DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Machine learning strategy for accelerated design of polymer dielectrics

Abstract

The ability to efficiently design new and advanced dielectric polymers is hampered by the lack of sufficient, reliable data on wide polymer chemical spaces, and the difficulty of generating such data given time and computational/experimental constraints. Here, we address the issue of accelerating polymer dielectrics design by extracting learning models from data generated by accurate state-of-the-art first principles computations for polymers occupying an important part of the chemical subspace. The polymers are ‘fingerprinted’ as simple, easily attainable numerical representations, which are mapped to the properties of interest using a machine learning algorithm to develop an on-demand property prediction model. Further, a genetic algorithm is utilised to optimise polymer constituent blocks in an evolutionary manner, thus directly leading to the design of polymers with given target properties. Furthermore, while this philosophy of learning to make instant predictions and design is demonstrated here for the example of polymer dielectrics, it is equally applicable to other classes of materials as well.

Authors:
 [1];  [2];  [1];  [2];  [1]
  1. Univ. of Connecticut, Storrs, CT (United States)
  2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1248893
Report Number(s):
LA-UR-15-26906
Journal ID: ISSN 2045-2322; srep20952
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 devices

Citation Formats

Mannodi-Kanakkithodi, Arun, Pilania, Ghanshyam, Huan, Tran Doan, Lookman, Turab, and Ramprasad, Rampi. Machine learning strategy for accelerated design of polymer dielectrics. United States: N. p., 2016. Web. doi:10.1038/srep20952.
Mannodi-Kanakkithodi, Arun, Pilania, Ghanshyam, Huan, Tran Doan, Lookman, Turab, & Ramprasad, Rampi. Machine learning strategy for accelerated design of polymer dielectrics. United States. https://doi.org/10.1038/srep20952
Mannodi-Kanakkithodi, Arun, Pilania, Ghanshyam, Huan, Tran Doan, Lookman, Turab, and Ramprasad, Rampi. Mon . "Machine learning strategy for accelerated design of polymer dielectrics". United States. https://doi.org/10.1038/srep20952. https://www.osti.gov/servlets/purl/1248893.
@article{osti_1248893,
title = {Machine learning strategy for accelerated design of polymer dielectrics},
author = {Mannodi-Kanakkithodi, Arun and Pilania, Ghanshyam and Huan, Tran Doan and Lookman, Turab and Ramprasad, Rampi},
abstractNote = {The ability to efficiently design new and advanced dielectric polymers is hampered by the lack of sufficient, reliable data on wide polymer chemical spaces, and the difficulty of generating such data given time and computational/experimental constraints. Here, we address the issue of accelerating polymer dielectrics design by extracting learning models from data generated by accurate state-of-the-art first principles computations for polymers occupying an important part of the chemical subspace. The polymers are ‘fingerprinted’ as simple, easily attainable numerical representations, which are mapped to the properties of interest using a machine learning algorithm to develop an on-demand property prediction model. Further, a genetic algorithm is utilised to optimise polymer constituent blocks in an evolutionary manner, thus directly leading to the design of polymers with given target properties. Furthermore, while this philosophy of learning to make instant predictions and design is demonstrated here for the example of polymer dielectrics, it is equally applicable to other classes of materials as well.},
doi = {10.1038/srep20952},
journal = {Scientific Reports},
number = ,
volume = 6,
place = {United States},
year = {2016},
month = {2}
}

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

Citation Metrics:
Cited by: 20 works
Citation information provided by
Web of Science

Save / Share:

Works referenced in this record:

Dielectric properties of carbon-, silicon-, and germanium-based polymers: A first-principles study
journal, January 2013


Computational high-throughput screening of electrocatalytic materials for hydrogen evolution
journal, October 2006

  • Greeley, Jeff; Jaramillo, Thomas F.; Bonde, Jacob
  • Nature Materials, Vol. 5, Issue 11, p. 909-913
  • DOI: 10.1038/nmat1752

A high-mobility electron-transporting polymer for printed transistors
journal, January 2009

