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Experiment Specification, Capture and Laboratory Automation Technology (ESCALATE): a software pipeline for automated chemical experimentation and data management

Published online by Cambridge University Press:  04 June 2019

Ian M. Pendleton Open the ORCID record for Ian M. Pendleton [Opens in a new window] ,
Gary Cattabriga ,
Zhi Li ,
Mansoor Ani Najeeb ,
Sorelle A. Friedler ,
Alexander J. Norquist Open the ORCID record for Alexander J. Norquist [Opens in a new window] ,
Emory M. Chan Open the ORCID record for Emory M. Chan [Opens in a new window]  and
Joshua Schrier Open the ORCID record for Joshua Schrier [Opens in a new window]
Ian M. Pendleton
Affiliation:
Department of Chemistry, Haverford College, 370 Lancaster Avenue, Haverford, Pennsylvania 19041, USA
Gary Cattabriga
Affiliation:
Department of Chemistry, Haverford College, 370 Lancaster Avenue, Haverford, Pennsylvania 19041, USA
Zhi Li
Affiliation:
Molecular Foundry, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, California 94720, USA
Mansoor Ani Najeeb
Affiliation:
Department of Chemistry, Haverford College, 370 Lancaster Avenue, Haverford, Pennsylvania 19041, USA
Sorelle A. Friedler
Affiliation:
Department of Computer Science, Haverford College, 370 Lancaster Avenue, Haverford, Pennsylvania 19041, USA
Alexander J. Norquist
Affiliation:
Department of Chemistry, Haverford College, 370 Lancaster Avenue, Haverford, Pennsylvania 19041, USA
Emory M. Chan
Affiliation:
Molecular Foundry, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, California 94720, USA
Joshua Schrier*
Affiliation:
Department of Chemistry, Fordham University, 441 E. Fordham Road, The Bronx, New York, 10458, USA
*
Address all correspondence to Joshua Schrier at [email protected]
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Abstract

Applying artificial intelligence to materials research requires abundant curated experimental data and the ability for algorithms to request new experiments. ESCALATE (Experiment Specification, Capture and Laboratory Automation Technology)—an ontological framework and open-source software package—solves this problem by providing an abstraction layer for human- and machine-readable experiment specification, comprehensive and extensible (meta-) data capture, and structured data reporting. ESCALATE simplifies the initial data collection process, and its reporting and experiment generation mechanisms simplify machine learning integration. An initial ESCALATE implementation for metal halide perovskite crystallization was used to perform 55 rounds of algorithmically-controlled experiment plans, capturing 4336 individual experiments.

Type
Artificial Intelligence Research Letters
Information
MRS Communications , Volume 9 , Issue 3 , September 2019 , pp. 846 - 859
Copyright
Copyright © Materials Research Society 2019 

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References

1.NSF CHE Workshop: Framing the Role of Big Data and Modern Data Science in Chemistry. Available at: https://www.nsf.gov/mps/che/workshops/data_chemistry_workshop_report_03262018.pdf (accessed December 21, 2018).Google Scholar
2.Mission Innovation: Materials Acceleration Platform: Accelerating Advanced Energy Materials Discovery by Integrating High-Throughput Methods with Artificial Intelligence Report of the Clean Energy Materials Innovation Challenge Expert Workshop. Available at: http://mission-innovation.net/wp-content/uploads/2018/01/Mission-Innovation-IC6-Report-Materials-Acceleration-Platform-Jan-2018.pdf (accessed December 21, 2018).Google Scholar
