Hostname: page-component-6587cd75c8-gglxz Total loading time: 0.001 Render date: 2025-04-23T19:56:04.348Z Has data issue: false hasContentIssue false
  • English
  • Français

Materials science in the artificial intelligence age: high-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics

Published online by Cambridge University Press:  22 July 2019

Rama K. Vasudevan Open the ORCID record for Rama K. Vasudevan [Opens in a new window] ,
Kamal Choudhary ,
Apurva Mehta ,
Ryan Smith ,
Gilad Kusne ,
Francesca Tavazza ,
Lukas Vlcek ,
Maxim Ziatdinov ,
Sergei V. Kalinin  and
Jason Hattrick-Simpers
Rama K. Vasudevan*
Affiliation:
Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
Kamal Choudhary
Affiliation:
Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA
Apurva Mehta
Affiliation:
Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
Ryan Smith
Affiliation:
Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA
Gilad Kusne
Affiliation:
Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA
Francesca Tavazza
Affiliation:
Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA
Lukas Vlcek
Affiliation:
Materials Science and Technology Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
Maxim Ziatdinov
Affiliation:
Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
Sergei V. Kalinin
Affiliation:
Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
Jason Hattrick-Simpers
Affiliation:
Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA
*
Address all correspondence to Rama K. Vasudevan at [email protected]
Get access

Abstract

The use of statistical/machine learning (ML) approaches to materials science is experiencing explosive growth. Here, we review recent work focusing on the generation and application of libraries from both experiment and theoretical tools. The library data enables classical correlative ML and also opens the pathway for exploration of underlying causative physical behaviors. We highlight key advances facilitated by this approach and illustrate how modeling, macroscopic experiments, and imaging can be combined to accelerate the understanding and development of new materials systems. These developments point toward a data-driven future wherein knowledge can be aggregated and synthesized, accelerating the advancement of materials science.

Type
Artificial Intelligence Prospectives
Information
MRS Communications , Volume 9 , Issue 3 , September 2019 , pp. 821 - 838
Copyright
Copyright © The Author(s) 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

1.Agrawal, A. and Choudhary, A.: Perspective: Materials informatics and big data: realization of the ‘fourth paradigm’ of science in materials science. APL Mater. 4, 053208 (2016).Google Scholar
2.Gakh, A.A., Gakh, E.G., Sumpter, B.G., and Noid, D.W.: Neural network-graph theory approach to the prediction of the physical properties of organic compounds. J. Chem. Inf. Comput. Sci. 34, 832 (1994).Google Scholar
3.Sumpter, B.G., Getino, C., and Noid, D.W.: Neural network predictions of energy transfer in macromolecules. J. Phys. Chem. 96, 2761 (1992).Google Scholar
4.Nikiforov, M., Reukov, V., Thompson, G., Vertegel, A., Guo, S., Kalinin, S., and Jesse, S.: Functional recognition imaging using artificial neural networks: applications to rapid cellular identification via broadband electromechanical response. Nanotechnology 20, 405708 (2009).Google Scholar
5.Currie, K.R. and LeClair, S.R.: Self-improving process control for molecular beam epitaxy. Int. J. Adv. Manuf. Technol. 8, 244 (1993).Google Scholar
6.Bensaoula, A., Malki, H.A., and Kwari, A.M.: The use of multilayer neural networks in material synthesis. IEEE Trans. Semiconduct. Manuf. 11, 421 (1998).Google Scholar
7.Lee, K.K., Brown, T., Dagnall, G., Bicknell-Tassius, R., Brown, A., and May, G.S.: Using neural networks to construct models of the molecular beam epitaxy process. IEEE Trans. Semiconduct. Manuf. 13, 34 (2000).Google Scholar
8.Takeuchi, I., Koinuma, H., Amis, E.J., Newsam, J.M., Wille, L.T., and Buelens, C.: SYMPOSIUM S: Combinatorial and artificial intelligence methods in materials science. Mater. Res. Soc. Symp. Proc 700, 358–371 (2002).Google Scholar
9.Bohannon, J.: Fears of an AI pioneer. Science 349, 252 (2015).Google Scholar
10.Sejnowski, T.J.: The Deep Learning Revolution (MIT Press, Cambridge, MA, 2018).Google Scholar
11.McCarthy, J., Minsky, M.L., Rochester, N., and Shannon, C.E.: A proposal for the Dartmouth summer research project on artificial intelligence, August 31, 1955. AI Magazine 27, 12 (2006).Google Scholar
12.LeCun, Y.: A theoretical framework for back-propagation. In Proceedings of the 1988 Connectionist Models Summer School, edited by Touresky, D., Hinton, G., and Sejnowski, T. (Morgan Kaufmann, CMU, Pittsburgh, PA, 1988) p. 21.Google Scholar
13.Boser, B.E., Guyon, I.M., and Vapnik, V.N.: A training algorithm for optimal margin classifiers. In Proceedings of the Fifth Annual Workshop on Computational Learning Theory; ACM, Pittsburgh, PA, USA, 1992; p. 144.Google Scholar
14.LeCun, Y., Bengio, Y., and Hinton, G.: Deep learning. Nature 521, 436 (2015).Google Scholar
15.Brodtkorb, A.R., Hagen, T.R., and Sætra, M.L.: Graphics processing unit (GPU) programming strategies and trends in GPU computing. J. Parallel Distrib. Comput. 73, 4 (2013).Google Scholar