  • Yan, He; Chen, Zhihua; Zheng, Yan
  • Nature, Vol. 457, Issue 7230, p. 679-686
  • DOI: 10.1038/nature07727

Projector augmented-wave method
journal, December 1994


Hybrid functionals based on a screened Coulomb potential
journal, May 2003

  • Heyd, Jochen; Scuseria, Gustavo E.; Ernzerhof, Matthias
  • The Journal of Chemical Physics, Vol. 118, Issue 18
  • DOI: 10.1063/1.1564060

Energy band gaps and lattice parameters evaluated with the Heyd-Scuseria-Ernzerhof screened hybrid functional
journal, November 2005

  • Heyd, Jochen; Peralta, Juan E.; Scuseria, Gustavo E.
  • The Journal of Chemical Physics, Vol. 123, Issue 17
  • DOI: 10.1063/1.2085170

Probabilistic machine learning and artificial intelligence
journal, May 2015


Understanding kernel ridge regression: Common behaviors from simple functions to density functionals
journal, May 2015

  • Vu, Kevin; Snyder, John C.; Li, Li
  • International Journal of Quantum Chemistry, Vol. 115, Issue 16
  • DOI: 10.1002/qua.24939

Crystal structure prediction using the Minima Hopping method
text, January 2010


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

Ab initiomolecular dynamics for liquid metals
journal, January 1993


Big Data of Materials Science - Critical Role of the Descriptor
text, January 2014


Chemical accuracy for the van der Waals density functional
preprint, January 2009


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


Electronic structure of AlFeN films exhibiting crystallographic orientation change from c- to a-axis with Fe concentrations and annealing effect
journal, February 2020


Phonons and Lattice Dielectric Properties of Zirconia
text, January 2001


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


Rational design and synthesis of polythioureas as capacitor dielectrics
journal, January 2015

  • Ma, Rui; Sharma, Vinit; Baldwin, Aaron F.
  • Journal of Materials Chemistry A, Vol. 3, Issue 28
  • DOI: 10.1039/C5TA01252J

New Group IV Chemical Motifs for Improved Dielectric Permittivity of Polyethylene
journal, April 2013

  • Pilania, G.; Wang, C. C.; Wu, K.
  • Journal of Chemical Information and Modeling, Vol. 53, Issue 4
  • DOI: 10.1021/ci400033h

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


Accelerated materials property predictions and design using motif-based fingerprints
text, January 2015


Rational Design of Organotin Polyesters
journal, April 2015

  • Baldwin, Aaron F.; Huan, Tran Doan; Ma, Rui
  • Macromolecules, Vol. 48, Issue 8
  • DOI: 10.1021/ma502424r

Finding Nature’s Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory
journal, June 2010

  • Hautier, Geoffroy; Fischer, Christopher C.; Jain, Anubhav
  • Chemistry of Materials, Vol. 22, Issue 12
  • DOI: 10.1021/cm100795d

Minima hopping: An efficient search method for the global minimum of the potential energy surface of complex molecular systems
journal, June 2004

  • Goedecker, Stefan
  • The Journal of Chemical Physics, Vol. 120, Issue 21
  • DOI: 10.1063/1.1724816

Poly(dimethyltin glutarate) as a Prospective Material for High Dielectric Applications
journal, November 2014

  • Baldwin, Aaron F.; Ma, Rui; Mannodi-Kanakkithodi, Arun
  • Advanced Materials, Vol. 27, Issue 2
  • DOI: 10.1002/adma.201404162

Polarization-Based Calculation of the Dielectric Tensor of Polar Crystals
journal, November 1997

  • Bernardini, Fabio; Fiorentini, Vincenzo; Vanderbilt, David
  • Physical Review Letters, Vol. 79, Issue 20
  • DOI: 10.1103/PhysRevLett.79.3958

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

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
text, January 2011


Chemical accuracy for the van der Waals density functional
journal, December 2009

