Finding new perovskite halides via machine learning
Abstract
Advanced materials with improved properties have the potential to fuel future technological advancements. However, identification and discovery of these optimal materials for a specific application is a non-trivial task, because of the vastness of the chemical search space with enormous compositional and configurational degrees of freedom. Materials informatics provides an efficient approach toward rational design of new materials, via learning from known data to make decisions on new and previously unexplored compounds in an accelerated manner. Here, we demonstrate the power and utility of such statistical learning (or machine learning, henceforth referred to as ML) via building a support vector machine (SVM) based classifier that uses elemental features (or descriptors) to predict the formability of a given ABX3 halide composition (where A and B represent monovalent and divalent cations, respectively, and X is F, Cl, Br, or I anion) in the perovskite crystal structure. The classification model is built by learning from a dataset of 185 experimentally known ABX3 compounds. After exploring a wide range of features, we identify ionic radii, tolerance factor, and octahedral factor to be the most important factors for the classification, suggesting that steric and geometric packing effects govern the stability of these halides. As amore »
- Authors:
-
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Univ. of Connecticut, Storrs, CT (United States)
- Publication Date:
- Research Org.:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1258584
- Report Number(s):
- LA-UR-16-21643
Journal ID: ISSN 2296-8016
- Grant/Contract Number:
- AC52-06NA25396
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Frontiers in Materials
- Additional Journal Information:
- Journal Volume: 3; Journal ID: ISSN 2296-8016
- Publisher:
- Frontiers Research Foundation
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 36 MATERIALS SCIENCE; perovskites; informatics; support vector machines; formability; materials discovery
Citation Formats
Pilania, Ghanshyam, Balachandran, Prasanna V., Kim, Chiho, and Lookman, Turab. Finding new perovskite halides via machine learning. United States: N. p., 2016.
Web. doi:10.3389/fmats.2016.00019.
Pilania, Ghanshyam, Balachandran, Prasanna V., Kim, Chiho, & Lookman, Turab. Finding new perovskite halides via machine learning. United States. https://doi.org/10.3389/fmats.2016.00019
Pilania, Ghanshyam, Balachandran, Prasanna V., Kim, Chiho, and Lookman, Turab. Tue .
"Finding new perovskite halides via machine learning". United States. https://doi.org/10.3389/fmats.2016.00019. https://www.osti.gov/servlets/purl/1258584.
@article{osti_1258584,
title = {Finding new perovskite halides via machine learning},
author = {Pilania, Ghanshyam and Balachandran, Prasanna V. and Kim, Chiho and Lookman, Turab},
abstractNote = {Advanced materials with improved properties have the potential to fuel future technological advancements. However, identification and discovery of these optimal materials for a specific application is a non-trivial task, because of the vastness of the chemical search space with enormous compositional and configurational degrees of freedom. Materials informatics provides an efficient approach toward rational design of new materials, via learning from known data to make decisions on new and previously unexplored compounds in an accelerated manner. Here, we demonstrate the power and utility of such statistical learning (or machine learning, henceforth referred to as ML) via building a support vector machine (SVM) based classifier that uses elemental features (or descriptors) to predict the formability of a given ABX3 halide composition (where A and B represent monovalent and divalent cations, respectively, and X is F, Cl, Br, or I anion) in the perovskite crystal structure. The classification model is built by learning from a dataset of 185 experimentally known ABX3 compounds. After exploring a wide range of features, we identify ionic radii, tolerance factor, and octahedral factor to be the most important factors for the classification, suggesting that steric and geometric packing effects govern the stability of these halides. As a result, the trained and validated models then predict, with a high degree of confidence, several novel ABX3 compositions with perovskite crystal structure.},
doi = {10.3389/fmats.2016.00019},
journal = {Frontiers in Materials},
number = ,
volume = 3,
place = {United States},
year = {Tue Apr 26 00:00:00 EDT 2016},
month = {Tue Apr 26 00:00:00 EDT 2016}
}
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Works referenced in this record:
Materials Prediction via Classification Learning
journal, August 2015
- Balachandran, Prasanna V.; Theiler, James; Rondinelli, James M.
- Scientific Reports, Vol. 5, Issue 1
Adaptive Strategies for Materials Design using Uncertainties
journal, January 2016
- Balachandran, Prasanna V.; Xue, Dezhen; Theiler, James
- Scientific Reports, Vol. 6, Issue 1
Bond valences—a simple structural model for inorganic chemistry
journal, January 1978
- Brown, I. D.
