirical modeling of dopability in diamond-like semiconductors journal December 2018
Active learning for accelerated design of layered materials journal December 2018
Solving the electronic structure problem with machine learning journal February 2019
Bandgap prediction by deep learning in configurationally hybridized graphene and boron nitride journal February 2019
Identifying Pb-free perovskites for solar cells by machine learning journal March 2019
Recent advances and applications of machine learning in solid-state materials science journal August 2019
Crowd-sourcing materials-science challenges with the NOMAD 2018 Kaggle competition journal November 2019
Reliable and explainable machine-learning methods for accelerated material discovery journal November 2019
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
Machine learning assisted optimization of electrochemical properties for Ni-rich cathode materials journal October 2018
Interaction trends between single metal atoms and oxide supports identified with density functional theory and statistical learning journal July 2018
A hybrid organic-inorganic perovskite dataset journal May 2017
Predicting electronic structure properties of transition metal complexes with neural networks journal January 2017
Computer-aided design of metal chalcohalide semiconductors: from chemical composition to crystal structure journal January 2018
The impact of chemical order on defect transport in mixed pyrochlores journal January 2019
Classification of spatially resolved molecular fingerprints for machine learning applications and development of a codebase for their implementation journal January 2018
(C 3 H 9 NI) 4 AgBiI 8 : a direct-bandgap layered double perovskite based on a short-chain spacer cation for light absorption journal January 2020
Bulk and surface DFT investigations of inorganic halide perovskites screened using machine learning and materials property databases journal January 2019
Machine learning for renewable energy materials journal January 2019
Novel stable structure of Li 3 PS 4 predicted by evolutionary algorithm under high-pressure journal January 2018
Predicting the stability of ternary intermetallics with density functional theory and machine learning journal June 2018
Physics-informed machine learning for inorganic scintillator discovery journal June 2018
From DFT to machine learning: recent approaches to materials science–a review journal May 2019
Towards photoferroic materials by design: recent progress and perspectives journal November 2019
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
Group-theoretical high-order rotational invariants for structural representations: Application to linearized machine learning interatomic potential journal June 2019
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
Machine learning the band gap properties of kesterite I 2 − II − IV − V 4 quaternary compounds for photovoltaics applications journal August 2018
Alternative materials for perovskite solar cells from materials informatics journal July 2019
Growing field of materials informatics: databases and artificial intelligence journal January 2020
A General-Purpose Machine Learning Framework for Predicting Properties of Inorganic Materials text January 2016
Representation of compounds for machine-learning prediction of physical properties text January 2016
Machine learning modeling of superconducting critical temperature text January 2017
Deep Neural Networks for Accurate Predictions of Garnet Stability text January 2017
Assessment of the GLLB-SC potential for solid-state properties and attempts for improvement text January 2017
Machine learning with force-field inspired descriptors for materials: fast screening and mapping energy landscape text January 2018
Group-theoretical high-order rotational invariants for structural representations: Application to linearized machine learning interatomic potential text January 2019
Towards Photoferroic Materials by Design: Recent Progresses and Perspective text January 2019
Predicting Superhard Materials via a Machine Learning Informed Evolutionary Structure Search preprint January 2019
Data-driven materials science: status, challenges and perspectives text January 2019
Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra text January 2019

Similar Records

Multi-fidelity machine learning models for accurate bandgap predictions of solids
Journal Article · Tue Dec 27 19:00:00 EST 2016 · Computational Materials Science · OSTI ID:1352377
Finding new perovskite halides via machine learning
Journal Article · Mon Apr 25 20:00:00 EDT 2016 · Frontiers in Materials · OSTI ID:1258584
A Machine Learning Approach for the Prediction of Formability and Thermodynamic Stability of Single and Double Perovskite Oxides
Journal Article · Thu Jan 14 19:00:00 EST 2021 · Chemistry of Materials · OSTI ID:1822781

Related Subjects

36 MATERIALS SCIENCE
computational methods
electronic
properties and materials
statistics