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 opensource 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.
Similar content being viewed by others
References
NSF CHEWorkshop: Framing the Role of Big Data andModern Data Science in Chemistry. Available at: https://www.nsf.gov/mps/che/workshops/data_ chemistry_workshop_report_03262018.pdf (accessed December 21, 2018).
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).
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).
A.B. Henson, P.S. Gromski, and L. Cronin: Designing algorithms to aid discovery by chemical robots. ACS Cent. Sci. 4, 793–804 (2018).
D.P. Tabor, L.M. Roch, S.K. Saikin, C. Kreisbeck, D. Sheberla, J.H. Montoya, S. Dwaraknath, M. Aykol, C. Ortiz, H. Tribukait, C. Amador-Bedolla, C.J. Brabec, B. Maruyama, K.A. Persson, and A. Aspuru-Guzik: Accelerating the discovery of materials for clean energy in the era of smart automation. Nat. Rev. Mater. 3, 5–20 (2018).
J.-P. Correa-Baena, K. Hippalgaonkar, J. van Duren, S. Jaffer, V.R. Chandrasekhar, V. Stevanovic, C. Wadia, S. Guha, and T. Buonassisi: Accelerating materials development via automation, machine learning, and high-performance computing. Joule 2, 1410–1420 (2018).
X.-D. Xiang, X. Sun, G. Briceño, Y. Lou, K.-A. Wang, H. Chang, W.G. Wallace-Freedman, S.-W. Chen, and P.G. Schultz: A combinatorial approach to materials discovery. Science 268, 1738–1740 (1995).
P.G. Schultz and X.-D. Xiang: Combinatorial approaches to materials science. Curr. Opin. Solid State Mater. Sci. 3, 153–158 (1998).
H. Koinuma and I. Takeuchi: Combinatorial solid-state chemistry of inorganic materials. Nat. Mater. 3, 429–438 (2004).
I. Takeuchi, R.B. van Dover, and H. Koinuma: Combinatorial synthesis and evaluation of functional inorganic materials using thin-film techniques. MRS Bull. 27, 301–308 (2002).
Z.H. Barber and M.G. Blamire: High throughput thin film materials science. Mater. Sci. Technol. 24, 757–770 (2008).
S.I. Woo, K.W. Kim, H.Y. Cho, K.S. Oh, M.K. Jeon, N.H. Tarte, T.S. Kim, and A. Mahmood: Current status of combinatorial and high-throughput methods for discovering new materials and catalysts. QSAR Comb. Sci. 24, 138–154 (2005).
M.L. Green, I. Takeuchi, and J.R. Hattrick-Simpers: Applications of high throughput (combinatorial) methodologies to electronic, magnetic, optical, and energy-related materials. J. Appl. Phys. 113, 231101 (2013).
L.A. Baumes, P. Serna, and A. Corma: 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).
R. Potyrailo, K. Rajan, K. Stoewe, I. Takeuchi, B. Chisholm, and H. Lam: Combinatorial and high-throughput screening of materials libraries: review of state of the art. ACS Comb. Sci. 13, 579–633 (2011).
M. Shevlin: Practical high-throughput experimentation for chemists. ACS Med. Chem. Lett. 8, 601–607 (2017).
W.F. Maier, K. Stöwe, and S. Sieg: Combinatorial and high-throughput materials science. Angew. Chem. Int. Ed Engl. 46, 6016–6067 (2007).
K.T. Butler, D.W. Davies, H. Cartwright, O. Isayev, and A. Walsh: Machine learning for molecular and materials science. Nature 559, 547–555 (2018).
B. Sanchez-Lengeling and A. Aspuru-Guzik: Inverse molecular design using machine learning: generative models for matter engineering. Science 361, 360–365 (2018).
P. Raccuglia, K.C. Elbert, P.D.F. Adler, C. Falk, M.B. Wenny, A. Mollo, M. Zeller, S.A. Friedler, J. Schrier, and A.J. Norquist: Machine-learning-assisted materials discovery using failed experiments. Nature 533, 73–76 (2016).
D.T. Ahneman, J.G. Estrada, S. Lin, S.D. Dreher, and A.G. Doyle: Predicting reaction performance in C-N cross-coupling using machine learning. Science 360, 186–190 (2018).