16.Rupp, K.: 42 Years of Microprocessor Trend Data, 2018. https://www.karlrupp.net/2018/02/42-years-of-microprocessor-trend-data/ (accessed July 17, 2019).Google Scholar
17.de Pablo, J.J., Jones, B., Kovacs, C.L., Ozolins, V., and Ramirez, A.P.: The materials genome initiative, the interplay of experiment, theory and computation. Curr. Opin. Solid State Mater. Sci. 18, 99 (2014).Google Scholar
18.Curtarolo, S., Setyawan, W., Wang, S., Xue, J., Yang, K., Taylor, R.H., Nelson, L.J., Hart, G.L., Sanvito, S., and Buongiorno-Nardelli, M.: AFLOWLIB. ORG: a distributed materials properties repository from high-throughput ab initio calculations. Comput. Mater. Sci. 58, 227 (2012).Google Scholar
19.Choudhary, K.: Jarvis-DFT, 2014. https://www.nist.gov/document/jarvis-dft1312017pdf (accessed July 17, 2019).Google Scholar
20.Kim, C., Chandrasekaran, A., Huan, T.D., Das, D., and Ramprasad, R.: Polymer genome: a data-powered polymer informatics platform for property predictions. J. Phys. Chem. C 122, 17575 (2018).Google Scholar
21.C. Informatics: Open Citrination Platform. https://citrination.com (accessed July 17, 2019).Google Scholar
22.Georgia Institute of Technology: Institute for Materials: Materials Innovation Network, 2019. https://matin.gatech.edu (accessed July 17, 2019).Google Scholar
23.Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., and Dubourg, V.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825 (2011).Google Scholar
24.Kalinin, S.V., Sumpter, B.G., and Archibald, R.K.: Big-deep-smart data in imaging for guiding materials design. Nat. Mater 14, 973 (2015).Google Scholar
25.Kusiak, A.: Smart manufacturing must embrace big data. Nat. News 544, 23 (2017).Google Scholar
26.Bonnet, N.: Artificial intelligence and pattern recognition techniques in microscope image processing and analysis. In Advances in Imaging and Electron Physics, edited by Hawkes, P.W. (Elsevier, San Diego, CA, 2000), pp. 1.Google Scholar
27.Nyshadham, C., Oses, C., Hansen, J.E., Takeuchi, I., Curtarolo, S., and Hart, G.L.: A computational high-throughput search for new ternary superalloys. Acta Mater. 122, 438 (2017).Google Scholar
28.Isayev, O., Fourches, D., Muratov, E.N., Oses, C., Rasch, K., Tropsha, A., and Curtarolo, S.: Materials cartography: representing and mining materials space using structural and electronic fingerprints. Chem. Mater. 27, 735 (2015).Google Scholar
29.de Pablo, J.J., Jackson, N.E., Webb, M.A., Chen, L.-Q., Moore, J.E., Morgan, D., Jacobs, R., Pollock, T., Schlom, D.G., Toberer, E.S., Analytis, J., Dabo, I., DeLongchamp, D.M., Fiete, G.A., Grason, G.M., Hautier, G., Mo, Y., Rajan, K., Reed, E.J., Rodriguez, E., Stevanovic, V., Suntivich, J., Thornton, K., and Zhao, J.-C.: New frontiers for the materials genome initiative. npj Comput. Mater. 5, 41 (2019).Google Scholar
30.Adams, B.L., Kalidindi, S., and Fullwood, D.T.: Microstructure Sensitive Design for Performance Optimization (Butterworth-Heinemann, Oxford, UK, 2012).Google Scholar
31.Huan, T.D., Mannodi-Kanakkithodi, A., Kim, C., Sharma, V., Pilania, G., and Ramprasad, R.: A polymer dataset for accelerated property prediction and design. Sci. Data 3, 160012 (2016).Google Scholar
32.Ziatdinov, M., Jesse, S., Vasudevan, R.K., Sumpter, B.G., Kalinin, S.V., and Dyck, O.: Tracking atomic structure evolution during directed electron beam induced Si-atom motion in graphene via deep machine learning. (2018) arXiv preprint arXiv:1809.04785.Google Scholar
33.Madsen, J., Liu, P., Kling, J., Wagner, J.B., Hansen, T.W., Winther, O., and Schiøtz, J.: A deep learning approach to identify local structures in atomic-resolution transmission electron microscopy images. Adv. Theory Simul. 1 (2018).Google Scholar
34.Kang, B. and Ceder, G.: Battery materials for ultrafast charging and discharging. Nature 458, 190 (2009).Google Scholar
35.Richards, W.D., Miara, L.J., Wang, Y., Kim, J.C., and Ceder, G.: Interface stability in solid-state batteries. Chem. Mater. 28, 266 (2015).Google Scholar
36.Kirklin, S., Saal, J.E., Hegde, V.I., and Wolverton, C.: High-throughput computational search for strengthening precipitates in alloys. Acta Mater. 102, 125 (2016).Google Scholar
37.Mounet, N., Gibertini, M., Schwaller, P., Campi, D., Merkys, A., Marrazzo, A., Sohier, T., Castelli, I.E., Cepellotti, A., and Pizzi, G.: Two-dimensional materials from high-throughput computational exfoliation of experimentally known compounds. Nat. Nanotechnol. 13, 246 (2018).Google Scholar
38.Choudhary, K., Kalish, I., Beams, R., and Tavazza, F.: High-throughput identification and characterization of two-dimensional materials using density functional theory. Sci. Rep. 7, 5179 (2017).Google Scholar
39.Mo, Y., Ong, S.P., and Ceder, G.: Insights into diffusion mechanisms in P2 layered oxide materials by first-principles calculations. Chem. Mater. 26, 5208 (2014).Google Scholar
40.Beams, R., Cançado, L.G., Krylyuk, S., Kalish, I., Kalanyan, B., Singh, A.K., Choudhary, K., Bruma, A., Vora, P.M., and Tavazza, F.A.N.: Characterization of Few-layer 1T′ MoTe2 by polarization-resolved second harmonic generation and Raman scattering. ACS Nano 10, 9626 (2016).Google Scholar
41.Sholl, D. and Steckel, J.A.: Density Functional Theory: A Practical introduction (John Wiley & Sons, Hoboken, NJ, 2011).Google Scholar