  • Klimeš, Jiří; Bowler, David R.; Michaelides, Angelos
  • Journal of Physics: Condensed Matter, Vol. 22, Issue 2
  • DOI: 10.1088/0953-8984/22/2/022201

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


Phonons and lattice dielectric properties of zirconia
journal, January 2002


A polymer high-k dielectric insulator for organic field-effect transistors
journal, September 2005

  • Müller, Klaus; Paloumpa, Ioanna; Henkel, Karsten
  • Journal of Applied Physics, Vol. 98, Issue 5
  • DOI: 10.1063/1.2032611

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

Conducting Boron Sheets Formed by the Reconstruction of the α -Boron (111) Surface
journal, September 2013


Compounds based on Group 14 elements: building blocks for advanced insulator dielectrics design
journal, October 2014

  • Mannodi-Kanakkithodi, A.; Wang, C. C.; Ramprasad, R.
  • Journal of Materials Science, Vol. 50, Issue 2
  • DOI: 10.1007/s10853-014-8640-2

Parallel Nanoimprint Forming of One-Dimensional Chiral Semiconductor for Strain-Engineered Optical Properties
journal, August 2020


Performance of genetic algorithms in search for water splitting perovskites
journal, May 2013

  • Jain, Anubhav; Castelli, Ivano E.; Hautier, Geoffroy
  • Journal of Materials Science, Vol. 48, Issue 19
  • DOI: 10.1007/s10853-013-7448-9

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

Phonons and related crystal properties from density-functional perturbation theory
journal, July 2001

  • Baroni, Stefano; de Gironcoli, Stefano; Dal Corso, Andrea
  • Reviews of Modern Physics, Vol. 73, Issue 2
  • DOI: 10.1103/RevModPhys.73.515

Pathways Towards Ferroelectricity in Hafnia
text, January 2014


Fast and accurate modeling of molecular atomization energies with machine learning
text, January 2012

  • Rupp, Matthias; Tkatchenko, Alexandre; Müller, Klaus-Robert
  • American Physical Society
  • DOI: 10.5451/unibas-ep43360

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

Inhomogeneous Electron Gas
journal, November 1964


Inhomogeneous Electron Gas
journal, March 1973


Pathways towards ferroelectricity in hafnia
journal, August 2014


Crystal structure prediction using the minima hopping method
journal, December 2010

  • Amsler, Maximilian; Goedecker, Stefan
  • The Journal of Chemical Physics, Vol. 133, Issue 22
  • DOI: 10.1063/1.3512900

π-Conjugated Polymers for Organic Electronics and Photovoltaic Cell Applications
journal, February 2011


Genetic-Algorithm Discovery of a Direct-Gap and Optically Allowed Superstructure from Indirect-Gap Si and Ge Semiconductors
journal, January 2012


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


Works referencing / citing this record:

Machine-learned impurity level prediction for semiconductors: the example of Cd-based chalcogenides
journal, April 2020

  • Mannodi-Kanakkithodi, Arun; Toriyama, Michael Y.; Sen, Fatih G.
  • npj Computational Materials, Vol. 6, Issue 1
  • DOI: 10.1038/s41524-020-0296-7

Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design
journal, February 2019

  • Lookman, Turab; Balachandran, Prasanna V.; Xue, Dezhen
  • npj Computational Materials, Vol. 5, Issue 1
  • DOI: 10.1038/s41524-019-0153-8

Machine learning assisted optimization of electrochemical properties for Ni-rich cathode materials
journal, October 2018


Challenges and opportunities of polymer design with machine learning and high throughput experimentation
journal, May 2019

  • Kumar, Jatin N.; Li, Qianxiao; Jun, Ye
  • MRS Communications, Vol. 9, Issue 02
  • DOI: 10.1557/mrc.2019.54

Rational Co-Design of Polymer Dielectrics for Energy Storage
journal, May 2016

  • Mannodi-Kanakkithodi, Arun; Treich, Gregory M.; Huan, Tran Doan
  • Advanced Materials, Vol. 28, Issue 30
  • DOI: 10.1002/adma.201600377