S. Lin, S. Dikler, W.D. Blincoe, R.D. Ferguson, R.P. Sheridan, Z. Peng, D.V. Conway, K. Zawatzky, H. Wang, T. Cernak, I.W. Davies, D.A. DiRocco, H. Sheng, C.J. Welch, and S.D. Dreher: Mapping the dark space of chemical reactions with extended nanomole synthesis and MALDI-TOF MS. Science. 361, eaar6236 (2018).
R.J. Xu, J.H. Olshansky, P.D.F. Adler, Y. Huang, M.D. Smith, M. Zeller, J. Schrier, and A.J. Norquist: Understanding structural adaptability: a reactant informatics approach to experiment design. Mol. Syst. Des. Eng. 3, 473–484 (2018).
V. Duros, J. Grizou, W. Xuan, Z. Hosni, D.-L. Long, H.N. Miras, and L. Cronin: Human versus robots in the discovery and crystallization of gigantic polyoxometalates. Angew. Chem. Int. Ed. Engl. 56, 10815–10820 (2017).
Z. Zhou, X. Li, and R.N. Zare: Optimizing chemical reactions with deep reinforcement learning. ACS Cent. Sci. 3, 1337–1344 (2017).
A.-C. Bédard, A. Adamo, K.C. Aroh, M.G. Russell, A.A. Bedermann, J. Torosian, B. Yue, K.F. Jensen, and T.F. Jamison: Reconfigurable system for automated optimization of diverse chemical reactions. Science 361, 1220–1225 (2018).
P. Nikolaev, D. Hooper, F. Webber, R. Rao, K. Decker, M. Krein, J. Poleski, R. Barto, and B. Maruyama: Autonomy in materials research: a case study in carbon nanotube growth. npj Comput. Mater. 2, 16031 (2016).
A.G. Kusne, T. Gao, A. Mehta, L. Ke, M.C. Nguyen, K.-M. Ho, V. Antropov, C.-Z. Wang, M.J. Kramer, C. Long, and I. Takeuchi: On-the-fly machine-learning for high-throughput experiments: search for rare-earth-free permanent magnets. Sci. Rep. 4, 6367 (2014).
B. Celse, S. Rebours, F. Gay, P. Coste, L. Bourgeois, O. Zammit, and V. Lebacque: 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).
J. Bai, Y. Xue, J. Bjorck, R. Le Bras, B. Rappazzo, R. Bernstein, S.K. Suram, R.B. Van Dover, J.M. Gregoire, and C.P. Gomes: Phase mapper: accelerating materials discovery with AI. AIMag 39, 15 (2018).
B. Cao, L.A. Adutwum, A.O. Oliynyk, E.J. Luber, B.C. Olsen, A. Mar, and J.M. Buriak: How To optimize materials and devices via design of experiments and machine learning: demonstration using organic photovoltaics. ACS Nano 12, 7434–7444 (2018).
V. Stanev, C. Oses, A.G. Kusne, E. Rodriguez, J. Paglione, S. Curtarolo, and I. Takeuchi: Machine learning modeling of superconducting critical temperature. npj Comput. Mater. 4, 1 (2018).
Q. Yan, J. Yu, S.K. Suram, L. Zhou, A. Shinde, P.F. Newhouse, W. Chen, G. Li, K.A. Persson, J.M. Gregoire, and J.B. Neaton: Solar fuels photoanode materials discovery by integrating high-throughput theory and experiment. Proc. Natl. Acad. Sci. USA 114, 3040–3043 (2017).
F. Ren, L. Ward, T. Williams, K.J. Laws, C. Wolverton, J. Hattrick-Simpers, and A. Mehta: Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments. Sci. Adv. 4, eaaq1566 (2018).
A. Shinde, S.K. Suram, Q. Yan, L. Zhou, A.K. Singh, J. Yu, K.A. Persson, J.B. Neaton, and J.M. Gregoire: 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).
M.L. Green, C.L. Choi, J.R. Hattrick-Simpers, A.M. Joshi, I. Takeuchi, S.C. Barron, E. Campo, T. Chiang, S. Empedocles, J.M. Gregoire, A.G. Kusne, J. Martin, A. Mehta, K. Persson, Z. Trautt, J. Van Duren, and A. Zakutayev: Fulfilling the promise of the materials genome initiative with high-throughput experimental methodologies. Appl. Phys. Rev. 4, 011105 (2017).