3.Multi-Agency, Multi-Year Program Plan in Advanced Energy Materials Discovery, Development, and Process Design: Available at: https://www.energy.gov/sites/prod/files/2018/12/f58/Multi-Agency%20Multi-Year%20Program%20Plan%20in%20Advanced%20Energy%20Materials%20Discovery%20Development%20and%20Process%20Design_Workshop%20Summary%20Report.pdf (accessed December 21, 2018).Google Scholar
4.Henson, A.B., Gromski, P.S., and Cronin, L.: Designing algorithms to aid discovery by chemical robots. ACS Cent. Sci. 4, 793–804 (2018).Google Scholar
5.Tabor, D.P., Roch, L.M., Saikin, S.K., Kreisbeck, C., Sheberla, D., Montoya, J.H., Dwaraknath, S., Aykol, M., Ortiz, C., Tribukait, H., Amador-Bedolla, C., Brabec, C.J., Maruyama, B., Persson, K.A., and Aspuru-Guzik, A.: Accelerating the discovery of materials for clean energy in the era of smart automation. Nat. Rev. Mater. 3, 5–20 (2018).Google Scholar
6.Correa-Baena, J.-P., Hippalgaonkar, K., van Duren, J., Jaffer, S., Chandrasekhar, V.R., Stevanovic, V., Wadia, C., Guha, S., and Buonassisi, T.: Accelerating materials development via automation, machine learning, and high-performance computing. Joule 2, 1410–1420 (2018).Google Scholar
7.Xiang, X.-D., Sun, X., Briceño, 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, 1738–1740 (1995).Google Scholar
8.Schultz, P.G. and Xiang, X.-D.: Combinatorial approaches to materials science. Curr. Opin. Solid State Mater. Sci. 3, 153–158 (1998).Google Scholar
9.Koinuma, H. and Takeuchi, I.: Combinatorial solid-state chemistry of inorganic materials. Nat. Mater. 3, 429–438 (2004).Google Scholar
10.Takeuchi, I., van Dover, R.B., and Koinuma, H.: Combinatorial synthesis and evaluation of functional inorganic materials using thin-film techniques. MRS Bull. 27, 301–308 (2002).Google Scholar
11.Barber, Z.H. and Blamire, M.G.: High throughput thin film materials science. Mater. Sci. Technol. 24, 757–770 (2008).Google Scholar
12.Woo, S.I., Kim, K.W., Cho, H.Y., Oh, K.S., Jeon, M.K., Tarte, N.H., Kim, T.S., and Mahmood, A.: Current status of combinatorial and high-throughput methods for discovering new materials and catalysts. QSAR Comb. Sci. 24, 138–154 (2005).Google Scholar
13.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
14.Baumes, L.A., Serna, P., and Corma, A.: Merging traditional and high-throughput approaches results in efficient design, synthesis and screening of catalysts for an industrial process. Appl. Catal. A 381, 197–208 (2010).Google Scholar
15.Potyrailo, R., Rajan, K., Stoewe, K., Takeuchi, I., Chisholm, B., and Lam, H.: Combinatorial and high-throughput screening of materials libraries: review of state of the art. ACS Comb. Sci. 13, 579–633 (2011).Google Scholar
16.Shevlin, M.: Practical high-throughput experimentation for chemists. ACS Med. Chem. Lett. 8, 601–607 (2017).Google Scholar
17.Maier, W.F., Stöwe, K., and Sieg, S.: Combinatorial and high-throughput materials science. Angew. Chem. Int. Ed Engl. 46, 6016–6067 (2007).Google Scholar
18.Butler, K.T., Davies, D.W., Cartwright, H., Isayev, O., and Walsh, A.: Machine learning for molecular and materials science. Nature 559, 547–555 (2018).Google Scholar
19.Sanchez-Lengeling, B. and Aspuru-Guzik, A.: Inverse molecular design using machine learning: generative models for matter engineering. Science 361, 360–365 (2018).Google Scholar
20.Raccuglia, P., Elbert, K.C., Adler, P.D.F., Falk, C., Wenny, M.B., Mollo, A., Zeller, M., Friedler, S.A., Schrier, J., and Norquist, A.J.: Machine-learning-assisted materials discovery using failed experiments. Nature 533, 73–76 (2016).Google Scholar