- Chem. Soc. Rev., Vol. 7, Issue 3
Recharging lithium battery research with first-principles methods
journal, March 2011
- Ceder, G.; Hautier, G.; Jain, A.
- MRS Bulletin, Vol. 36, Issue 3
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
AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations
journal, June 2012
- Curtarolo, Stefano; Setyawan, Wahyu; Wang, Shidong
- Computational Materials Science, Vol. 58
Formability of ABO3 cubic perovskites
journal, April 2008
- Feng, L. M.; Jiang, L. Q.; Zhu, M.
- Journal of Physics and Chemistry of Solids, Vol. 69, Issue 4
A high-throughput infrastructure for density functional theory calculations
journal, June 2011
- Jain, Anubhav; Hautier, Geoffroy; Moore, Charles J.
- Computational Materials Science, Vol. 50, Issue 8
High pressure synthesis of (ABX3) (AX)n compounds
journal, January 1972
- Kafalas, J. A.; Longo, J. M.
- Journal of Solid State Chemistry, Vol. 4, Issue 1
From Organized High-Throughput Data to Phenomenological Theory using Machine Learning: The Example of Dielectric Breakdown
journal, February 2016
- Kim, Chiho; Pilania, Ghanshyam; Ramprasad, Ramamurthy
- Chemistry of Materials, Vol. 28, Issue 5
Prediction of Formability in Perovskite-Type Oxides~!2008-08-05~!2008-10-08~!2008-12-05~!
journal, December 2008
- Kumar, A.; Verma, A. S.; Bhardwaj, S. R.
- The Open Applied Physics Journal, Vol. 1, Issue 1
Formability of ABX 3 ( X = F, Cl, Br, I) halide perovskites
journal, November 2008
- Li, Chonghea; Lu, Xionggang; Ding, Weizhong
- Acta Crystallographica Section B Structural Science, Vol. 64, Issue 6
Formability of ABO3 perovskites
journal, June 2004
- Li, Chonghe; Soh, Kitty Chi Kwan; Wu, Ping
- Journal of Alloys and Compounds, Vol. 372, Issue 1-2
Machine Learning Strategy for Accelerated Design of Polymer Dielectrics
journal, February 2016
- Mannodi-Kanakkithodi, Arun; Pilania, Ghanshyam; Huan, Tran Doan
- Scientific Reports, Vol. 6, Issue 1
On the crystal chemistry of normal valence compounds
journal, December 1959
- Mooser, E.; Pearson, W. B.
- Acta Crystallographica, Vol. 12, Issue 12
The Major Ternary Structural Families
book, January 1974
- Muller, Olaf; Roy, Rustum
- Springer-Verlag, New York
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
Structure classification and melting temperature prediction in octet AB solids via machine learning
journal, June 2015
- Pilania, G.; Gubernatis, J. E.; Lookman, T.
- Physical Review B, Vol. 91, Issue 21
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
Electronic structure and biaxial strain in perovskite and hybrid improper ferroelectricity in and superlattices
journal, September 2014
- Pilania, Ghanshyam; Lookman, Turab
- Physical Review B, Vol. 90, Issue 11
Machine learning bandgaps of double perovskites
journal, January 2016
- Pilania, G.; Mannodi-Kanakkithodi, A.; Uberuaga, B. P.
- Scientific Reports, Vol. 6, Issue 1
Cation ordering and effect of biaxial strain in double perovskite CsRbCaZnCl 6
journal, March 2015
- Pilania, G.; Uberuaga, B. P.
- Journal of Applied Physics, Vol. 117, Issue 11
Accelerating materials property predictions using machine learning
journal, September 2013
- Pilania, Ghanshyam; Wang, Chenchen; Jiang, Xun
- Scientific Reports, Vol. 3, Issue 1
Global multinary structural chemistry of stable quasicrystals, high- ferroelectrics, and high- superconductors
journal, April 1992
- Rabe, K. M.; Phillips, J. C.; Villars, P.
- Physical Review B, Vol. 45, Issue 14
Proposed design principle of fluoride-based materials for deep ultraviolet light emitting devices
journal, September 2007
- Sarukura, Nobuhiko; Murakami, Hidetoshi; Estacio, Elmer
- Optical Materials, Vol. 30, Issue 1
Materials Scientists Look to a Data-Intensive Future
journal, March 2012
- Service, R. F.
- Science, Vol. 335, Issue 6075
Rational design of all organic polymer dielectrics
journal, September 2014
- Sharma, Vinit; Wang, Chenchen; Lorenzini, Robert G.
