Multifidelity Information Fusion with Machine Learning: A Case Study of Dopant Formation Energies in Hafnia

Batra, Rohit; Pilania, Ghanshyam; Uberuaga, Blas P.; Ramprasad, Rampi

doi:10.1021/acsami.9b02174

Title: Multifidelity Information Fusion with Machine Learning: A Case Study of Dopant Formation Energies in Hafnia

Journal Article · Tue Apr 16 00:00:00 EDT 2019 · ACS Applied Materials and Interfaces

DOI:https://doi.org/10.1021/acsami.9b02174· OSTI ID:1511635

^[1];

^[2];

^[3]

Georgia Inst. of Technology, Atlanta, GA (United States); Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Georgia Inst. of Technology, Atlanta, GA (United States)

Cost versus accuracy trade-offs are frequently encountered in materials science and engineering, where a particular property of interest can be measured/computed at different levels of accuracy or fidelity. Naturally, the most accurate measurement is also the most resource and time intensive, while the inexpensive quicker alternatives tend to be noisy. In such situations, a number of machine learning (ML) based multifidelity information fusion (MFIF) strategies can be employed to fuse information accessible from varying sources of fidelity and make predictions at the highest level of accuracy. In this work, we perform a comparative study on traditionally employed single-fidelity and three MFIF strategies, namely, (1) Δ-learning, (2) low-fidelity as a feature, and (3) multifidelity cokriging (CK) to compare their relative prediction accuracies and efficiencies for accelerated property predictions and high throughput chemical space explorations. We perform our analysis using a dopant formation energy data set for hafnia, which is a well-known high-k material and is being extensively studied for its promising ferroelectric, piezoelectric, and pyroelectric properties. We use a dopant formation energy data set of 42 dopants in hafnia—each studied in six different hafnia phases—computed at two levels of fidelities to find merits and limitations of these ML strategies. The findings of this work indicate that the MFIF based learning schemes outperform the traditional SF machine learning methods, such as Gaussian process regression and CK provides an accurate, inexpensive and flexible alternative to other MFIF strategies. While the results presented here are for the case study of hafnia, they are expected to be general. Furthermore, materials discovery problems that involve huge chemical space explorations can be studied efficiently (or even made feasible in some situations) through a combination of a large number of low-fidelity and a few high-fidelity measurements/computations, in conjunction with the CK approach.

View Accepted Manuscript (DOE)

Cite

Export

Save

Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: 89233218CNA000001

OSTI ID:: 1511635

Report Number(s):: LA-UR-19-23134

Journal Information:: ACS Applied Materials and Interfaces, Vol. 11, Issue 28; ISSN 1944-8244

Publisher:: American Chemical Society (ACS)Copyright Statement

Country of Publication:: United States

Language:: English

Citation Metrics:

Cited by: 37 works

Citation information provided by
Web of Science

References (65)