21.Ahneman, D.T., Estrada, J.G., Lin, S., Dreher, S.D., and Doyle, A.G.: Predicting reaction performance in C-N cross-coupling using machine learning. Science 360, 186–190 (2018).Google Scholar
22.Lin, S., Dikler, S., Blincoe, W.D., Ferguson, R.D., Sheridan, R.P., Peng, Z., Conway, D.V., Zawatzky, K., Wang, H., Cernak, T., Davies, I.W., DiRocco, D.A., Sheng, H., Welch, C.J., and Dreher, S.D.: Mapping the dark space of chemical reactions with extended nanomole synthesis and MALDI-TOF MS. Science. 361, eaar6236 (2018).Google Scholar
23.Xu, R.J., Olshansky, J.H., Adler, P.D.F., Huang, Y., Smith, M.D., Zeller, M., Schrier, J., and Norquist, A.J.: Understanding structural adaptability: a reactant informatics approach to experiment design. Mol. Syst. Des. Eng. 3, 473–484 (2018).Google Scholar
24.Duros, V., Grizou, J., Xuan, W., Hosni, Z., Long, D.-L., Miras, H.N., and Cronin, L.: Human versus robots in the discovery and crystallization of gigantic polyoxometalates. Angew. Chem. Int. Ed. Engl. 56, 10815–10820 (2017).Google Scholar
25.Zhou, Z., Li, X., and Zare, R.N.: Optimizing chemical reactions with deep reinforcement learning. ACS Cent. Sci. 3, 1337–1344 (2017).Google Scholar
26.Bédard, A.-C., Adamo, A., Aroh, K.C., Russell, M.G., Bedermann, A.A., Torosian, J., Yue, B., Jensen, K.F., and Jamison, T.F.: Reconfigurable system for automated optimization of diverse chemical reactions. Science 361, 1220–1225 (2018).Google Scholar
27.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
28.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
29.Celse, B., Rebours, S., Gay, F., Coste, P., Bourgeois, L., Zammit, O., and Lebacque, V.: Integration of an informatics system in a high throughput experimentation. Description of a global framework illustrated through several examples. Oil Gas Sci. Technol.––Rev. IFP Energies nouvelles 68, 445–468 (2013).Google Scholar
30.Bai, J., Xue, Y., Bjorck, J., Le Bras, R., Rappazzo, B., Bernstein, R., Suram, S.K., Van Dover, R.B., Gregoire, J.M., and Gomes, C.P.: Phase mapper: accelerating materials discovery with AI. AIMag 39, 15 (2018).Google Scholar
31.Cao, B., Adutwum, L.A., Oliynyk, A.O., Luber, E.J., Olsen, B.C., Mar, A., and Buriak, J.M.: How To optimize materials and devices via design of experiments and machine learning: demonstration using organic photovoltaics. ACS Nano 12, 7434–7444 (2018).Google Scholar
32.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, 1 (2018).Google Scholar
33.Yan, Q., Yu, J., Suram, S.K., Zhou, L., Shinde, A., Newhouse, P.F., Chen, W., Li, G., Persson, K.A., Gregoire, J.M., and Neaton, J.B.: Solar fuels photoanode materials discovery by integrating high-throughput theory and experiment. Proc. Natl. Acad. Sci. USA 114, 3040–3043 (2017).Google Scholar
34.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
35.Shinde, A., Suram, S.K., Yan, Q., Zhou, L., Singh, A.K., Yu, J., Persson, K.A., Neaton, J.B., and Gregoire, J.M.: Discovery of manganese-based solar fuel photoanodes via integration of electronic structure calculations, Pourbaix stability modeling, and high-throughput experiments. ACS Energy Lett. 2, 2307–2312 (2017).Google Scholar
36.Green, M.L., Choi, C.L., Hattrick-Simpers, J.R., Joshi, A.M., Takeuchi, I., Barron, S.C., Campo, E., Chiang, T., Empedocles, S., Gregoire, J.M., Kusne, A.G., Martin, J., Mehta, A., Persson, K., Trautt, Z., Van Duren, J., and Zakutayev, A.: Fulfilling the promise of the materials genome initiative with high-throughput experimental methodologies. Appl. Phys. Rev. 4, 011105 (2017).Google Scholar