A. Zakutayev, N. Wunder, M. Schwarting, J.D. Perkins, R. White, K. Munch, W. Tumas, and C. Phillips: An open experimental database for exploring inorganic materials. Sci. Data 5, 180053 (2018).
J. Li, Y. Lu, Y. Xu, C. Liu, Y. Tu, S. Ye, H. Liu, Y. Xie, H. Qian, and X. Zhu: AIR-Chem: authentic intelligent robotics for chemistry. J. Phys. Chem. A 122, 9142–9148 (2018).
N. Adams and U.S. Schubert: From data to knowledge: chemical data management, data mining, and modeling in polymer science. J. Comb. Chem. 6, 12–23 (2004).
N. Adams and U.S. Schubert: Software solutions for combinatorial and high-throughput materials and polymer research. Macromol. Rapid Commun. 25, 48–58 (2004).
L.M. Roch, F. Häse, C. Kreisbeck, T. Tamayo-Mendoza, L.P.E. Yunker, J.E. Hein, and A. Aspuru-Guzik: ChemOS: orchestrating autonomous experimentation. Sci Robot. 3, eaat5559 (2018).
J. Hachmann, M.A.F. Afzal, M. Haghighatlari, and Y. Pal: 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).
L.A. Baumes, S. Jimenez, and A. Corma: hITeQ: a new workflow-based computing environment for streamlining discovery. Application in materials science. Catal. Today 159, 126–137 (2011).
K. Tran, A. Palizhati, S. Back, and Z.W. Ulissi: Dynamic workflows for routine materials discovery in surface science. J. Chem. Inf. Model. 58, 2392–2400 (2018).
M. Bates, A.J. Berliner, J. Lachoff, P.R. Jaschke, and E.S. Groban: Wet Lab accelerator: a web-based application democratizing laboratory automation for synthetic biology. ACS Synth. Biol. 6, 167–171 (2017).
Autoprotocol: Available at: http://autoprotocol.org/ (accessed January 8, 2019).
G. Linshiz, N. Stawski, S. Poust, C. Bi, J.D. Keasling, and N.J. Hillson: PaR-PaR laboratory automation platform. ACS Synth. Biol. 2, 216–222 (2013).
E. Whitehead, F. Rudolf, H.-M. Kaltenbach, and J. Stelling: Automated planning enables complex protocols on liquid-handling robots. ACS Synth. Biol. 7, 922–932 (2018).
B. Keller, J. Vrana, A. Miller, G. Newman, and E. Klavins: Aquarium: The Laboratory Operating System (Version v2.5.0). Zenodo. (2019).
Emerald Cloud Lab: Available at: https://www.emeraldcloudlab.com/ (accessed January 11, 2019).
B. Miles and P.L. Lee: Achieving reproducibility and closed-loop automation in biological experimentation with an IoT-enabled lab of the future. SLAS Technol. 23, 432–439 (2018).
Transcriptic: Powering On-Demand Biology Transcriptic. Available at: https://transcriptic.com/ (accessed January 15, 2019).
D.B. Mitzi: Synthesis, Structure, and Properties of Organic-Inorganic Perovskites and Related Materials In Progress in Inorganic Chemistry, edited by K.D. Karlin (John Wiley & Sons, Inc., 9, Hoboken, NJ, USA, 1999), pp. 1–121.
M.D. Smith, E.J. Crace, A. Jaffe, and H.I. Karunadasa: The diversity of layered halide perovskites. Annu. Rev. Mater. Res. 48, 111–136 (2018).
S. Li, C. Zhang, J.-J. Song, X. Xie, J.-Q. Meng, and S. Xu: Metal halide perovskite single crystals: from growth process to application. Crystals. (Basel) 8, 220 (2018).
H.J. Snaith: Present status and future prospects of perovskite photovoltaics. Nat. Mater. 17, 372–376 (2018).
M.I.H. Ansari, A. Qurashi, and M.K. Nazeeruddin: Frontiers, opportunities, and challenges in perovskite solar cells: a critical review. J. Photochem. Photobiol. C: Photochem. Rev. 35, 1–24 (2018).
F. Yao, P. Gui, Q. Zhang, and Q. Lin: Molecular engineering of perovskite photodetectors: recent advances in materials and devices. Mol. Syst. Des. Eng. 3, 702–716 (2018).
G. Lozano: The role of metal halide perovskites in next-generation lighting devices. J. Phys. Chem. Lett. 9, 3987–3997 (2018).
