Learning from Imperfections: Predicting Structure and Thermodynamics from Atomic Imaging of Fluctuations

Vlcek, Lukas; Ziatdinov, Maxim; Maksov, Artem; Tselev, Alexander; Baddorf, Arthur P.; Kalinin, Sergei V.; Vasudevan, Rama K.

doi:10.1021/acsnano.8b07980

Title: Learning from Imperfections: Predicting Structure and Thermodynamics from Atomic Imaging of Fluctuations

Journal Article · 04 December 2018 · ACS Nano

DOI: https://doi.org/10.1021/acsnano.8b07980 · OSTI ID:1505330

^[1];

^[2];

^[1];

^[3];

^[2];

^[2]

Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Univ. of Tennessee, Knoxville, TN (United States)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
CICECO - Aveiro Institute of Materials, Aveiro (Portugal)

In materials characterization, traditionally a single experimental sample is used to derive information about a single point in the composition space, while the imperfections, impurities, and stochastic details of material structure are deemed irrelevant or complicating factors in the analysis. Here we demonstrate that atomic-scale studies of a single nominal composition can provide information about microstructures and thermodynamic response over a finite area of chemical space. Using the principles of statistical inference, we develop a framework for incorporating structural fluctuations into statistical mechanical models and use it to solve the inverse problem of deriving effective interatomic interactions responsible for elemental segregation in a La_5/8Ca_3/8MnO₃ thin film. Here, the results are further analyzed by a variational autoencoder to detect anomalous behavior in the composition phase diagram. This study provides a framework for creating generative models from a combination of multiple experimental data and provides direct insight into the driving forces for cation segregation in manganites.

View Accepted Manuscript (DOE)

Cite

Export

Save

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1505330

Journal Information:: ACS Nano, Journal Name: ACS Nano Journal Issue: 1 Vol. 13; ISSN 1936-0851

Publisher:: American Chemical Society (ACS)Copyright Statement

Country of Publication:: United States

Language:: English

References (62)