42.Jain, A., Ong, S.P., Hautier, G., Chen, W., Richards, W.D., Dacek, S., Cholia, S., Gunter, D., Skinner, D., and Ceder, G.: Commentary: the materials project: a materials genome approach to accelerating materials innovation. APL Mater. 1, 011002 (2013).Google Scholar
43.Kirklin, S., Saal, J.E., Meredig, B., Thompson, A., Doak, J.W., Aykol, M., Rühl, S., and Wolverton, C.: The open quantum materials database (OQMD): assessing the accuracy of DFT formation energies. npj Comput. Mater. 1, 15010 (2015).Google Scholar
44.Pizzi, G., Cepellotti, A., Sabatini, R., Marzari, N., and Kozinsky, B.: AiiDA: automated interactive infrastructure and database for computational science. Comput. Mater. Sci. 111, 218 (2016).Google Scholar
45.Choudhary, K., Cheon, G., Reed, E., and Tavazza, F.: Elastic properties of bulk and low-dimensional materials using van der Waals density functional. Phys. Rev. B 98, 014107 (2018).Google Scholar
46.Geilhufe, R.M., Olsthoorn, B., Ferella, A., Koski, T., Kahlhoefer, F., Conrad, J., and Balatsky, A.V.: Materials informatics for dark matter detection. (2018) arXiv preprint arXiv:06040.Google Scholar
47.Ramakrishnan, R., Dral, P.O., Rupp, M., and Von Lilienfeld, O.A.: Quantum chemistry structures and properties of 134 kilo molecules. Sci. Data 1, 140022 (2014).Google Scholar
48.Allen, M.P. and Tildesley, D.J.: Computer Simulation of Liquids (Oxford University Press, New York, 2017).Google Scholar
49.Choudhary, K., Biacchi, A.J., Ghosh, S., Hale, L., Walker, A.R.H., and Tavazza, F.: High-throughput assessment of vacancy formation and surface energies of materials using classical force-fields. J. Phys. Condens. Matter 30, 395901 (2018).Google Scholar
50.Choudhary, K., Congo, F.Y.P., Liang, T., Becker, C., Hennig, R.G., and Tavazza, F.: Evaluation and comparison of classical interatomic potentials through a user-friendly interactive web-interface. Sci. Data 4, 160125 (2017).Google Scholar
51.Ogata, S., Lidorikis, E., Shimojo, F., Nakano, A., Vashishta, P., and Kalia, R.K.: Hybrid finite-element/molecular-dynamics/electronic-density-functional approach to materials simulations on parallel computers. Comput. Phys. Commun. 138, 143 (2001).Google Scholar
52.Butler, K.T., Davies, D.W., Cartwright, H., Isayev, O., and Walsh, A.: Machine learning for molecular and materials science. Nature 559, 547 (2018).Google Scholar
53.Ward, L., Agrawal, A., Choudhary, A., and Wolverton, C.: A general-purpose machine learning framework for predicting properties of inorganic materials. npj Comput. Mater. 2, 16028 (2016).Google Scholar
54.Rupp, M., Tkatchenko, A., Müller, K.-R., and Von Lilienfeld, O.A.: Fast and accurate modeling of molecular atomization energies with machine learning. Phys. Rev. Lett. 108, 058301 (2012).Google Scholar
55.Faber, F., Lindmaa, A., Lilienfeld, O.A.V., and Armiento, R.: Crystal structure representations for machine learning models of formation energies. Int. J. Quantum Chem. 115, 1094 (2015).Google Scholar
56.Schütt, K., Glawe, H., Brockherde, F., Sanna, A., Müller, K., and Gross, E.: How to represent crystal structures for machine learning: towards fast prediction of electronic properties. Phys. Rev. B 89, 205118 (2014).Google Scholar
57.Ward, L., Liu, R., Krishna, A., Hegde, V.I., Agrawal, A., Choudhary, A., and Wolverton, C.: Including crystal structure attributes in machine learning models of formation energies via Voronoi tessellations. Phys. Rev. B 96, 024104 (2017).Google Scholar
58.Bartók, A.P., Kondor, R., and Csányi, G.: On representing chemical environments. Phys. Rev. B 87, 184115 (2013).Google Scholar
59.Faber, F.A., Hutchison, L., Huang, B., Gilmer, J., Schoenholz, S.S., Dahl, G.E., Vinyals, O., Kearnes, S., Riley, P.F., and von Lilienfeld, O.A.: Prediction errors of molecular machine learning models lower than hybrid DFT error. J. Chem. Theory Comput. 13, 5255 (2017).Google Scholar
60.Choudhary, K., DeCost, B., and Tavazza, F.: Machine learning with force-field inspired descriptors for materials: fast screening and mapping energy landscape. (2018) arXiv preprint arXiv:07325.Google Scholar
61.Isayev, O., Oses, C., Toher, C., Gossett, E., Curtarolo, S., and Tropsha, A.: Universal fragment descriptors for predicting properties of inorganic crystals. Nat. Commun. 8, 15679 (2017).Google Scholar
62.Kearnes, S., McCloskey, K., Berndl, M., Pande, V., and Riley, P.: Molecular graph convolutions: moving beyond fingerprints. J. Comput. Aided Mol. Des. 30, 595 (2016).Google Scholar
63.Schütt, K., Kindermans, P.-J., Felix, H.E.S., Chmiela, S., Tkatchenko, A., and Müller, K.-R.: SchNet: A continuous-filter convolutional neural network for modeling quantum interactions. In Advances in Neural Information Processing Systems; 2017; p. 991.Google Scholar
64.Xie, T. and Grossman, J.C.: Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Phys. Rev. Lett. 120, 145301 (2018).Google Scholar
65.Schütt, K.T., Sauceda, H.E., Kindermans, P.-J., Tkatchenko, A., and Müller, K.-R.: Schnet—a deep learning architecture for molecules and materials. J. Chem. Phys. 148, 241722 (2018).Google Scholar
66.Chen, C., Ye, W., Zuo, Y., Zheng, C., and Ong, S.P.: Graph networks as a universal machine learning framework for molecules and crystals. (2018) arXiv preprint arXiv:05055.Google Scholar
67.Gilmer, J., Schoenholz, S.S., Riley, P.F., Vinyals, O., and Dahl, G.E.: Neural message passing for quantum chemistry. (2017) arXiv preprint arXiv:01212.Google Scholar