Machine learning: Trends, perspectives, and prospects Jordan, M. I.; Mitchell, T. M. Science, Vol. 349, Issue 6245 https://doi.org/10.1126/science.aaa8415	journal	July 2015
Deep learning LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey Nature, Vol. 521, Issue 7553 https://doi.org/10.1038/nature14539	journal	May 2015
Mastering the game of Go with deep neural networks and tree search Silver, David; Huang, Aja; Maddison, Chris J. Nature, Vol. 529, Issue 7587 https://doi.org/10.1038/nature16961	journal	January 2016
Machine Learning in Materials Science Mueller, Tim; Kusne, Aaron Gilad; Ramprasad, Rampi Reviews in Computational Chemistry, Vol. 29 https://doi.org/10.1002/9781119148739.ch4	book	January 2016
Machine learning for molecular and materials science Butler, Keith T.; Davies, Daniel W.; Cartwright, Hugh Nature, Vol. 559, Issue 7715 https://doi.org/10.1038/s41586-018-0337-2	journal	July 2018
Statistical inference and adaptive design for materials discovery Lookman, Turab; Balachandran, Prasanna V.; Xue, Dezhen Current Opinion in Solid State and Materials Science, Vol. 21, Issue 3 https://doi.org/10.1016/j.cossms.2016.10.002	journal	June 2017
Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science Agrawal, Ankit; Choudhary, Alok APL Materials, Vol. 4, Issue 5 https://doi.org/10.1063/1.4946894	journal	April 2016
NOMAD: The FAIR concept for big data-driven materials science Draxl, Claudia; Scheffler, Matthias MRS Bulletin, Vol. 43, Issue 9 https://doi.org/10.1557/mrs.2018.208	journal	September 2018
Machine learning in materials informatics: recent applications and prospects Ramprasad, Rampi; Batra, Rohit; Pilania, Ghanshyam npj Computational Materials, Vol. 3, Issue 1 https://doi.org/10.1038/s41524-017-0056-5	journal	December 2017
Atomistic calculations and materials informatics: A review Ward, Logan; Wolverton, Chris Current Opinion in Solid State and Materials Science, Vol. 21, Issue 3 https://doi.org/10.1016/j.cossms.2016.07.002	journal	June 2017
The 2019 materials by design roadmap Alberi, Kirstin; Nardelli, Marco Buongiorno; Zakutayev, Andriy Journal of Physics D: Applied Physics, Vol. 52, Issue 1 https://doi.org/10.1088/1361-6463/aad926	journal	October 2018
Combinatorial screening for new materials in unconstrained composition space with machine learning Meredig, B.; Agrawal, A.; Kirklin, S. Physical Review B, Vol. 89, Issue 9 https://doi.org/10.1103/PhysRevB.89.094104	journal	March 2014
Compositional descriptor-based recommender system for the materials discovery Seko, Atsuto; Hayashi, Hiroyuki; Tanaka, Isao The Journal of Chemical Physics, Vol. 148, Issue 24 https://doi.org/10.1063/1.5016210	journal	June 2018
Accelerated search for materials with targeted properties by adaptive design Xue, Dezhen; Balachandran, Prasanna V.; Hogden, John Nature Communications, Vol. 7, Issue 1 https://doi.org/10.1038/ncomms11241	journal	April 2016
Learning from data to design functional materials without inversion symmetry Balachandran, Prasanna V.; Young, Joshua; Lookman, Turab Nature Communications, Vol. 8, Issue 1 https://doi.org/10.1038/ncomms14282	journal	February 2017
Using Machine Learning To Identify Factors That Govern Amorphization of Irradiated Pyrochlores Pilania, Ghanshyam; Whittle, Karl R.; Jiang, Chao Chemistry of Materials, Vol. 29, Issue 6 https://doi.org/10.1021/acs.chemmater.6b04666	journal	November 2016
Insightful classification of crystal structures using deep learning Ziletti, Angelo; Kumar, Devinder; Scheffler, Matthias Nature Communications, Vol. 9, Issue 1 https://doi.org/10.1038/s41467-018-05169-6	journal	July 2018
Machine learning for heterogeneous catalyst design and discovery Goldsmith, Bryan R.; Esterhuizen, Jacques; Liu, Jin-Xun AIChE Journal, Vol. 64, Issue 7 https://doi.org/10.1002/aic.16198	journal	May 2018
Materials informatics Ramakrishna, Seeram; Zhang, Tong-Yi; Lu, Wen-Cong Journal of Intelligent Manufacturing, Vol. 30, Issue 6 https://doi.org/10.1007/s10845-018-1392-0	journal	January 2018
Physics-informed machine learning for inorganic scintillator discovery Pilania, G.; McClellan, K. J.; Stanek, C. R. The Journal of Chemical Physics, Vol. 148, Issue 24 https://doi.org/10.1063/1.5025819	journal	June 2018
Accelerating materials property predictions using machine learning Pilania, Ghanshyam; Wang, Chenchen; Jiang, Xun Scientific Reports, Vol. 3, Issue 1 https://doi.org/10.1038/srep02810	journal	September 2013
Machine Learning Force Fields: Construction, Validation, and Outlook Botu, V.; Batra, R.; Chapman, J. The Journal of Physical Chemistry C, Vol. 121, Issue 1 https://doi.org/10.1021/acs.jpcc.6b10908	journal	December 2016