Bayesian Optimization for Materials Design Frazier, Peter I.; Wang, Jialei Information Science for Materials Discovery and Design https://doi.org/10.1007/978-3-319-23871-5_3	book	December 2015
Development of lanthanum strontium manganite perovskite cathode materials of solid oxide fuel cells: a review Jiang, San Ping Journal of Materials Science, Vol. 43, Issue 21 https://doi.org/10.1007/s10853-008-2966-6	journal	November 2008
Informatics Infrastructure for the Materials Genome Initiative Dima, Alden; Bhaskarla, Sunil; Becker, Chandler JOM, Vol. 68, Issue 8 https://doi.org/10.1007/s11837-016-2000-4	journal	July 2016
A computational high-throughput search for new ternary superalloys Nyshadham, Chandramouli; Oses, Corey; Hansen, Jacob E. Acta Materialia, Vol. 122 https://doi.org/10.1016/j.actamat.2016.09.017	journal	January 2017
Application of spherical indentation and the materials knowledge system framework to establishing microstructure-yield strength linkages from carbon steel scoops excised from high-temperature exposed components Iskakov, Almambet; Yabansu, Yuksel C.; Rajagopalan, Sudhir Acta Materialia, Vol. 144 https://doi.org/10.1016/j.actamat.2017.11.024	journal	February 2018
A high-throughput infrastructure for density functional theory calculations Jain, Anubhav; Hautier, Geoffroy; Moore, Charles J. Computational Materials Science, Vol. 50, Issue 8 https://doi.org/10.1016/j.commatsci.2011.02.023	journal	June 2011
AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations Curtarolo, Stefano; Setyawan, Wahyu; Wang, Shidong Computational Materials Science, Vol. 58 https://doi.org/10.1016/j.commatsci.2012.02.002	journal	June 2012
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
In situ study of activation and de-activation of LSM fuel cell cathodes – Electrochemistry and surface analysis of thin-film electrodes Huber, Anne-Katrin; Falk, Mareike; Rohnke, Marcus Journal of Catalysis, Vol. 294 https://doi.org/10.1016/j.jcat.2012.07.010	journal	October 2012
Sr Segregation in Perovskite Oxides: Why It Happens and How It Exists Koo, Bonjae; Kim, Kyeounghak; Kim, Jun Kyu Joule, Vol. 2, Issue 8 https://doi.org/10.1016/j.joule.2018.07.016	journal	August 2018
Effects of annealing on structure and composition of LSMO thin films Xie, Haipeng; Huang, Han; Cao, Ningtong Physica B: Condensed Matter, Vol. 477 https://doi.org/10.1016/j.physb.2015.07.032	journal	November 2015
Fast oxygen diffusion in A-site ordered perovskites Taskin, A. A.; Lavrov, A. N.; Ando, Yoichi Progress in Solid State Chemistry, Vol. 35, Issue 2-4 https://doi.org/10.1016/j.progsolidstchem.2007.01.014	journal	January 2007
Growth Mode Transition in Complex Oxide Heteroepitaxy: Atomically Resolved Studies Tselev, Alexander; Vasudevan, Rama K.; Gianfrancesco, Anthony G. Crystal Growth & Design, Vol. 16, Issue 5 https://doi.org/10.1021/acs.cgd.5b01826	journal	April 2016
Consistent Integration of Experimental and Ab Initio Data into Effective Physical Models Vlcek, Lukas; Vasudevan, Rama K.; Jesse, Stephen Journal of Chemical Theory and Computation, Vol. 13, Issue 11 https://doi.org/10.1021/acs.jctc.7b00114	journal	October 2017
Surface Control of Epitaxial Manganite Films via Oxygen Pressure Tselev, Alexander; Vasudevan, Rama K.; Gianfrancesco, Anthony G. ACS Nano, Vol. 9, Issue 4 https://doi.org/10.1021/acsnano.5b00743	journal	February 2015
Knowledge Extraction from Atomically Resolved Images Vlcek, Lukas; Maksov, Artem; Pan, Minghu ACS Nano, Vol. 11, Issue 10 https://doi.org/10.1021/acsnano.7b05036	journal	October 2017
Deep Learning of Atomically Resolved Scanning Transmission Electron Microscopy Images: Chemical Identification and Tracking Local Transformations Ziatdinov, Maxim; Dyck, Ondrej; Maksov, Artem ACS Nano, Vol. 11, Issue 12 https://doi.org/10.1021/acsnano.7b07504	journal	October 2016
Rapid Identification of Areas of Interest in Thin Film Materials Libraries by Combining Electrical, Optical, X-ray Diffraction, and Mechanical High-Throughput Measurements: A Case Study for the System Ni–Al Thienhaus, S.; Naujoks, D.; Pfetzing-Micklich, J. ACS Combinatorial Science, Vol. 16, Issue 12 https://doi.org/10.1021/co5000757	journal	November 2014
Cation Size Mismatch and Charge Interactions Drive Dopant Segregation at the Surfaces of Manganite Perovskites Lee, Wonyoung; Han, Jeong Woo; Chen, Yan Journal of the American Chemical Society, Vol. 135, Issue 21 https://doi.org/10.1021/ja3125349	journal	May 2013
Atomic-scale images of charge ordering in a mixed-valence manganite Renner, Ch.; Aeppli, G.; Kim, B. -G. Nature, Vol. 416, Issue 6880 https://doi.org/10.1038/416518a	journal	April 2002