68.Ward, L., Dunn, A., Faghaninia, A., Zimmermann, N.E., Bajaj, S., Wang, Q., Montoya, J., Chen, J., Bystrom, K., and Dylla, M.: Matminer: an open source toolkit for materials data mining. Comput. Mater. Sci. 152, 60 (2018).Google Scholar
69.De Jong, M., Chen, W., Notestine, R., Persson, K., Ceder, G., Jain, A., Asta, M., and Gamst, A.: A statistical learning framework for materials science: application to elastic moduli of k-nary inorganic polycrystalline compounds. Sci. Rep. 6, 34256 (2016).Google Scholar
70.Gómez-Bombarelli, R., Wei, J.N., Duvenaud, D., Hernández-Lobato, J.M., Sánchez-Lengeling, B., Sheberla, D., Aguilera-Iparraguirre, J., Hirzel, T.D., Adams, R.P., and Aspuru-Guzik, A.: Automatic chemical design using a data-driven continuous representation of molecules. ACS Cent. Sci. 4, 268 (2018).Google Scholar
71.Olsthoorn, B., Geilhufe, R.M., Borysov, S.S., and Balatsky, A.V.: Band gap prediction for large organic crystal structures with machine learning. (2018) arXiv preprint arXiv:12814.Google Scholar
72.Mannodi-Kanakkithodi, A., Pilania, G., Huan, T.D., Lookman, T., and Ramprasad, R.: Machine learning strategy for accelerated design of polymer dielectrics. Sci. Rep. 6, 20952 (2016).Google Scholar
73.Collins, C.R., Gordon, G.J., von Lilienfeld, O.A., and Yaron, D.J.: Constant size descriptors for accurate machine learning models of molecular properties. J. Chem. Phys. 148, 241718 (2018).Google Scholar
74.Christensen, A., Faber, F., Huang, B., Bratholm, L., Tkatchenko, A., Müller, K., and von Lilienfeld, O.: QML: A Python Toolkit for Quantum Machine Learning, 2017. https://www.qmlcode.org (accessed July 17, 2019).Google Scholar
75.Khorshidi, A. and Peterson, A.A.: Amp: a modular approach to machine learning in atomistic simulations. Comput. Phys. Commun. 207, 310 (2016).Google Scholar
76.Pun, G., Batra, R., Ramprasad, R., and Mishin, Y.: Physically-informed artificial neural networks for atomistic modeling of materials. (2018) arXiv preprint arXiv:01696.Google Scholar
77.Bartók, A.P. and Csányi, G.: Gaussian approximation potentials: a brief tutorial introduction. Int. J. Quantum Chem. 115, 1051 (2015).Google Scholar
78.Huan, T.D., Batra, R., Chapman, J., Krishnan, S., Chen, L., and Ramprasad, R.: A universal strategy for the creation of machine learning-based atomistic force fields. npj Comput. Mater. 3, 37 (2017).Google Scholar
79.Thompson, A.P., Swiler, L.P., Trott, C.R., Foiles, S.M., and Tucker, G.J.: Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials. J. Comput. Phys. 285, 316 (2015).Google Scholar
80.Kolb, B., Lentz, L.C., and Kolpak, A.M.: Discovering charge density functionals and structure-property relationships with PROPhet: a general framework for coupling machine learning and first-principles methods. Sci. Rep. 7, 1192 (2017).Google Scholar
81.Yao, K., Herr, J.E., Toth, D.W., Mckintyre, R., and Parkhill, J.: The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics. Chem. Sci. 9, 2261 (2018).Google Scholar
82.Smith, J.S., Isayev, O., and Roitberg, A.E.: ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost. Chem. Sci. 8, 3192 (2017).Google Scholar
83.Artrith, N., Urban, A., and Ceder, G.: Efficient and accurate machine-learning interpolation of atomic energies in compositions with many species. Phys. Rev. B 96, 014112 (2017).Google Scholar
84.Wang, H., Zhang, L., Han, J., and Weinan, E.: DeePMD-kit: a deep learning package for many-body potential energy representation and molecular dynamics. Comput. Phys. Commun. 228, 178 (2018).Google Scholar
85.Chmiela, S., Tkatchenko, A., Sauceda, H.E., Poltavsky, I., Schütt, K.T., and Müller, K.-R.: Machine learning of accurate energy-conserving molecular force fields. Sci. Adv. 3, e1603015 (2017).Google Scholar
86.Mardt, A., Pasquali, L., Wu, H., and Noé, F.: VAMPnets for deep learning of molecular kinetics. Nat. Commun. 9, 5 (2018).Google Scholar
87.Xue, D., Balachandran, P.V., Hogden, J., Theiler, J., Xue, D., and Lookman, T.: Accelerated search for materials with targeted properties by adaptive design. Nat. Commun. 7, 11241 (2016).Google Scholar
88.Gunning, David and Aha, David: DARPA’s Explainable Artificial Intelligence (XAI) Program. AI Magazine 40, 44 (2019).Google Scholar
89.Mayr, A., Klambauer, G., Unterthiner, T., Steijaert, M., Wegner, J.K., Ceulemans, H., Clevert, D.-A., and Hochreiter, S.: Large-scale comparison of machine learning methods for drug target prediction on ChEMBL. Chem. Sci. 9, 5541 (2018).Google Scholar
90.Curtarolo, S., Morgan, D., Persson, K., Rodgers, J., and Ceder, G.: Predicting crystal structures with data mining of quantum calculations. Phys. Rev. Lett. 91, 135503 (2003).Google Scholar
91.Pilania, G., Balachandran, P.V., Kim, C., and Lookman, T.: Finding new perovskite halides via machine learning. Front. Mater. 3, 19 (2016).Google Scholar
92.Oliynyk, A.O., Antono, E., Sparks, T.D., Ghadbeigi, L., Gaultois, M.W., Meredig, B., and Mar, A.: High-throughput machine-learning-driven synthesis of full-Heusler compounds. Chem. Mater. 28, 7324 (2016).Google Scholar
93.Hautier, G., Fischer, C.C., Jain, A., Mueller, T., and Ceder, G.: Finding nature's missing ternary oxide compounds using machine learning and density functional theory. Chem. Mater. 22, 3762 (2010).Google Scholar
94.Ahmad, Z., Xie, T., Maheshwari, C., Grossman, J.C., and Viswanathan, V.: Machine learning enabled computational screening of inorganic solid electrolytes for suppression of dendrite formation in lithium metal anodes. ACS Cent. Sci. 4, 996 (2018).Google Scholar