A universal strategy for the creation of machine learning-based atomistic force fields Huan, Tran Doan; Batra, Rohit; Chapman, James npj Computational Materials, Vol. 3, Issue 1 https://doi.org/10.1038/s41524-017-0042-y	journal	September 2017
From Organized High-Throughput Data to Phenomenological Theory using Machine Learning: The Example of Dielectric Breakdown Kim, Chiho; Pilania, Ghanshyam; Ramprasad, Ramamurthy Chemistry of Materials, Vol. 28, Issue 5 https://doi.org/10.1021/acs.chemmater.5b04109	journal	February 2016
Solving the electronic structure problem with machine learning Chandrasekaran, Anand; Kamal, Deepak; Batra, Rohit npj Computational Materials, Vol. 5, Issue 1 https://doi.org/10.1038/s41524-019-0162-7	journal	February 2019
Polymer Genome: A Data-Powered Polymer Informatics Platform for Property Predictions Kim, Chiho; Chandrasekaran, Anand; Huan, Tran Doan The Journal of Physical Chemistry C, Vol. 122, Issue 31 https://doi.org/10.1021/acs.jpcc.8b02913	journal	July 2018
Impact of dataset uncertainties on machine learning model predictions: the example of polymer glass transition temperatures Jha, Anurag; Chandrasekaran, Anand; Kim, Chiho Modelling and Simulation in Materials Science and Engineering, Vol. 27, Issue 2 https://doi.org/10.1088/1361-651X/aaf8ca	journal	January 2019
Scoping the polymer genome: A roadmap for rational polymer dielectrics design and beyond Mannodi-Kanakkithodi, Arun; Chandrasekaran, Anand; Kim, Chiho Materials Today, Vol. 21, Issue 7 https://doi.org/10.1016/j.mattod.2017.11.021	journal	September 2018
Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach Ramakrishnan, Raghunathan; Dral, Pavlo O.; Rupp, Matthias Journal of Chemical Theory and Computation, Vol. 11, Issue 5 https://doi.org/10.1021/acs.jctc.5b00099	journal	April 2015
Prediction model of band gap for inorganic compounds by combination of density functional theory calculations and machine learning techniques Lee, Joohwi; Seko, Atsuto; Shitara, Kazuki Physical Review B, Vol. 93, Issue 11 https://doi.org/10.1103/PhysRevB.93.115104	journal	March 2016
A strategy to apply machine learning to small datasets in materials science Zhang, Ying; Ling, Chen npj Computational Materials, Vol. 4, Issue 1 https://doi.org/10.1038/s41524-018-0081-z	journal	May 2018
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
Predicting the output from a complex computer code when fast approximations are available Kennedy, M. Biometrika, Vol. 87, Issue 1 https://doi.org/10.1093/biomet/87.1.1	journal	March 2000
Multi-fidelity machine learning models for accurate bandgap predictions of solids Pilania, G.; Gubernatis, J. E.; Lookman, T. Computational Materials Science, Vol. 129 https://doi.org/10.1016/j.commatsci.2016.12.004	journal	March 2017
Boosting Quantum Machine Learning Models with a Multilevel Combination Technique: Pople Diagrams Revisited Zaspel, Peter; Huang, Bing; Harbrecht, Helmut Journal of Chemical Theory and Computation, Vol. 15, Issue 3 https://doi.org/10.1021/acs.jctc.8b00832	journal	December 2018
High dielectric constant gate oxides for metal oxide Si transistors Robertson, John Reports on Progress in Physics, Vol. 69, Issue 2 https://doi.org/10.1088/0034-4885/69/2/R02	journal	December 2005
High-κ gate dielectrics: Current status and materials properties considerations Wilk, G. D.; Wallace, R. M.; Anthony, J. M. Journal of Applied Physics, Vol. 89, Issue 10 https://doi.org/10.1063/1.1361065	journal	May 2001
Recent progress in ab initio simulations of hafnia-based gate stacks Zhu, H.; Tang, C.; Fonseca, L. R. C. Journal of Materials Science, Vol. 47, Issue 21 https://doi.org/10.1007/s10853-012-6568-y	journal	May 2012
Interface engineering through atomic dopants in HfO2-based gate stacks Zhu, H.; Ramanath, Ganpati; Ramprasad, R. Journal of Applied Physics, Vol. 114, Issue 11 https://doi.org/10.1063/1.4821797	journal	September 2013
Local dielectric permittivity of HfO2 based slabs and stacks: A first principles study Shi, N.; Ramprasad, R. Applied Physics Letters, Vol. 91, Issue 24 https://doi.org/10.1063/1.2822834	journal	December 2007
Oxygen defect accumulation at Si:HfO2 interfaces Tang, C.; Ramprasad, R. Applied Physics Letters, Vol. 92, Issue 18 https://doi.org/10.1063/1.2917576	journal	May 2008
Dielectric properties of nanoscaleHfO2slabs Ramprasad, R.; Shi, N. Physical Review B, Vol. 72, Issue 5 https://doi.org/10.1103/PhysRevB.72.052107	journal	August 2005
Ferroelectricity in hafnium oxide thin films Böscke, T. S.; Müller, J.; Bräuhaus, D. Applied Physics Letters, Vol. 99, Issue 10 https://doi.org/10.1063/1.3634052	journal	September 2011
Impact of different dopants on the switching properties of ferroelectric hafniumoxide Schroeder, Uwe; Yurchuk, Ekaterina; Müller, Johannes Japanese Journal of Applied Physics, Vol. 53, Issue 8S1 https://doi.org/10.7567/JJAP.53.08LE02	journal	July 2014