Verification of the Crooks fluctuation theorem and recovery of RNA folding free energies Collin, D.; Ritort, F.; Jarzynski, C. Nature, Vol. 437, Issue 7056 https://doi.org/10.1038/nature04061	journal	September 2005
Chemically induced Jahn–Teller ordering on manganite surfaces Gai, Zheng; Lin, Wenzhi; Burton, J. D. Nature Communications, Vol. 5, Issue 1 https://doi.org/10.1038/ncomms5528	journal	July 2014
The high-throughput highway to computational materials design Curtarolo, Stefano; Hart, Gus L. W.; Nardelli, Marco Buongiorno Nature Materials, Vol. 12, Issue 3 https://doi.org/10.1038/nmat3568	journal	February 2013
Machine learning phases of matter Carrasquilla, Juan; Melko, Roger G. Nature Physics, Vol. 13, Issue 5 https://doi.org/10.1038/nphys4035	journal	February 2017
New tool in the box Zdeborová, Lenka Nature Physics, Vol. 13, Issue 5 https://doi.org/10.1038/nphys4053	journal	February 2017
Equilibrium free energies from non-equilibrium trajectories with relaxation fluctuation spectroscopy Ross, David; Strychalski, Elizabeth A.; Jarzynski, Christopher Nature Physics, Vol. 14, Issue 8 https://doi.org/10.1038/s41567-018-0153-5	journal	May 2018
A polymer dataset for accelerated property prediction and design Huan, Tran Doan; Mannodi-Kanakkithodi, Arun; Kim, Chiho Scientific Data, Vol. 3, Issue 1 https://doi.org/10.1038/sdata.2016.12	journal	March 2016
On-the-fly machine-learning for high-throughput experiments: search for rare-earth-free permanent magnets Kusne, Aaron Gilad; Gao, Tieren; Mehta, Apurva Scientific Reports, Vol. 4, Issue 1 https://doi.org/10.1038/srep06367	journal	September 2014
Statistically optimal analysis of samples from multiple equilibrium states Shirts, Michael R.; Chodera, John D. The Journal of Chemical Physics, Vol. 129, Issue 12 https://doi.org/10.1063/1.2978177	journal	September 2008
Cationic and charge segregation in La2/3Ca1/3MnO3 thin films grown on (001) and (110) SrTiO3 Estradé, S.; Arbiol, J.; Peiró, F. Applied Physics Letters, Vol. 93, Issue 11 https://doi.org/10.1063/1.2981574	journal	September 2008
Phase development and electrical property analysis of pulsed laser deposited Pb(Mg1/3Nb2/3)O3–PbTiO3 (70/30) epitaxial thin films Maria, J. -P.; Hackenberger, W.; Trolier-McKinstry, S. Journal of Applied Physics, Vol. 84, Issue 9 https://doi.org/10.1063/1.368809	journal	November 1998
Rigorous force field optimization principles based on statistical distance minimization Vlcek, Lukas; Chialvo, Ariel A. The Journal of Chemical Physics, Vol. 143, Issue 14 https://doi.org/10.1063/1.4932360	journal	October 2015
Perspective: Codesign for materials science: An optimal learning approach Lookman, Turab; Alexander, Francis J.; Bishop, Alan R. APL Materials, Vol. 4, Issue 5 https://doi.org/10.1063/1.4944627	journal	April 2016
Perspective: Composition–structure–property mapping in high-throughput experiments: Turning data into knowledge Hattrick-Simpers, Jason R.; Gregoire, John M.; Kusne, A. Gilad APL Materials, Vol. 4, Issue 5 https://doi.org/10.1063/1.4950995	journal	May 2016
Fulfilling the promise of the materials genome initiative with high-throughput experimental methodologies Green, M. L.; Choi, C. L.; Hattrick-Simpers, J. R. Applied Physics Reviews, Vol. 4, Issue 1 https://doi.org/10.1063/1.4977487	journal	March 2017
Relaxor ferroelectrics Cross, L. Eric Ferroelectrics, Vol. 76, Issue 1 https://doi.org/10.1080/00150198708016945	journal	December 1987
Excess properties of liquid mixtures from computer simulation: a coupling-parameter approach to the determination of their dependence on molecular asymmetry Chialvo, Ariel A. Molecular Physics, Vol. 73, Issue 1 https://doi.org/10.1080/00268979100101111	journal	May 1991
CMR manganites: physics, thin films and devices Haghiri-Gosnet, A-M; Renard, J-P Journal of Physics D: Applied Physics, Vol. 36, Issue 8 https://doi.org/10.1088/0022-3727/36/8/201	journal	April 2003
Machine learning and data science in soft materials engineering Ferguson, Andrew L. Journal of Physics: Condensed Matter, Vol. 30, Issue 4 https://doi.org/10.1088/1361-648X/aa98bd	journal	December 2017
Surfaces of the perovskite manganites La 1 − x Ca x MnO 3 Choi, Jaewu; Zhang, Jiandi; Liou, S. -H. Physical Review B, Vol. 59, Issue 20 https://doi.org/10.1103/PhysRevB.59.13453	journal	May 1999
Very large magnetoresistance and coherent switching in half-metallic manganite tunnel junctions Jo, Moon-Ho; Mathur, N. D.; Todd, N. K. Physical Review B, Vol. 61, Issue 22 https://doi.org/10.1103/PhysRevB.61.R14905	journal	June 2000
Short-range order in two-dimensional binary alloys Ottaviano, L.; Ressel, B.; Di Teodoro, C. Physical Review B, Vol. 67, Issue 4 https://doi.org/10.1103/PhysRevB.67.045401	journal	January 2003