95.Pyzer-Knapp, E.O., Li, K., and Aspuru-Guzik, A.: Learning from the Harvard clean energy project: the use of neural networks to accelerate materials discovery. Adv. Funct. Mater. 25, 6495 (2015).Google Scholar
96.Stanev, V., Oses, C., Kusne, A.G., Rodriguez, E., Paglione, J., Curtarolo, S., and Takeuchi, I.: Machine learning modeling of superconducting critical temperature. npj Comput. Mater. 4, 29 (2018).Google Scholar
97.Botu, V., Batra, R., Chapman, J., and Ramprasad, R.: Machine learning force fields: construction, validation, and outlook. J. Phys. Chem. C 121, 511 (2016).Google Scholar
98.Kalinin, S.V., Rodriguez, B.J., Budai, J.D., Jesse, S., Morozovska, A., Bokov, A.A., and Ye, Z.-G.: Direct evidence of mesoscopic dynamic heterogeneities at the surfaces of ergodic ferroelectric relaxors. Phys. Rev. B 81, 064107 (2010).Google Scholar
99.Blaiszik, B., Chard, K., Pruyne, J., Ananthakrishnan, R., Tuecke, S., and Foster, I.: The materials data facility: data services to advance materials science research. JOM 68, 2045 (2016).Google Scholar
100.Sheppard, D.: Robert Le Rossignol, 1884–1976: engineer of the ‘Haber’ process. Notes Rec. R. Soc. 71, 263 (2017).Google Scholar
101.Hanak, J.J.: The ‘multiple-sample concept’ in materials research: synthesis, compositional analysis and testing of entire multicomponent systems. J. Mater. Sci. 5, 964 (1970).Google Scholar
102.Xiang, X.-D., Sun, X., Briceno, G., Lou, Y., Wang, K.-A., Chang, H., Wallace-Freedman, W.G., Chen, S.-W., and Schultz, P.G.: A combinatorial approach to materials discovery. Science 268, 1738 (1995).Google Scholar
103.Barber, Z. and Blamire, M.: High throughput thin film materials science. Mater. Sci. Technol. 24, 757 (2008).Google Scholar
104.Green, M.L., Choi, C., Hattrick-Simpers, J., Joshi, A., Takeuchi, I., Barron, S., Campo, E., Chiang, T., Empedocles, S., and Gregoire, J.: Fulfilling the promise of the materials genome initiative with high-throughput experimental methodologies. Appl. Phys. Rev. 4, 011105 (2017).Google Scholar
105.Maier, W.F., Stoewe, K., and Sieg, S.: Combinatorial and high-throughput materials science. Angew. Chem. 46, 6016 (2007).Google Scholar
106.Green, M.L., Takeuchi, I., and Hattrick-Simpers, J.R.: Applications of high throughput (combinatorial) methodologies to electronic, magnetic, optical, and energy-related materials. J. Appl. Phys. 113, 231101 (2013).Google Scholar
107.Dubois, J.-L., Duquenne, C., Holderich, W., and Kervennal, J.: Process for Dehydrating Glycerol to Acrolein (Google Patents, 2010).Google Scholar
108.Arriola, D.J., Carnahan, E.M., Hustad, P.D., Kuhlman, R.L., and Wenzel, T.T.: Catalytic production of olefin block copolymers via chain shuttling polymerization. Science 312, 714 (2006).Google Scholar
109.Meguro, S., Ohnishi, T., Lippmaa, M., and Koinuma, H.: Elements of informatics for combinatorial solid-state materials science. Meas. Sci. Technol. 16, 309 (2004).Google Scholar
110.Takeuchi, I., Lippmaa, M., and Matsumoto, Y.: Combinatorial experimentation and materials informatics. MRS Bull. 31, 999 (2006).Google Scholar
111.Koinuma, H.: Combinatorial materials research projects in Japan. Appl. Surf. Sci. 189, 179 (2002).Google Scholar
112.Smotkin, E.S. and Diaz-Morales, R.R.: New electrocatalysts by combinatorial methods. Ann. Rev. Mater. Res. 33, 557 (2003).Google Scholar
113.Watanabe, Y., Umegaki, T., Hashimoto, M., Omata, K., and Yamada, M.: Optimization of Cu oxide catalysts for methanol synthesis by combinatorial tools using 96 well microplates, artificial neural network and genetic algorithm. Catal. Today 89, 455 (2004).Google Scholar
114.Dell'Anna, R., Lazzeri, P., Canteri, R., Long, C.J., Hattrick-Simpers, J., Takeuchi, I., and Anderle, M.: Data analysis in combinatorial experiments: applying supervised principal component technique to investigate the relationship between ToF-SIMS Spectra and the composition distribution of ternary metallic alloy thin films. QSAR Comb. Sci. 27, 171 (2008).Google Scholar
115.Takeuchi, I., Long, C., Famodu, O., Murakami, M., Hattrick-Simpers, J., Rubloff, G., Stukowski, M., and Rajan, K.: Data management and visualization of x-ray diffraction spectra from thin film ternary composition spreads. Rev. Sci. Instrum. 76, 062223 (2005).Google Scholar
116.Yomada, Y. and Kobayashi, T.: Utilization of combinatorial method and high throughput experimentation for development of heterogeneous catalysts. J. Jpn. Petrol Inst. 49, 157 (2006).Google Scholar
117.Rodemerck, U., Baerns, M., Holena, M., and Wolf, D.: Application of a genetic algorithm and a neural network for the discovery and optimization of new solid catalytic materials. Appl. Surf. Sci. 223, 168 (2004).Google Scholar
118.Long, C., Hattrick-Simpers, J., Murakami, M., Srivastava, R., Takeuchi, I., Karen, V.L., and Li, X.: Rapid structural mapping of ternary metallic alloy systems using the combinatorial approach and cluster analysis. Rev. Sci. Instrum. 78, 072217 (2007).Google Scholar
119.Gregoire, J.M., Dale, D., and Van Dover, R.B.: A wavelet transform algorithm for peak detection and application to powder x-ray diffraction data. Rev. Sci. Instrum. 82, 015105 (2011).Google Scholar