- Nature Communications, Vol. 5, Issue 1
Effect of SiO2, NaCl, Al2O3, and FeCl3 on Phase Change Behavior of Supported and Unsupported TiO2
journal, October 1993
- Eskelinen, Pekka
- Journal of Solid State Chemistry, Vol. 106, Issue 2
Identification of Potential Photovoltaic Absorbers Based on First-Principles Spectroscopic Screening of Materials
journal, February 2012
- Yu, Liping; Zunger, Alex
- Physical Review Letters, Vol. 108, Issue 6
Green Synthesis and Property Characterization of Single-Crystalline Perovskite Fluoride Nanorods
journal, December 2007
- Zhang, F.; Mao, Y.; Park, T. -J.
- Advanced Functional Materials, Vol. 18, Issue 1
Structural stability and formability of AB O 3 -type perovskite compounds
journal, November 2007
- Zhang, Huan; Li, Na; Li, Keyan
- Acta Crystallographica Section B Structural Science, Vol. 63, Issue 6
Simple descriptor derived from symbolic regression accelerating the discovery of new perovskite catalysts
journal, July 2020
- Weng, Baicheng; Song, Zhilong; Zhu, Rilong
- Nature Communications, Vol. 11, Issue 1
The Nature of Statistical Learning Theory
book, January 2000
- Vapnik, Vladimir N.
- Springer New York, NY
The Nature Of Statistical Learning Theory~
journal, November 1997
- Cherkassky, V.
- IEEE Transactions on Neural Networks, Vol. 8, Issue 6
Green Synthesis and Property Characterization of Single-Crystalline Perovskite Fluoride Nanorods
journal, December 2007
- Zhang, F.; Mao, Y.; Park, T. -J.
- Advanced Functional Materials, Vol. 18, Issue 1
High pressure synthesis of (ABX3) (AX)n compounds
journal, January 1972
- Kafalas, J. A.; Longo, J. M.
- Journal of Solid State Chemistry, Vol. 4, Issue 1
AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations
journal, June 2012
- Curtarolo, Stefano; Setyawan, Wahyu; Wang, Shidong
- Computational Materials Science, Vol. 58
Formability of ABO3 perovskites
journal, June 2004
- Li, Chonghe; Soh, Kitty Chi Kwan; Wu, Ping
- Journal of Alloys and Compounds, Vol. 372, Issue 1-2
Proposed design principle of fluoride-based materials for deep ultraviolet light emitting devices
journal, September 2007
- Sarukura, Nobuhiko; Murakami, Hidetoshi; Estacio, Elmer
- Optical Materials, Vol. 30, Issue 1
Rational design of all organic polymer dielectrics
journal, September 2014
- Sharma, Vinit; Wang, Chenchen; Lorenzini, Robert G.
- Nature Communications, Vol. 5, Issue 1
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
Coupling structural evolution and oxygen-redox electrochemistry in layered transition metal oxides
journal, March 2022
- Eum, Donggun; Kim, Byunghoon; Song, Jun-Hyuk
- Nature Materials, Vol. 21, Issue 6
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
Machine learning bandgaps of double perovskites
journal, January 2016
- Pilania, G.; Mannodi-Kanakkithodi, A.; Uberuaga, B. P.
- Scientific Reports, Vol. 6, Issue 1
Adaptive Strategies for Materials Design using Uncertainties
journal, January 2016
- Balachandran, Prasanna V.; Xue, Dezhen; Theiler, James
- Scientific Reports, Vol. 6, Issue 1
Machine Learning Strategy for Accelerated Design of Polymer Dielectrics
journal, February 2016
- Mannodi-Kanakkithodi, Arun; Pilania, Ghanshyam; Huan, Tran Doan
- Scientific Reports, Vol. 6, Issue 1
First-principles nonlocal-pseudopotential approach in the density-functional formalism. II. Application to electronic and structural properties of solids
journal, November 1979
- Zunger, Alex; Cohen, Marvin L.
- Physical Review B, Vol. 20, Issue 10
Recharging lithium battery research with first-principles methods
journal, March 2011
- Ceder, G.; Hautier, G.; Jain, A.
- MRS Bulletin, Vol. 36, Issue 3
Prediction of Formability in Perovskite-Type Oxides~!2008-08-05~!2008-10-08~!2008-12-05~!
journal, December 2008
- Kumar, A.; Verma, A. S.; Bhardwaj, S. R.