XPS investigation of Mn valence in lanthanum manganite thin films under variation of oxygen content Beyreuther, Elke; Grafström, Stefan; Eng, Lukas M. Physical Review B, Vol. 73, Issue 15 https://doi.org/10.1103/PhysRevB.73.155425	journal	April 2006
How to represent crystal structures for machine learning: Towards fast prediction of electronic properties Schütt, K. T.; Glawe, H.; Brockherde, F. Physical Review B, Vol. 89, Issue 20 https://doi.org/10.1103/PhysRevB.89.205118	journal	May 2014
Learning phase transitions from dynamics van Nieuwenburg, Evert; Bairey, Eyal; Refael, Gil Physical Review B, Vol. 98, Issue 6 https://doi.org/10.1103/PhysRevB.98.060301	journal	August 2018
Statistical distance and Hilbert space Wootters, W. K. Physical Review D, Vol. 23, Issue 2 https://doi.org/10.1103/PhysRevD.23.357	journal	January 1981
Discovering phases, phase transitions, and crossovers through unsupervised machine learning: A critical examination Hu, Wenjian; Singh, Rajiv R. P.; Scalettar, Richard T. Physical Review E, Vol. 95, Issue 6 https://doi.org/10.1103/PhysRevE.95.062122	journal	June 2017
Unsupervised learning of phase transitions: From principal component analysis to variational autoencoders Wetzel, Sebastian J. Physical Review E, Vol. 96, Issue 2 https://doi.org/10.1103/PhysRevE.96.022140	journal	August 2017
Tunable Metallicity of the La 5 / 8 Ca 3 / 8 MnO 3 ( 001 ) Surface by an Oxygen Overlayer Fuchigami, K.; Gai, Z.; Ward, T. Z. Physical Review Letters, Vol. 102, Issue 6 https://doi.org/10.1103/PhysRevLett.102.066104	journal	February 2009
Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning Rupp, Matthias; Tkatchenko, Alexandre; Müller, Klaus-Robert Physical Review Letters, Vol. 108, Issue 5 https://doi.org/10.1103/PhysRevLett.108.058301	journal	January 2012
Finding Density Functionals with Machine Learning Snyder, John C.; Rupp, Matthias; Hansen, Katja Physical Review Letters, Vol. 108, Issue 25 https://doi.org/10.1103/PhysRevLett.108.253002	journal	June 2012
Statistical distance and the geometry of quantum states Braunstein, Samuel L.; Caves, Carlton M. Physical Review Letters, Vol. 72, Issue 22 https://doi.org/10.1103/PhysRevLett.72.3439	journal	May 1994
Metastable Filamentary Vortex Flow in Thin Film Superconductors Grønbech-Jensen, Niels; Bishop, A. R.; Domínguez, Daniel Physical Review Letters, Vol. 76, Issue 16 https://doi.org/10.1103/PhysRevLett.76.2985	journal	April 1996
Random Potential Effect near the Bicritical Region in Perovskite Manganites as Revealed by Comparison with the Ordered Perovskite Analogs Akahoshi, D.; Uchida, M.; Tomioka, Y. Physical Review Letters, Vol. 90, Issue 17 https://doi.org/10.1103/PhysRevLett.90.177203	journal	May 2003
A -Site Ordering versus Electronic Inhomogeneity in Colossally Magnetoresistive Manganite Films Moshnyaga, V.; Sudheendra, L.; Lebedev, O. I. Physical Review Letters, Vol. 97, Issue 10 https://doi.org/10.1103/PhysRevLett.97.107205	journal	September 2006
Experimental Free Energy Surface Reconstruction from Single-Molecule Force Spectroscopy using Jarzynski’s Equality Harris, Nolan C.; Song, Yang; Kiang, Ching-Hwa Physical Review Letters, Vol. 99, Issue 6 https://doi.org/10.1103/PhysRevLett.99.068101	journal	August 2007
Surface reconstructions and modified surface states in L a 1 − x C a x Mn O 3 Vasudevan, Rama K.; Dixit, Hemant; Tselev, Alexander Physical Review Materials, Vol. 2, Issue 10 https://doi.org/10.1103/PhysRevMaterials.2.104418	journal	October 2018
The physics of manganites: Structure and transport Salamon, Myron B.; Jaime, Marcelo Reviews of Modern Physics, Vol. 73, Issue 3 https://doi.org/10.1103/RevModPhys.73.583	journal	August 2001
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
Evaluating bag-of-visual-words representations in scene classification Yang, Jun; Jiang, Yu-Gang; Hauptmann, Alexander G. Proceedings of the international workshop on Workshop on multimedia information retrieval - MIR '07 https://doi.org/10.1145/1290082.1290111	conference	January 2007
Feature extraction via similarity search: application to atom finding and denoising in electron and scanning probe microscopy imaging Somnath, Suhas; Smith, Christopher R.; Kalinin, Sergei V. Advanced Structural and Chemical Imaging, Vol. 4, Issue 1 https://doi.org/10.1186/s40679-018-0052-y	journal	March 2018
Phase diagram studies in the quasi binary systems LaMnO ₃ –SrMnO ₃ and LaMnO ₃ –CaMnO ₃ Majewski, Peter; Epple, Lars; Rozumek, Michael Journal of Materials Research, Vol. 15, Issue 5 https://doi.org/10.1557/JMR.2000.0164	journal	May 2000