120.Long, C., Bunker, D., Li, X., Karen, V., and Takeuchi, I.: Rapid identification of structural phases in combinatorial thin-film libraries using x-ray diffraction and non-negative matrix factorization. Rev. Sci. Instrum. 80, 103902 (2009).Google Scholar
121.LeBras, R., Damoulas, T., Gregoire, J.M., Sabharwal, A., Gomes, C.P., and Van Dover, R.B.: Constraint reasoning and kernel clustering for pattern decomposition with scaling. In International Conference on Principles and Practice of Constraint Programming, Perugia, Italy (Springer, 2011), pp. 508.Google Scholar
122.Bunn, J.K., Han, S., Zhang, Y., Tong, Y., Hu, J., and Hattrick-Simpers, J.R.: Generalized machine learning technique for automatic phase attribution in time variant high-throughput experimental studies. J. Mater. Res. 30, 879 (2015).Google Scholar
123.Bunn, J.K., Hu, J., and Hattrick-Simpers, J.R.: Semi-supervised approach to phase identification from combinatorial sample diffraction patterns. JOM 68, 2116 (2016).Google Scholar
124.Hattrick-Simpers, J.R., Gregoire, J.M., and Kusne, A.G.: Perspective: composition–structure–property mapping in high-throughput experiments: turning data into knowledge. APL Mater. 4, 053211 (2016).Google Scholar
125.Kusne, A.G., Keller, D., Anderson, A., Zaban, A., and Takeuchi, I.: High-throughput determination of structural phase diagram and constituent phases using GRENDEL. Nanotechnology 26, 444002 (2015).Google Scholar
126.Suram, S.K., Xue, Y., Bai, J., Le Bras, R., Rappazzo, B., Bernstein, R., Bjorck, J., Zhou, L., van Dover, R.B., and Gomes, C.P.: Automated phase mapping with AgileFD and its application to light absorber discovery in the V–Mn–Nb oxide system. ACS Comb. Sci. 19, 37 (2016).Google Scholar
127.Kusne, A.G., Gao, T., Mehta, A., Ke, L., Nguyen, M.C., Ho, K.-M., Antropov, V., Wang, C.-Z., Kramer, M.J., Long, C., and Takeuchi, I.: On-the-fly machine-learning for high-throughput experiments: search for rare-earth-free permanent magnets. Sci. Rep. 4, 6367 (2014).Google Scholar
128.Cui, J., Chu, Y.S., Famodu, O.O., Furuya, Y., Hattrick-Simpers, J., James, R.D., Ludwig, A., Thienhaus, S., Wuttig, M., and Zhang, Z.: Combinatorial search of thermoelastic shape-memory alloys with extremely small hysteresis width. Nat. Mater. 5, 286 (2006).Google Scholar
129.Zakutayev, A., Stevanovic, V., and Lany, S.: Non-equilibrium alloying controls optoelectronic properties in Cu2O thin films for photovoltaic absorber applications. Appl. Phys. Lett. 106, 123903 (2015).Google Scholar
130.Yan, Q., Yu, J., Suram, S.K., Zhou, L., Shinde, A., Newhouse, P.F., Chen, W., Li, G., Persson, K.A., and Gregoire, J.M.: Solar fuels photoanode materials discovery by integrating high-throughput theory and experiment. Proc. Natl. Acad. Sci. USA 114, 3040 (2017).Google Scholar
131.Hattrick-Simpers, J.R., Choudhary, K., and Corgnale, C.: A simple constrained machine learning model for predicting high-pressure-hydrogen-compressor materials. Mol. Sys. Des. Eng 3, 509 (2018).Google Scholar
132.Ren, F., Ward, L., Williams, T., Laws, K.J., Wolverton, C., Hattrick-Simpers, J., and Mehta, A.: Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments. Sci. Adv. 4, eaaq1566 (2018).Google Scholar
133.Yuan, R., Liu, Z., Balachandran, P.V., Xue, D., Zhou, Y., Ding, X., Sun, J., Xue, D., and Lookman, T.: Accelerated discovery of large electrostrains in BaTiO3-based piezoelectrics using active learning. Adv. Mater. 30, 1702884 (2018).Google Scholar
134.Bassman, L., Rajak, P., Kalia, R.K., Nakano, A., Sha, F., Sun, J., Singh, D.J., Aykol, M., Huck, P., and Persson, K.: Active learning for accelerated design of layered materials. npj Comput. Mater. 4, 74 (2018).Google Scholar
135.Podryabinkin, E.V., Tikhonov, E.V., Shapeev, A.V., and Oganov, A.R.: Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning. Phys. Rev. B 99, 064114 (2019).Google Scholar
136.Talapatra, A., Boluki, S., Duong, T., Qian, X., Dougherty, E., and Arróyave, R.: Autonomous efficient experiment design for materials discovery with Bayesian model averaging. Phys. Rev. Mater. 2, 113803 (2018).Google Scholar
137.Lookman, T., Balachandran, P.V., Xue, D., and Yuan, R.: Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design. npj Comput. Mater. 5, 21 (2019).Google Scholar
138.Meredig, B., Antono, E., Church, C., Hutchinson, M., Ling, J., Paradiso, S., Blaiszik, B., Foster, I., Gibbons, B., and Hattrick-Simpers, J.: Can machine learning identify the next high-temperature superconductor? Examining extrapolation performance for materials discovery. Mol. Syst. Des. Eng. 3, 819 (2018).Google Scholar
139.King, R.D., Rowland, J., Aubrey, W., Liakata, M., Markham, M., Soldatova, L.N., Whelan, K.E., Clare, A., Young, M., and Sparkes, A.: The robot scientist Adam. Computer 42, 46 (2009).Google Scholar
140.Nikolaev, P., Hooper, D., Webber, F., Rao, R., Decker, K., Krein, M., Poleski, J., Barto, R., and Maruyama, B.: Autonomy in materials research: a case study in carbon nanotube growth. npj Comput. Mater. 2, 16031 (2016).Google Scholar
141.Roch, L.M., Häse, F., Kreisbeck, C., Tamayo-Mendoza, T., Yunker, L.P., Hein, J.E., and Aspuru-Guzik, A.: ChemOS: orchestrating autonomous experimentation. Sci. Robot. 3, eaat5559 (2018).Google Scholar
142.DeCost, B. and Kusne, G.: Deep Transfer Learning for Active Optimization of Functional Materials Properties in the Data-Limited Regime (MRS Fall, Boston, MA, 2018).Google Scholar