- The Open Applied Physics Journal, Vol. 1, Issue 1
Machine learning bandgaps of double perovskites
dataset, January 2022
- Pilania, G.; Mannodi-Kanakkithodi, A.; Uberuaga, B. P.
- Materials Data Facility
Works referencing / citing this record:
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
Tin Halide Perovskites: Progress and Challenges
journal, April 2020
- Yang, Wen‐Fan; Igbari, Femi; Lou, Yan‐Hui
- Advanced Energy Materials, Vol. 10, Issue 13
A Critical Review of Machine Learning of Energy Materials
journal, January 2020
- Chen, Chi; Zuo, Yunxing; Ye, Weike
- Advanced Energy Materials, Vol. 10, Issue 8
Density Functional Theory - Machine Learning Approach to Analyze the Bandgap of Elemental Halide Perovskites and Ruddlesden-Popper Phases
journal, July 2018
- Allam, Omar; Holmes, Colin; Greenberg, Zev
- ChemPhysChem, Vol. 19, Issue 19
Predicting the electronic structure, magnetism, and transport properties of new Co-based Heusler alloys
journal, August 2018
- Khandy, Shakeel Ahmad; Islam, Ishtihadah; Gupta, Dinesh C.
- International Journal of Energy Research, Vol. 42, Issue 13
Halogen in materials design: Chloroammonium lead triiodide perovskite (ClNH 3 PbI 3 ) a dynamical bandgap semiconductor in 3D for photovoltaics
journal, June 2018
- Varadwaj, Pradeep R.; Varadwaj, Arpita; Marques, Helder M.
- Journal of Computational Chemistry, Vol. 39, Issue 23
Machine learning properties of binary wurtzite superlattices
journal, January 2018
- Pilania, G.; Liu, X. -Y.
- Journal of Materials Science, Vol. 53, Issue 9
Data-enabled structure–property mappings for lanthanide-activated inorganic scintillators
journal, February 2019
- Pilania, G.; Liu, Xiang-Yang; Wang, Zhehui
- Journal of Materials Science, Vol. 54, Issue 11
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
Materials Design in Digital Era: Challenges and Opportunities
journal, June 2019
- Jain, Deepak; Dwadasi, Balarama Sridhar; Kumar, Dharmendr
- Transactions of the Indian Institute of Metals, Vol. 72, Issue 8
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
Rationalizing the interphase stability of Li|doped-Li 7 La 3 Zr 2 O 12 via automated reaction screening and machine learning
journal, January 2019
- Liu, Bo; Yang, Jiong; Yang, Hongliang
- Journal of Materials Chemistry A, Vol. 7, Issue 34
White light emission in low-dimensional perovskites
journal, January 2019
- Cortecchia, Daniele; Yin, Jun; Petrozza, Annamaria
- Journal of Materials Chemistry C, Vol. 7, Issue 17
The geometric blueprint of perovskites
journal, May 2018
- Filip, Marina R.; Giustino, Feliciano
- Proceedings of the National Academy of Sciences, Vol. 115, Issue 21
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
New tolerance factor to predict the stability of perovskite oxides and halides
journal, February 2019
- Bartel, Christopher J.; Sutton, Christopher; Goldsmith, Bryan R.
- Science Advances, Vol. 5, Issue 2
Prediction of new iodine-containing apatites using machine learning and density functional theory
journal, August 2019
- Hartnett, Timothy Q.; Ayyasamy, Mukil V.; Balachandran, Prasanna V.
- MRS Communications, Vol. 9, Issue 3
Materials science in the artificial intelligence age: high-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics
journal, July 2019
- Vasudevan, Rama K.; Choudhary, Kamal; Mehta, Apurva
- MRS Communications, Vol. 9, Issue 3
New Tolerance Factor to Predict the Stability of Perovskite Oxides and Halides
text, January 2018
- Bartel, Christopher J.; Sutton, Christopher; Goldsmith, Bryan R.
- arXiv
The Geometric Blueprint of Perovskites
text, January 2018
- Filip, Marina R.; Giustino, Feliciano
- arXiv
Machine learning-guided synthesis of advanced inorganic materials
journal, December 2020
- Tang, Bijun; Lu, Yuhao; Zhou, Jiadong
- Materials Today, Vol. 41
Hunting for Organic Molecules with Artificial Intelligence: Molecules Optimized for Desired Excitation Energies
preprint, April 2018
- Sumita, Masato; Yang, Xiufeng; Ishihara, Shinsuke