37.Zakutayev, A., Wunder, N., Schwarting, M., Perkins, J.D., White, R., Munch, K., Tumas, W., and Phillips, C.: An open experimental database for exploring inorganic materials. Sci. Data 5, 180053 (2018).Google Scholar
38.Li, J., Lu, Y., Xu, Y., Liu, C., Tu, Y., Ye, S., Liu, H., Xie, Y., Qian, H., and Zhu, X.: AIR-Chem: authentic intelligent robotics for chemistry. J. Phys. Chem. A 122, 9142–9148 (2018).Google Scholar
39.Adams, N. and Schubert, U.S.: From data to knowledge: chemical data management, data mining, and modeling in polymer science. J. Comb. Chem. 6, 12–23 (2004).Google Scholar
40.Adams, N. and Schubert, U.S.: Software solutions for combinatorial and high-throughput materials and polymer research. Macromol. Rapid Commun. 25, 48–58 (2004).Google Scholar
41.Roch, L.M., Häse, F., Kreisbeck, C., Tamayo-Mendoza, T., Yunker, L.P.E., Hein, J.E., and Aspuru-Guzik, A.: ChemOS: orchestrating autonomous experimentation. Sci Robot. 3, eaat5559 (2018).Google Scholar
42.Hachmann, J., Afzal, M.A.F., Haghighatlari, M., and Pal, Y.: Building and deploying a cyberinfrastructure for the data-driven design of chemical systems and the exploration of chemical space. Mol. Simul. 44, 921–929 (2018).Google Scholar
43.Baumes, L.A., Jimenez, S., and Corma, A.: hITeQ: a new workflow-based computing environment for streamlining discovery. Application in materials science. Catal. Today 159, 126–137 (2011).Google Scholar
44.Tran, K., Palizhati, A., Back, S., and Ulissi, Z.W.: Dynamic workflows for routine materials discovery in surface science. J. Chem. Inf. Model. 58, 2392–2400 (2018).Google Scholar
45.Bates, M., Berliner, A.J., Lachoff, J., Jaschke, P.R., and Groban, E.S.: Wet Lab accelerator: a web-based application democratizing laboratory automation for synthetic biology. ACS Synth. Biol. 6, 167–171 (2017).Google Scholar
46.Autoprotocol: Available at: http://autoprotocol.org/ (accessed January 8, 2019).Google Scholar
47.Linshiz, G., Stawski, N., Poust, S., Bi, C., Keasling, J.D., and Hillson, N.J.: PaR-PaR laboratory automation platform. ACS Synth. Biol. 2, 216–222 (2013).Google Scholar
48.Whitehead, E., Rudolf, F., Kaltenbach, H.-M., and Stelling, J.: Automated planning enables complex protocols on liquid-handling robots. ACS Synth. Biol. 7, 922–932 (2018).Google Scholar
49.Keller, B., Vrana, J., Miller, A., Newman, G., and Klavins, E.: Aquarium: The Laboratory Operating System (Version v2.5.0). Zenodo. (2019).Google Scholar
50.Emerald Cloud Lab: Available at: https://www.emeraldcloudlab.com/ (accessed January 11, 2019).Google Scholar
51.Miles, B. and Lee, P.L.: Achieving reproducibility and closed-loop automation in biological experimentation with an IoT-enabled lab of the future. SLAS Technol. 23, 432–439 (2018).Google Scholar
52.Transcriptic: Powering On-Demand Biology | Transcriptic. Available at: https://transcriptic.com/ (accessed January 15, 2019).Google Scholar
53.Mitzi, D.B.: Synthesis, Structure, and Properties of Organic-Inorganic Perovskites and Related Materials In Progress in Inorganic Chemistry, edited by Karlin, K.D. (John Wiley & Sons, Inc., 9, Hoboken, NJ, USA, 1999), pp. 1–121.Google Scholar
54.Smith, M.D., Crace, E.J., Jaffe, A., and Karunadasa, H.I.: The diversity of layered halide perovskites. Annu. Rev. Mater. Res. 48, 111–136 (2018).Google Scholar
55.Li, S., Zhang, C., Song, J.-J., Xie, X., Meng, J.-Q., and Xu, S.: Metal halide perovskite single crystals: from growth process to application. Crystals. (Basel) 8, 220 (2018).Google Scholar