143.Kusne, G., DeCost, B., Hattrick-Simpers, J., and Takeuchi, I.: Autonomous Materials Research Systems—Phase Mapping (MRS Fall, Boston, MA, 2018).Google Scholar
144.Caramelli, D., Salley, D., Henson, A., Camarasa, G.A., Sharabi, S., Keenan, G., and Cronin, L.: Networking chemical robots for reaction multitasking. Nat. Commun 9, 3406 (2018).Google Scholar
145.Klucznik, T., Mikulak-Klucznik, B., McCormack, M.P., Lima, H., Szymkuć, S., Bhowmick, M., Molga, K., Zhou, Y., Rickershauser, L., and Gajewska, E.P.: Efficient syntheses of diverse, medicinally relevant targets planned by computer and executed in the laboratory. Chem 4, 522 (2018).Google Scholar
146.ASM International: https://www.asminternational.org/materials-resources/online-databases/-/journal_content/56/10192/15468789/DATABASE (2019).Google Scholar
147.Ziatdinov, M., Dyck, O., Maksov, A., Li, X., Sang, X., Xiao, K., Unocic, R.R., Vasudevan, R., Jesse, S., and Kalinin, S.V.: Deep learning of atomically resolved scanning transmission electron microscopy images: chemical identification and tracking local transformations. ACS Nano 11, 12742 (2017).Google Scholar
148.Ziatdinov, M., Maksov, A., and Kalinin, S.V.: Learning surface molecular structures via machine vision. npj Comput. Mater. 3, 31 (2017).Google Scholar
149.Barthel, J.: Dr. Probe: a software for high-resolution STEM image simulation. Ultramicroscopy 193, 1 (2018).Google Scholar
150.Long, J., Shelhamer, E., and Darrell, T.: Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, Boston, MA (2015), pp. 3431.Google Scholar
151.Ziatdinov, M., Dyck, O., Sumpter, B.G., Jesse, S., Vasudevan, R.K., and Kalinin, S.V.: Building and exploring libraries of atomic defects in graphene: scanning transmission electron and scanning tunneling microscopy study. (2018) arXiv preprint arXiv:1809.04256.Google Scholar
152.Maksov, A., Dyck, O., Wang, K., Xiao, K., Geohegan, D.B., Sumpter, B.G., Vasudevan, R.K., Jesse, S., Kalinin, S.V., and Ziatdinov, M.: Deep learning analysis of defect and phase evolution during electron beam-induced transformations in WS2. npj Comput. Mater. 5, 12 (2019).Google Scholar
153.Ziatdinov, M., Dyck, O., Jesse, S., and Kalinin, S.V.. Atomic mechanisms for the Si atom dynamics in graphene: chemical transformations at the edge and in the bulk. (2019) arXiv preprint arXiv:1901.09322.Google Scholar
154.Yablon, D.G., Gannepalli, A., Proksch, R., Killgore, J., Hurley, D.C., Grabowski, J., and Tsou, A.H.: Quantitative viscoelastic mapping of polyolefin blends with contact resonance atomic force microscopy. Macromolecules 45, 4363 (2012).Google Scholar
155.Schlücker, S., Schaeberle, M.D., Huffman, S.W., and Levin, I.W.: Raman microspectroscopy: a comparison of point, line, and wide-field imaging methodologies. Anal. Chem. 75, 4312 (2003).Google Scholar
156.Ievlev, A.V., Maksymovych, P., Trassin, M., Seidel, J., Ramesh, R., Kalinin, S.V., and Ovchinnikova, O.S.: Chemical state evolution in ferroelectric films during tip-induced polarization and electroresistive switching. ACS Appl. Mater. Interfaces 8, 29588 (2016).Google Scholar
157.Hruszkewycz, S., Folkman, C., Highland, M., Holt, M., Baek, S., Streiffer, S., Baldo, P., Eom, C., and Fuoss, P.: X-ray nanodiffraction of tilted domains in a poled epitaxial BiFeO3 thin film. Appl. Phys. Lett. 99, 232903 (2011).Google Scholar
158.Cai, Z., Lai, B., Xiao, Y., and Xu, S.: An X-ray diffraction microscope at the Advanced Photon Source. In Journal de Physique IV (Proceedings); EDP Sciences, 2003; p. 17.Google Scholar
159.Kalinin, S.V., Karapetian, E., and Kachanov, M.: Nanoelectromechanics of piezoresponse force microscopy. Phys. Rev. B 70, 184101 (2004).Google Scholar
160.Eliseev, E.A., Kalinin, S.V., Jesse, S., Bravina, S.L., and Morozovska, A.N.: Electromechanical detection in scanning probe microscopy: tip models and materials contrast. J. Appl. Phys. 102, 014109 (2007).Google Scholar
161.Monig, H., Todorovic, M., Baykara, M.Z., Schwendemann, T.C., Rodrigo, L., Altman, E.I., Perez, R., and Schwarz, U.D.: Understanding scanning tunneling microscopy contrast mechanisms on metal oxides: a case study. ACS Nano 7, 10233 (2013).Google Scholar
162.Ievlev, A.V., Susner, M.A., McGuire, M.A., Maksymovych, P., and Kalinin, S.V.: Quantitative analysis of the local phase transitions induced by laser heating. ACS Nano 9, 12442 (2015).Google Scholar
163.Dönges, S.A., Khatib, O., O'Callahan, B.T., Atkin, J.M., Park, J.H., Cobden, D., and Raschke, M.B.: Ultrafast nanoimaging of the photoinduced phase transition dynamics in VO2. Nano Lett. 16, 3029 (2016).Google Scholar
164.Kim, Y., Strelcov, E., Hwang, I.R., Choi, T., Park, B.H., Jesse, S., and Kalinin, S.V.: Correlative multimodal probing of ionically-mediated electromechanical phenomena in simple oxides. Sci. Rep. 3, 2924 (2013).Google Scholar
165.Ovchinnikov, O., Jesse, S., Bintacchit, P., Trolier-McKinstry, S., and Kalinin, S.V.: Disorder identification in hysteresis data: recognition analysis of the random-bond–random-field ising model. Phys. Rev. Lett. 103, 157203 (2009).Google Scholar
166.Borodinov, N., Neumayer, S., Kalinin, S.V., Ovchinnikova, O.S., Vasudevan, R.K., and Jesse, S.: Deep neural networks for understanding noisy data applied to physical property extraction in scanning probe microscopy. npj Comput. Mater. 5, 25 (2019).Google Scholar