Ferroelectricity and Antiferroelectricity of Doped Thin HfO ₂ -Based Films Park, Min Hyuk; Lee, Young Hwan; Kim, Han Joon Advanced Materials, Vol. 27, Issue 11 https://doi.org/10.1002/adma.201404531	journal	February 2015
Toward a multifunctional monolithic device based on pyroelectricity and the electrocaloric effect of thin antiferroelectric Hf x Zr 1−x O 2 films Park, Min Hyuk; Kim, Han Joon; Kim, Yu Jin Nano Energy, Vol. 12 https://doi.org/10.1016/j.nanoen.2014.09.025	journal	March 2015
Phase Relations and Volume Changes of Hafnia under High Pressure and High Temperature Ohtaka, Osamu; Fukui, Hiroshi; Kunisada, Taichi Journal of the American Ceramic Society, Vol. 84, Issue 6 https://doi.org/10.1111/j.1151-2916.2001.tb00843.x	journal	June 2001
On the structural origins of ferroelectricity in HfO ₂ thin films Sang, Xiahan; Grimley, Everett D.; Schenk, Tony Applied Physics Letters, Vol. 106, Issue 16 https://doi.org/10.1063/1.4919135	journal	April 2015
Pathways towards ferroelectricity in hafnia Huan, Tran Doan; Sharma, Vinit; Rossetti, George A. Physical Review B, Vol. 90, Issue 6 https://doi.org/10.1103/PhysRevB.90.064111	journal	August 2014
Factors Favoring Ferroelectricity in Hafnia: A First-Principles Computational Study Batra, Rohit; Huan, Tran Doan; Jones, Jacob L. The Journal of Physical Chemistry C, Vol. 121, Issue 8 https://doi.org/10.1021/acs.jpcc.6b11972	journal	February 2017
Prediction of new metastable $$\hbox {HfO}_{2}$$ HfO 2 phases: toward understanding ferro- and antiferroelectric films Barabash, S. V. Journal of Computational Electronics, Vol. 16, Issue 4 https://doi.org/10.1007/s10825-017-1077-5	journal	November 2017
An extensive study of the influence of dopants on the ferroelectric properties of HfO ₂ Starschich, S.; Boettger, U. Journal of Materials Chemistry C, Vol. 5, Issue 2 https://doi.org/10.1039/C6TC04807B	journal	January 2017
Dopants Promoting Ferroelectricity in Hafnia: Insights from a comprehensive Chemical Space Exploration Batra, Rohit; Huan, Tran Doan; Rossetti, George A. Chemistry of Materials, Vol. 29, Issue 21 https://doi.org/10.1021/acs.chemmater.7b02835	journal	October 2017
Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set Kresse, G.; Furthmüller, J. Physical Review B, Vol. 54, Issue 16, p. 11169-11186 https://doi.org/10.1103/PhysRevB.54.11169	journal	October 1996
Generalized Gradient Approximation Made Simple Perdew, John P.; Burke, Kieron; Ernzerhof, Matthias Physical Review Letters, Vol. 77, Issue 18, p. 3865-3868 https://doi.org/10.1103/PhysRevLett.77.3865	journal	October 1996
Projector augmented-wave method Blöchl, P. E. Physical Review B, Vol. 50, Issue 24, p. 17953-17979 https://doi.org/10.1103/PhysRevB.50.17953	journal	December 1994
Special points for Brillouin-zone integrations Monkhorst, Hendrik J.; Pack, James D. Physical Review B, Vol. 13, Issue 12, p. 5188-5192 https://doi.org/10.1103/PhysRevB.13.5188	journal	June 1976
Unexpectedly large energy variations from dopant interactions in ferroelectric HfO2 from high-throughput ab initio calculations Falkowski, Max; Künneth, Christopher; Materlik, Robin npj Computational Materials, Vol. 4, Issue 1 https://doi.org/10.1038/s41524-018-0133-4	journal	December 2018
First-principles study on doping and phase stability of ${HfO}_{2}$ Lee, Choong-Ki; Cho, Eunae; Lee, Hyo-Sug Physical Review B, Vol. 78, Issue 1 https://doi.org/10.1103/PhysRevB.78.012102	journal	July 2008
Revised effective ionic radii and systematic studies of interatomic distances in halides and chalcogenides Shannon, R. D. Acta Crystallographica Section A, Vol. 32, Issue 5, p. 751-767 https://doi.org/10.1107/S0567739476001551	journal	September 1976
The Nature of the Chemical Bond. iv. the Energy of Single Bonds and the Relative Electronegativity of Atoms Pauling, Linus Journal of the American Chemical Society, Vol. 54, Issue 9 https://doi.org/10.1021/ja01348a011	journal	September 1932
CRC Handbook of Chemistry and Physics Haynes, William M. CRC Press https://doi.org/10.1201/b17118	book	June 2014
Critical assessment of regression-based machine learning methods for polymer dielectrics Mannodi-Kanakkithodi, Arun; Pilania, Ghanshyam; Ramprasad, Rampi Computational Materials Science, Vol. 125 https://doi.org/10.1016/j.commatsci.2016.08.039	journal	December 2016
Bayesian Analysis of Hierarchical Multifidelity Codes Le Gratiet, Loic SIAM/ASA Journal on Uncertainty Quantification, Vol. 1, Issue 1 https://doi.org/10.1137/120884122	journal	January 2013
Recursive Co-Kriging Model for Design of Computer Experiments with Multiple Levels of Fidelity Le Gratiet, Loic; Garnier, Josselin International Journal for Uncertainty Quantification, Vol. 4, Issue 5 https://doi.org/10.1615/Int.J.UncertaintyQuantification.2014006914	journal	January 2014