56.Snaith, H.J.: Present status and future prospects of perovskite photovoltaics. Nat. Mater. 17, 372–376 (2018).Google Scholar
57.Ansari, M.I.H., Qurashi, A., and Nazeeruddin, M.K.: Frontiers, opportunities, and challenges in perovskite solar cells: a critical review. J. Photochem. Photobiol. C: Photochem. Rev. 35, 1–24 (2018).Google Scholar
58.Yao, F., Gui, P., Zhang, Q., and Lin, Q.: Molecular engineering of perovskite photodetectors: recent advances in materials and devices. Mol. Syst. Des. Eng. 3, 702–716 (2018).Google Scholar
59.Lozano, G.: The role of metal halide perovskites in next-generation lighting devices. J. Phys. Chem. Lett. 9, 3987–3997 (2018).Google Scholar
60.Smith, M.D. and Karunadasa, H.I.: White-light emission from layered halide perovskites. Acc. Chem. Res. 51, 619–627 (2018).Google Scholar
61.Ahmad, S., George, C., Beesley, D.J., Baumberg, J.J., and De Volder, M.: Photo-rechargeable organo-halide perovskite batteries. Nano Lett. 18, 1856–1862 (2018).Google Scholar
62.Häse, F., Roch, L.M., and Aspuru-Guzik, A.: Next-generation experimentation with self-driving laboratories. TRECHEM. Doi:10.1016/j.trechm.2019.02.007.Google Scholar
63.McLaughlin, J.A., Myers, C.J., Zundel, Z., Mısırlı, G., Zhang, M., Ofiteru, I.D., Goñi-Moreno, A., and Wipat, A.: Synbiohub: a standards-enabled design repository for synthetic biology. ACS Synth. Biol. 7, 682–688 (2018).Google Scholar
64.Grethe, G., Blanke, G., Kraut, H., and Goodman, J.M.: International chemical identifier for reactions (RInChI). J. Cheminform. 10, 22 (2018).Google Scholar
65.Press, W.H., Flannery, B.P., Teukolsky, S.A., and Vetterling, W.T.: Numerical Recipes in C: The Art of Scientific Computing, 2nd ed. (Cambridge University Press, Cambridge, New York, 1992).Google Scholar
66.The precision of the NIMBUS4 is negatively impacted by the operating conditions required for metal halide perovskite synthesis including high temperature and use of GBL as a solvent.Google Scholar
67.JSON: Available at: http://json.org/ (accessed January 11, 2019).Google Scholar
68.Allotrope Foundation Data Standard: Available at: https://www.allotrope.org (accessed January 15, 2019).Google Scholar
69.ChemAxon––Software Solutions and Services for Chemistry & Biology: Available at: https://chemaxon.com/ (accessed 4 January 2019).Google Scholar
70.Landrum, G.: RDKit, Available at: http://www.rdkit.org (accessed 15 January 2019).Google Scholar
71.Wilkinson, M.D., Dumontier, M., Aalbersberg, I.J.J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L.B., Bourne, P.E., Bouwman, J., Brookes, A.J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C.T., Finkers, R., Gonzalez-Beltran, A., Gray, A.J.G., Groth, P., Goble, C., Grethe, J.S., Heringa, J., ‘t Hoen, P.A.C., Hooft, R., Kuhn, T., Kok, R., Kok, J., Lusher, S.J., Martone, M.E., Mons, A., Packer, A.L., Persson, B., Rocca-Serra, P., Roos, M., van Schaik, R., Sansone, S.-A., Schultes, E., Sengstag, T., Slater, T., Strawn, G., Swertz, M.A., Thompson, M., van der Lei, J., van Mulligen, E., Velterop, J., Waagmeester, A., Wittenburg, P., Wolstencroft, K., Zhao, J., and Mons, B.: The FAIR guiding principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016).Google Scholar
72.Citrine Informatics: Available at: https://citrine.io/ (accessed March 22, 2019).Google Scholar
73.McKinney, W.: Data Structures for Statistical Computing in Python. In Proceedings of the 9th Python in Science Conference, edited by S. van der Walt and J. Millman, (Scipy 2010, Austin, TX, 2010), pp. 51–56.Google Scholar
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