Cited By (1)

Materials science in the artificial intelligence age: high-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics Vasudevan, Rama K.; Choudhary, Kamal; Mehta, Apurva MRS Communications, Vol. 9, Issue 3 https://doi.org/10.1557/mrc.2019.95	journal	July 2019

Similar Records

Exotic Long-Range Surface Reconstruction on La 0.7 Sr 0.3 MnO 3 Thin Films

Journal Article · 2021 · ACS Applied Materials and Interfaces · OSTI ID:1783032

Kelley, Kyle; Sharma, Vinit K.; Zhang, Wenrui; +3 more

Role of structure imperfection in the formation of the magnetotransport properties of rare-earth manganites with a perovskite structure

Journal Article · 2017 · Journal of Experimental and Theoretical Physics · OSTI ID:22617090

Pashchenko, V. P.; Prokopenko, V. K.; Turchenko, V. A.; +6 more

Intrinsic and extrinsic magnetic properties of the naturally layered manganites

Conference · 1999 · OSTI ID:750606

Berger, A; Mitchell, J F; Miller, D J; +2 more

Related Subjects

36 MATERIALS SCIENCE
generative model
manganite
scanning tunneling microscopy
segregation
statistical inference
thin film

Title: Learning from Imperfections: Predicting Structure and Thermodynamics from Atomic Imaging of Fluctuations

Citation Formats

References (62)

Cited By (1)

Similar Records

Related Subjects