Literature DB >> 32049386

Beyond Ternary OPV: High-Throughput Experimentation and Self-Driving Laboratories Optimize Multicomponent Systems.

Stefan Langner1, Florian Häse2,3,4,5, José Darío Perea1, Tobias Stubhan6, Jens Hauch6, Loïc M Roch2,3,4,5, Thomas Heumueller1, Alán Aspuru-Guzik3,4,5,7, Christoph J Brabec1,2.   

Abstract

Fundamental advances to increase the efficiency as well as stability of organic photovoltaics (OPVs) are achieved by designing ternary blends, which represents a clear trend toward multicomponent active layer blends. The development of high-throughput and autonomous experimentation methods is reported for the effective optimization of multicomponent polymer blends for OPVs. A method for automated film formation enabling the fabrication of up to 6048 films per day is introduced. Equipping this automated experimentation platform with a Bayesian optimization, a self-driving laboratory is constructed that autonomously evaluates measurements to design and execute the next experiments. To demonstrate the potential of these methods, a 4D parameter space of quaternary OPV blends is mapped and optimized for photostability. While with conventional approaches, roughly 100 mg of material would be necessary, the robot-based platform can screen 2000 combinations with less than 10 mg, and machine-learning-enabled autonomous experimentation identifies stable compositions with less than 1 mg.
© 2020 The Authors. Published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Keywords:  high-throughput experimentation; machine learning; organic photovoltaics; photostability; solar energy

Year:  2020        PMID: 32049386     DOI: 10.1002/adma.201907801

Source DB:  PubMed          Journal:  Adv Mater        ISSN: 0935-9648            Impact factor:   30.849


  11 in total

1.  Autonomous Chemical Experiments: Challenges and Perspectives on Establishing a Self-Driving Lab.

Authors:  Martin Seifrid; Robert Pollice; Andrés Aguilar-Granda; Zamyla Morgan Chan; Kazuhiro Hotta; Cher Tian Ser; Jenya Vestfrid; Tony C Wu; Alán Aspuru-Guzik
Journal:  Acc Chem Res       Date:  2022-08-10       Impact factor: 24.466

Review 2.  Applications and Techniques for Fast Machine Learning in Science.

Authors:  Allison McCarn Deiana; Nhan Tran; Joshua Agar; Michaela Blott; Giuseppe Di Guglielmo; Javier Duarte; Philip Harris; Scott Hauck; Mia Liu; Mark S Neubauer; Jennifer Ngadiuba; Seda Ogrenci-Memik; Maurizio Pierini; Thea Aarrestad; Steffen Bähr; Jürgen Becker; Anne-Sophie Berthold; Richard J Bonventre; Tomás E Müller Bravo; Markus Diefenthaler; Zhen Dong; Nick Fritzsche; Amir Gholami; Ekaterina Govorkova; Dongning Guo; Kyle J Hazelwood; Christian Herwig; Babar Khan; Sehoon Kim; Thomas Klijnsma; Yaling Liu; Kin Ho Lo; Tri Nguyen; Gianantonio Pezzullo; Seyedramin Rasoulinezhad; Ryan A Rivera; Kate Scholberg; Justin Selig; Sougata Sen; Dmitri Strukov; William Tang; Savannah Thais; Kai Lukas Unger; Ricardo Vilalta; Belina von Krosigk; Shen Wang; Thomas K Warburton
Journal:  Front Big Data       Date:  2022-04-12

3.  Data-Driven Strategies for Accelerated Materials Design.

Authors:  Robert Pollice; Gabriel Dos Passos Gomes; Matteo Aldeghi; Riley J Hickman; Mario Krenn; Cyrille Lavigne; Michael Lindner-D'Addario; AkshatKumar Nigam; Cher Tian Ser; Zhenpeng Yao; Alán Aspuru-Guzik
Journal:  Acc Chem Res       Date:  2021-02-02       Impact factor: 22.384

Review 4.  The role of machine learning in clinical research: transforming the future of evidence generation.

Authors:  E Hope Weissler; Tristan Naumann; Tomas Andersson; Rajesh Ranganath; Olivier Elemento; Yuan Luo; Daniel F Freitag; James Benoit; Michael C Hughes; Faisal Khan; Paul Slater; Khader Shameer; Matthew Roe; Emmette Hutchison; Scott H Kollins; Uli Broedl; Zhaoling Meng; Jennifer L Wong; Lesley Curtis; Erich Huang; Marzyeh Ghassemi
Journal:  Trials       Date:  2021-08-16       Impact factor: 2.279

5.  Accelerated AI development for autonomous materials synthesis in flow.

Authors:  Robert W Epps; Amanda A Volk; Kristofer G Reyes; Milad Abolhasani
Journal:  Chem Sci       Date:  2021-03-09       Impact factor: 9.825

6.  Learning Design Rules for Selective Oxidation Catalysts from High-Throughput Experimentation and Artificial Intelligence.

Authors:  Lucas Foppa; Christopher Sutton; Luca M Ghiringhelli; Sandip De; Patricia Löser; Stephan A Schunk; Ansgar Schäfer; Matthias Scheffler
Journal:  ACS Catal       Date:  2022-01-31       Impact factor: 13.084

Review 7.  From Platform to Knowledge Graph: Evolution of Laboratory Automation.

Authors:  Jiaru Bai; Liwei Cao; Sebastian Mosbach; Jethro Akroyd; Alexei A Lapkin; Markus Kraft
Journal:  JACS Au       Date:  2022-01-10

8.  A self-driving laboratory advances the Pareto front for material properties.

Authors:  Benjamin P MacLeod; Fraser G L Parlane; Connor C Rupnow; Kevan E Dettelbach; Michael S Elliott; Thomas D Morrissey; Ted H Haley; Oleksii Proskurin; Michael B Rooney; Nina Taherimakhsousi; David J Dvorak; Hsi N Chiu; Christopher E B Waizenegger; Karry Ocean; Mehrdad Mokhtari; Curtis P Berlinguette
Journal:  Nat Commun       Date:  2022-02-22       Impact factor: 17.694

Review 9.  Progress and prospects for accelerating materials science with automated and autonomous workflows.

Authors:  Helge S Stein; John M Gregoire
Journal:  Chem Sci       Date:  2019-09-20       Impact factor: 9.825

Review 10.  Self-Driving Laboratories for Development of New Functional Materials and Optimizing Known Reactions.

Authors:  Mikhail A Soldatov; Vera V Butova; Danil Pashkov; Maria A Butakova; Pavel V Medvedev; Andrey V Chernov; Alexander V Soldatov
Journal:  Nanomaterials (Basel)       Date:  2021-03-02       Impact factor: 5.076

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