M.D. Smith and H.I. Karunadasa: White-light emission from layered halide perovskites. Acc. Chem. Res. 51, 619–627 (2018).
S. Ahmad, C. George, D.J. Beesley, J.J. Baumberg, and M. De Volder: Photo-rechargeable organo-halide perovskite batteries. Nano Lett. 18, 1856–1862 (2018).
F. Häse, L.M. Roch, and A. Aspuru-Guzik: Next-generation experimentation with self-driving laboratories. TRECHEM. Doi:10.1016/j.trechm.2019.02.007.
J.A. McLaughlin, C.J. Myers, Z. Zundel, G. Misirli, M. Zhang, I.D. Ofiteru, A. Goñi-Moreno, and A. Wipat: Synbiohub: a standards-enabled design repository for synthetic biology. ACS Synth. Biol. 7, 682–688 (2018).
G. Grethe, G. Blanke, H. Kraut, and J.M. Goodman: International chemical identifier for reactions (RInChI). J. Cheminform. 10, 22 (2018).
W.H. Press, B.P. Flannery, S.A. Teukolsky, and W.T. Vetterling: Numerical Recipes in C: The Art of Scientific Computing, 2nd ed. (Cambridge University Press, Cambridge, New York, 1992).
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.
JSON: Available at: http://json.org/ (accessed January 11, 2019).
Allotrope Foundation Data Standard: Available at: https://www.allotrope.org (accessed January 15, 2019).
ChemAxon––Software Solutions and Services for Chemistry & Biology: Available at: https://chemaxon.com/ (accessed 4 January 2019).
G. Landrum: RDKit, Available at: http://www.rdkit.org (accessed 15 January 2019).
M.D. Wilkinson, M. Dumontier, I.J.J. Aalbersberg, G. Appleton, M. Axton, A. Baak, N. Blomberg, J.-W. Boiten, L.B. da Silva Santos, P.E. Bourne, J. Bouwman, A.J. Brookes, T. Clark, M. Crosas, I. Dillo, O. Dumon, S. Edmunds, C.T. Evelo, R. Finkers, A. Gonzalez-Beltran, A.J.G. Gray, P. Groth, C. Goble, J.S. Grethe, J. Heringa, P.A.C. ‘t Hoen, R. Hooft, T. Kuhn, R. Kok, J. Kok, S.J. Lusher, M.E. Martone, A. Mons, A.L. Packer, B. Persson, P. Rocca-Serra, M. Roos, R. van Schaik, S.-A. Sansone, E. Schultes, T. Sengstag, T. Slater, G. Strawn, M.A. Swertz, M. Thompson, J. van der Lei, E. van Mulligen, J. Velterop, A. Waagmeester, P. Wittenburg, K. Wolstencroft, J. Zhao, and B. Mons: The FAIR guiding principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016).
Citrine Informatics: Available at: https://citrine.io/ (accessed March 22, 2019).
W. McKinney: 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.
Acknowledgments
We thank Alex Cristofaro (MIT Broad Institute) and Scott Novotney (Two-Six Labs) for helpful feedback in the development of the ESCALATE state space and file reporting mechanisms. This material is based upon the work supported by the Defense Advanced Research Projects Agency (DARPA) under Contract No. HR001118C0036. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of DARPA. This material is DISTRIBUTION A. Approved for public release: distribution unlimited. Work at the Molecular Foundry was supported by the Office of Science, Office of Basic Energy Sciences, of the US Department of Energy under Contract No. DE-AC02-05CH11231. J.S. acknowledges the Henry Dreyfus Teacher-Scholar Award (TH-14-010).
Author information
Authors and Affiliations
Corresponding author
Supplementary material
43579_2019_9030846_MOESM1_ESM.pdf
Experiment Specification, Capture and Laboratory Automation Technology (ESCALATE): A software pipeline for automated chemical experimentation and data management
Supplementary material
Supplementary material
The supplementary material for this article can be found at https://doi.org/10.1557/mrc.2019.72.
Rights and permissions
About this article
Cite this article
Pendleton, I.M., Cattabriga, G., Li, Z. et al. Experiment Specification, Capture and Laboratory Automation Technology (ESCALATE): a software pipeline for automated chemical experimentation and data management. MRS Communications 9, 846–859 (2019). https://doi.org/10.1557/mrc.2019.72
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1557/mrc.2019.72