167.Pradhan, D.K., Kumari, S., Strelcov, E., Pradhan, D.K., Katiyar, R.S., Kalinin, S.V., Laanait, N., and Vasudevan, R.K.: Reconstructing phase diagrams from local measurements via Gaussian processes: mapping the temperature-composition space to confidence. npj Comput. Mater. 4, 1 (2018).Google Scholar
168.Li, L., Yang, Y., Zhang, D., Ye, Z.-G., Jesse, S., Kalinin, S.V., and Vasudevan, R.K.: Machine learning-enabled identification of material phase transitions based on experimental data: exploring collective dynamics in ferroelectric relaxors. Sci. Adv 4, 8672 (2018).Google Scholar
169.Shah, V.P., Younan, N.H., and King, R.L.: An efficient pan-sharpening method via a combined adaptive PCA approach and contourlets. IEEE Trans. Geosci. Remote Sens. 46, 1323 (2008).Google Scholar
170.Somnath, S., Belianinov, A., Kalinin, S.V., and Jesse, S.: Full information acquisition in piezoresponse force microscopy. Appl. Phys. Lett 107, 263102 (2015).Google Scholar
171.Somnath, S., Law, K.J., Morozovska, A., Maksymovych, P., Kim, Y., Lu, X., Alexe, M., Archibald, R., Kalinin, S.V., and Jesse, S.: Ultrafast current imaging by Bayesian inversion. Nat. Commun. 9, 513 (2018).Google Scholar
172.Somnath, S., Belianinov, A., Kalinin, S.V., and Jesse, S.: Rapid mapping of polarization switching through complete information acquisition. Nat. Commun. 7, 13290 (2016).Google Scholar
173.Collins, L., Belianinov, A., Somnath, S., Balke, N., Kalinin, S.V., and Jesse, S.: Full data acquisition in kelvin probe force microscopy: mapping dynamic electric phenomena in real space. Sci. Rep. 6, 30557 (2016).Google Scholar
174.Balke, N., Jesse, S., Yu, P., Carmichael, B., Kalinin, S.V., and Tselev, A.: Quantification of surface displacements and electromechanical phenomena via dynamic atomic force microscopy. Nanotechnology 27, 425707 (2016).Google Scholar
175.Labuda, A. and Proksch, R.: Quantitative measurements of electromechanical response with a combined optical beam and interferometric atomic force microscope. Appl. Phys. Lett. 106, 253103 (2015).Google Scholar
176.Kalidindi, S.R. and De Graef, M.: Materials data science: current status and future outlook. Ann. Rev. Mater. Res. 45, 171 (2015).Google Scholar
177.Fullwood, D.T., Niezgoda, S.R., and Kalidindi, S.R.: Microstructure reconstructions from 2-point statistics using phase-recovery algorithms. Acta Mater. 56, 942 (2008).Google Scholar
178.Kalidindi, S.R., Niezgoda, S.R., and Salem, A.A.: Microstructure informatics using higher-order statistics and efficient data-mining protocols. JOM 63, 34 (2011).Google Scholar
179.Sharma, V., Wang, C., Lorenzini, R.G., Ma, R., Zhu, Q., Sinkovits, D.W., Pilania, G., Oganov, A.R., Kumar, S., Sotzing, G.A., Boggs, S.A., and Ramprasad, R.: Rational design of all organic polymer dielectrics. Nat. Commun. 5, 4845 (2014).Google Scholar
180.Gopakumar, A.M., Balachandran, P.V., Xue, D., Gubernatis, J.E., and Lookman, T.: Multi-objective optimization for materials discovery via adaptive design. Sci. Rep. 8, 3738 (2018).Google Scholar
181.Hutchinson, M.L., Antono, E., Gibbons, B.M., Paradiso, S., Ling, J., and Meredig, B.: Overcoming data scarcity with transfer learning. (2017) arXiv preprint arXiv:1711.05099.Google Scholar
182.Oviedo, F., Ren, Z., Sun, S., Settens, C., Liu, Z., Hartono, N.T.P., Ramasamy, S., DeCost, B.L., Tian, S.I.P., Romano, G., Gilad Kusne, A., and Buonassisi, T.: Fast and interpretable classification of small x-ray diffraction datasets using data augmentation and deep neural networks. npj Comput. Mater. 5, 60 (2019).Google Scholar
183.Vlcek, L., Ziatdinov, M., Maksov, A., Tselev, A., Baddorf, A.P., Kalinin, S.V., and Vasudevan, R.K.: Learning from imperfections: predicting structure and thermodynamics from atomic imaging of fluctuations. ACS Nano 13, 718 (2019).Google Scholar
184.Vlcek, L., Vasudevan, R.K., Jesse, S., and Kalinin, S.V.: Consistent integration of experimental and ab initio data into effective physical models. J. Chem. Theory Comput. 13, 5179 (2017).Google Scholar
185.Vlcek, L., Maksov, A., Pan, M., Vasudevan, R.K., and Kalinin, S.V.: Knowledge extraction from atomically resolved images. ACS Nano 11, 10313 (2017).Google Scholar
186.Belianinov, A., He, Q., Kravchenko, M., Jesse, S., Borisevich, A., and Kalinin, S.V.: Identification of phases, symmetries and defects through local crystallography. Nat. Commun 6, 7801 (2015).Google Scholar
187.Ross, D., Strychalski, E.A., Jarzynski, C., and Stavis, S.M.: Equilibrium free energies from non-equilibrium trajectories with relaxation fluctuation spectroscopy. Nat. Phys 14, 842 (2018).Google Scholar
188.Kutnjak, Z., Petzelt, J., and Blinc, R.: The giant electromechanical response in ferroelectric relaxors as a critical phenomenon. Nature 441, 956 (2006).Google Scholar
189.Somnath, S., Smith, C.R., Laanait, N., Vasudevan, R.K., Ievlev, A., Belianinov, A., Lupini, A.R., Shankar, M., Kalinin, S.V., and Jesse, S.: USID and pycroscopy—open frameworks for storing and analyzing spectroscopic and imaging data. (2019) arXiv preprint arXiv:1903.09515.Google Scholar
190.Hall, S.R., Allen, F.H., and Brown, I.D.: The crystallographic information file (CIF): a new standard archive file for crystallography. Acta Crystallogr. A 47, 655 (1991).Google Scholar
191.Pearl, J.: Theoretical impediments to machine learning with seven sparks from the causal revolution. (2018) arXiv preprint arXiv:1801.04016.Google Scholar