A deep neural network model for packing density predictions and its application in the study of 1.5 million organic molecules
journal, January 2019

  • Afzal, Mohammad Atif Faiz; Sonpal, Aditya; Haghighatlari, Mojtaba
  • Chemical Science, Vol. 10, Issue 36
  • DOI: 10.1039/c9sc02677k

Combining first-principles and data modeling for the accurate prediction of the refractive index of organic polymers
journal, June 2018

  • Afzal, Mohammad Atif Faiz; Cheng, Chong; Hachmann, Johannes
  • The Journal of Chemical Physics, Vol. 148, Issue 24
  • DOI: 10.1063/1.5007873

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

Machine learning enables polymer cloud-point engineering via inverse design
journal, July 2019

  • Kumar, Jatin N.; Li, Qianxiao; Tang, Karen Y. T.
  • npj Computational Materials, Vol. 5, Issue 1
  • DOI: 10.1038/s41524-019-0209-9

Growing field of materials informatics: databases and artificial intelligence
journal, January 2020

  • Lopez-Bezanilla, Alejandro; Littlewood, Peter B.
  • MRS Communications, Vol. 10, Issue 1
  • DOI: 10.1557/mrc.2020.2

High‐Throughput Combinatorial Optimizations of Perovskite Light‐Emitting Diodes Based on All‐Vacuum Deposition
journal, October 2019

  • Li, Jinghui; Du, Peipei; Li, Shunran
  • Advanced Functional Materials, Vol. 29, Issue 51
  • DOI: 10.1002/adfm.201903607

Impact of dataset uncertainties on machine learning model predictions: the example of polymer glass transition temperatures
journal, January 2019

  • Jha, Anurag; Chandrasekaran, Anand; Kim, Chiho
  • Modelling and Simulation in Materials Science and Engineering, Vol. 27, Issue 2
  • DOI: 10.1088/1361-651x/aaf8ca

Predicting the stability of ternary intermetallics with density functional theory and machine learning
journal, June 2018

  • Schmidt, Jonathan; Chen, Liming; Botti, Silvana
  • The Journal of Chemical Physics, Vol. 148, Issue 24
  • DOI: 10.1063/1.5020223

Python for Scientific Computing
journal, January 2007


Design of multifunctional supercapacitor electrodes using an informatics approach
journal, January 2019

  • Patel, Anish G.; Johnson, Luke; Arroyave, Raymundo
  • Molecular Systems Design & Engineering, Vol. 4, Issue 3
  • DOI: 10.1039/c8me00060c

A universal strategy for the creation of machine learning-based atomistic force fields
journal, September 2017


A polymer dataset for accelerated property prediction and design
journal, March 2016

  • Huan, Tran Doan; Mannodi-Kanakkithodi, Arun; Kim, Chiho
  • Scientific Data, Vol. 3, Issue 1
  • DOI: 10.1038/sdata.2016.12

Neural Network Analysis of Dynamic Fracture in a Layered Material
journal, January 2019

  • Rajak, Pankaj; Kalia, Rajiv K.; Nakano, Aiichiro
  • MRS Advances, Vol. 4, Issue 19
  • DOI: 10.1557/adv.2018.673

Machine learning for composite materials
journal, March 2019


Learning physical descriptors for materials science by compressed sensing
journal, February 2017

  • Ghiringhelli, Luca M.; Vybiral, Jan; Ahmetcik, Emre
  • New Journal of Physics, Vol. 19, Issue 2
  • DOI: 10.1088/1367-2630/aa57bf

Pressure-stabilized binary compounds of magnesium and silicon
journal, February 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

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

A Transfer Learning Approach for Microstructure Reconstruction and Structure-property Predictions
journal, September 2018


Machine learning properties of binary wurtzite superlattices
journal, January 2018


Applying machine learning techniques to predict the properties of energetic materials
journal, June 2018


