Self-driving laboratory for accelerated discovery of thin-film materials.

Autor:	MacLeod BP; Department of Chemistry, The University of British Columbia, Vancouver, British Columbia, Canada.; Stewart Blusson Quantum Matter Institute, The University of British Columbia, Vancouver, British Columbia, Canada., Parlane FGL; Department of Chemistry, The University of British Columbia, Vancouver, British Columbia, Canada.; Stewart Blusson Quantum Matter Institute, The University of British Columbia, Vancouver, British Columbia, Canada., Morrissey TD; Department of Chemistry, The University of British Columbia, Vancouver, British Columbia, Canada.; Stewart Blusson Quantum Matter Institute, The University of British Columbia, Vancouver, British Columbia, Canada., Häse F; Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA.; Department of Chemistry, University of Toronto, Toronto, Ontario, Canada.; Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.; Vector Institute for Artificial Intelligence, MaRS Centre, Toronto, Ontario, Canada., Roch LM; Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA.; Department of Chemistry, University of Toronto, Toronto, Ontario, Canada.; Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.; Vector Institute for Artificial Intelligence, MaRS Centre, Toronto, Ontario, Canada., Dettelbach KE; Department of Chemistry, The University of British Columbia, Vancouver, British Columbia, Canada., Moreira R; Department of Chemistry, The University of British Columbia, Vancouver, British Columbia, Canada., Yunker LPE; Department of Chemistry, The University of British Columbia, Vancouver, British Columbia, Canada., Rooney MB; Department of Chemistry, The University of British Columbia, Vancouver, British Columbia, Canada., Deeth JR; Department of Chemistry, The University of British Columbia, Vancouver, British Columbia, Canada., Lai V; Department of Chemistry, The University of British Columbia, Vancouver, British Columbia, Canada., Ng GJ; Department of Chemistry, The University of British Columbia, Vancouver, British Columbia, Canada., Situ H; Department of Chemistry, The University of British Columbia, Vancouver, British Columbia, Canada., Zhang RH; Department of Chemistry, The University of British Columbia, Vancouver, British Columbia, Canada., Elliott MS; Department of Chemistry, The University of British Columbia, Vancouver, British Columbia, Canada., Haley TH; Department of Chemistry, The University of British Columbia, Vancouver, British Columbia, Canada., Dvorak DJ; Stewart Blusson Quantum Matter Institute, The University of British Columbia, Vancouver, British Columbia, Canada., Aspuru-Guzik A; Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA.; Department of Chemistry, University of Toronto, Toronto, Ontario, Canada.; Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.; Vector Institute for Artificial Intelligence, MaRS Centre, Toronto, Ontario, Canada.; Canadian Institute for Advanced Research (CIFAR), MaRS Centre, Toronto, Ontario, Canada., Hein JE; Department of Chemistry, The University of British Columbia, Vancouver, British Columbia, Canada., Berlinguette CP; Department of Chemistry, The University of British Columbia, Vancouver, British Columbia, Canada.; Stewart Blusson Quantum Matter Institute, The University of British Columbia, Vancouver, British Columbia, Canada.; Canadian Institute for Advanced Research (CIFAR), MaRS Centre, Toronto, Ontario, Canada.; Department of Chemical and Biological Engineering, The University of British Columbia, Vancouver, British Columbia, Canada.
Jazyk:	angličtina
Zdroj:	Science advances [Sci Adv] 2020 May 13; Vol. 6 (20), pp. eaaz8867. Date of Electronic Publication: 2020 May 13 (Print Publication: 2020).
DOI:	10.1126/sciadv.aaz8867
Abstrakt:	Discovering and optimizing commercially viable materials for clean energy applications typically takes more than a decade. Self-driving laboratories that iteratively design, execute, and learn from materials science experiments in a fully autonomous loop present an opportunity to accelerate this research process. We report here a modular robotic platform driven by a model-based optimization algorithm capable of autonomously optimizing the optical and electronic properties of thin-film materials by modifying the film composition and processing conditions. We demonstrate the power of this platform by using it to maximize the hole mobility of organic hole transport materials commonly used in perovskite solar cells and consumer electronics. This demonstration highlights the possibilities of using autonomous laboratories to discover organic and inorganic materials relevant to materials sciences and clean energy technologies. (Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).)
Databáze:	MEDLINE
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