Cited By (3)

Ab initio machine learning in chemical compound space Bing, Huang,; Anatole, von Lilienfeld, O. American Chemical Society https://doi.org/10.5451/unibas-ep87406	text	January 2021
Polymer informatics: Current status and critical next steps Chen, Lihua; Pilania, Ghanshyam; Batra, Rohit Materials Science and Engineering: R: Reports, Vol. 144 https://doi.org/10.1016/j.mser.2020.100595	journal	April 2021
Understanding Magnetic Properties of Actinide-Based Compounds from Machine Learning Ghosh, Ayana; Ronning, Filip; Nakhmanson, Serge arXiv https://doi.org/10.48550/arxiv.1907.10587	text	January 2019

Similar Records

Multi-fidelity classification using Gaussian processes: Accelerating the prediction of large-scale computational models

Journal Article · Fri Aug 30 00:00:00 EDT 2019 · Computer Methods in Applied Mechanics and Engineering · OSTI ID:1511635

Costabal, Francisco Sahli; Perdikaris, Paris; Kuhl, Ellen; +1 more

On-line Generation and Error Handling for Surrogate Models within Multifidelity Uncertainty Quantification

Technical Report · Sun Sep 01 00:00:00 EDT 2019 · OSTI ID:1511635

Blonigan, Patrick Joseph; Geraci, Gianluca; Rizzi, Francesco; +2 more

Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization

Journal Article · Wed Aug 08 00:00:00 EDT 2018 · SIAM Review · OSTI ID:1511635

Peherstorfer, Benjamin; Willcox, Karen; Gunzburger, Max

Related Subjects

36 MATERIALS SCIENCE
density functional theory
dopant formation energy
hafnia
machine learning
materials informatics
multifidelity learning

Title: Multifidelity Information Fusion with Machine Learning: A Case Study of Dopant Formation Energies in Hafnia

Citation Formats

References (65)

Cited By (3)

Similar Records

Related Subjects