Soft Matter Informatics: Current Progress and Challenges
journal, November 2018

  • Peerless, James S.; Milliken, Nina J. B.; Oweida, Thomas J.
  • Advanced Theory and Simulations, Vol. 2, Issue 1
  • DOI: 10.1002/adts.201800129

Enabling technologies in polymer synthesis: accessing a new design space for advanced polymer materials
journal, January 2020

  • Knox, Stephen T.; Warren, Nicholas J.
  • Reaction Chemistry & Engineering, Vol. 5, Issue 3
  • DOI: 10.1039/c9re00474b

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

Machine-Learning-Assisted De Novo Design of Organic Molecules and Polymers: Opportunities and Challenges
journal, January 2020


A self-attention based message passing neural network for predicting molecular lipophilicity and aqueous solubility
journal, February 2020


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

Benchmarking DFT approaches for the calculation of polarizability inputs for refractive index predictions in organic polymers
journal, January 2019

  • Afzal, Mohammad Atif Faiz; Hachmann, Johannes
  • Physical Chemistry Chemical Physics, Vol. 21, Issue 8
  • DOI: 10.1039/c8cp05492d

Accelerated search for BaTiO 3 -based piezoelectrics with vertical morphotropic phase boundary using Bayesian learning
journal, November 2016

  • Xue, Dezhen; Balachandran, Prasanna V.; Yuan, Ruihao
  • Proceedings of the National Academy of Sciences, Vol. 113, Issue 47
  • DOI: 10.1073/pnas.1607412113

Data-enabled structure–property mappings for lanthanide-activated inorganic scintillators
journal, February 2019


Machine-learning-assisted discovery of polymers with high thermal conductivity using a molecular design algorithm
journal, June 2019


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

iQSPR in XenonPy: A Bayesian Molecular Design Algorithm
journal, November 2019

  • Wu, Stephen; Lambard, Guillaume; Liu, Chang
  • Molecular Informatics, Vol. 39, Issue 1-2
  • DOI: 10.1002/minf.201900107

Silicon‐containing fluorenylacetylene resins with low curing temperature and high thermal stability
journal, July 2019

  • Lu, Liewei; Guo, Kangkang; Zhu, Junli
  • Journal of Applied Polymer Science, Vol. 136, Issue 48
  • DOI: 10.1002/app.48262

Active-learning and materials design: the example of high glass transition temperature polymers
journal, June 2019

  • Kim, Chiho; Chandrasekaran, Anand; Jha, Anurag
  • MRS Communications, Vol. 9, Issue 3
  • DOI: 10.1557/mrc.2019.78

Finding New Perovskite Halides via Machine Learning
journal, April 2016

  • Pilania, Ghanshyam; Balachandran, Prasanna V.; Kim, Chiho
  • Frontiers in Materials, Vol. 3
  • DOI: 10.3389/fmats.2016.00019

Layered structures of organic/inorganic hybrid halide perovskites
text, January 2015


Application of Computational Biology and Artificial Intelligence Technologies in Cancer Precision Drug Discovery
journal, November 2019

  • Nagarajan, Nagasundaram; Yapp, Edward K. Y.; Le, Nguyen Quoc Khanh
  • BioMed Research International, Vol. 2019
  • DOI: 10.1155/2019/8427042

Solving the electronic structure problem with machine learning
journal, February 2019

  • Chandrasekaran, Anand; Kamal, Deepak; Batra, Rohit
  • npj Computational Materials, Vol. 5, Issue 1
  • DOI: 10.1038/s41524-019-0162-7

Bulk and surface DFT investigations of inorganic halide perovskites screened using machine learning and materials property databases
journal, January 2019

  • Jain, Deepak; Chaube, Suryanaman; Khullar, Prerna
  • Physical Chemistry Chemical Physics, Vol. 21, Issue 35
  • DOI: 10.1039/c9cp03240a

Machine learning in materials design and discovery: Examples from the present and suggestions for the future
journal, December 2018


Layered structures of organic/inorganic hybrid halide perovskites
journal, March 2016