Accelerating the discovery of materials for clean energy in the era of smart automation

Abstract

The discovery and development of novel materials in the field of energy are essential to accelerate the transition to a low-carbon economy. Bringing recent technological innovations in automation, robotics and computer science together with current approaches in chemistry, materials synthesis and characterization will act as a catalyst for revolutionizing traditional research and development in both industry and academia. This Perspective provides a vision for an integrated artificial intelligence approach towards autonomous materials discovery, which, in our opinion, will emerge within the next 5 to 10 years. The approach we discuss requires the integration of the following tools, which have already seen substantial development to date: high-throughput virtual screening, automated synthesis planning, automated laboratories and machine learning algorithms. In addition to reducing the time to deployment of new materials by an order of magnitude, this integrated approach is expected to lower the cost associated with the initial discovery. Thus, the price of the final products (for example, solar panels, batteries and electric vehicles) will also decrease. This in turn will enable industries and governments to meet more ambitious targets in terms of reducing greenhouse gas emissions at a faster pace.

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Fig. 1: Examples of clean energy generation and storage technologies.
Fig. 2: Workflow of a closed-loop approach to autonomous materials discovery.
Fig. 3: State-of-the-art virtual screening: from human intuition to experimental verification.
Fig. 4: General concept for the automated generation of retrosynthesis trees.
Fig. 5: High-throughput characterization of materials.
Fig. 6: Autonomous experimentation procedures.

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Acknowledgements

D.P.T. and A.A.-G. were supported by the National Science Foundation (NSF) Science and Technology Center for Integrated Quantum Materials, CIQM (Grant No. NSF-DMR-1231319). L.M.R. and A.A.-G. acknowledge support from Anders Frøseth. S.K.S., C.K. and A.A.-G. were supported by the NSF (Grant No. CHE-1464862). D.S. and A.A.-G. acknowledge the Harvard Climate Solution Fund. J.H.M. and K.P. were supported by the Materials Project Center (Grant No. EDCBEE) through the US Department of Energy, Office of Basic Energy Sciences, Materials Sciences and Engineering Division (Contract No. DE-AC02 05CH11231). S.D. acknowledges support from the Center for the Next Generation of Materials by Design, an Energy Frontier Research Center funded by the US Department of Energy, Office of Science, Basic Energy Sciences (Contract No. DE-AC36-08GO28308). A.A.-G. acknowledges support from the Canadian Institute for Advanced Research (Grant No. BSE-ASPU-162439-CF).

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D.P.T., L.M.R., S.K.S., C.K. and D.S. researched data and wrote the article. All authors contributed to the discussion of content and assisted in editing the manuscript before submission.

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Awesome Pipeline: https://github.com/pditommaso/awesome-pipelineChemPlanner: http://www.chemplanner.com/Clean Energy Materials Innovation Challenge: http://mission-innovation.net/our- work/innovation-challenges/clean-energy-materials-challenge/Climeworks: http://www.climeworks.com/Dial-a-Molecule: http://generic.wordpress.soton.ac.uk/dial-a-molecule/InfoChem: http://www.infochem.de/products/databases/spresi.shtmlInorganic Crystal Structure Database: http://www2.fiz-karlsruhe.de/icsd_home.htmlNIST high-throughput screening tool: https://www.nist.gov/laboratories/tools-instruments/high-throughput-combinatorial-screening-tool-characterization-thinRDKit: Open-source cheminformatics: http://www.rdkit.org Reaxys: https://www.reaxys.com/UniEnergy Technologies: http://www.uetechnologies.com/

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Tabor, D.P., Roch, L.M., Saikin, S.K. et al. Accelerating the discovery of materials for clean energy in the era of smart automation. Nat Rev Mater 3, 5–20 (2018). https://doi.org/10.1038/s41578-018-0005-z

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