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Bayesian optimization has emerged as a powerful strategy to accelerate scientific discovery by means of autonomous experimentation.
The design of experiments
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Solar cells. High-efficiency solution-processed perovskite solar cells with millimeter-scale grains
Wanyi Nie, Hsinhan Tsai, Reza Asadpour, Jean-Christophe Blancon, Amanda J. Neukirch, Gautam Gupta, Jared J. Crochet, Manish Chhowalla, Sergei Tretiak, Muhammad A. Alam, Hsing-Lin Wang, and Aditya D. Mohite · 2015
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Variational Dropout and the Local Reparameterization Trick
Diederik P. Kingma, Tim Salimans, and Max Welling · 2015
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DOE simplified: practical tools for effective experimentation
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Taking the Human Out of the Loop: A Review of Bayesian Optimization
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Autonomy in materials research: a case study in carbon nanotube growth
Pavel Nikolaev, Daylond Hooper, Frederick Webber, Rahul Rao, Kevin Decker, Michael Krein, Jason Poleski, Rick Barto, and Benji Maruyama · 2016
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A Bayesian approach to calibrating high-throughput virtual screening results and application to organic photovoltaic materials
Edward O. Pyzer-Knapp, Gregor N. Simm, and Alán Aspuru Guzik · 2016
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Intriguing Optoelectronic Properties of Metal Halide Perovskites
Joseph S. Manser, Jeffrey A. Christians, and Prashant V. Kamat · 2016
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Tensorflow: A system for large-scale machine learning
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, and others · 2016
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Prediction of Organic Reaction Outcomes Using Machine Learning
Connor W. Coley, Regina Barzilay, Tommi S. Jaakkola, William H. Green, and Klavs F. Jensen · 2017
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Optimizing Chemical Reactions with Deep Reinforcement Learning
Zhenpeng Zhou, Xiaocheng Li, and Richard N. Zare · 2017
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Deploying a top-100 supercomputer for large parallel workloads: The niagara supercomputer
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Autonomous Discovery in the Chemical Sciences Part I: Progress
Connor W. Coley, Natalie S. Eyke, and Klavs F. Jensen · 2020
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Autonomous Discovery in the Chemical Sciences Part II: Outlook
Connor W. Coley, Natalie S. Eyke, and Klavs F. Jensen · 2020
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Beyond Ternary OPV: High‐Throughput Experimentation and Self‐Driving Laboratories Optimize Multicomponent Systems
Stefan Langner, Florian Häse, José Darío Perea, Tobias Stubhan, Jens Hauch, Loïc M. Roch, Thomas Heumueller, Alán Aspuru‐Guzik, and Christoph J. Brabec · 2020
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Design Principles and Top Non-Fullerene Acceptor Candidates for Organic Photovoltaics
Steven A. Lopez, Benjamin Sanchez-Lengeling, Julio de Goes Soares, and Alán Aspuru-Guzik · 2017
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A hybrid organic-inorganic perovskite dataset
Chiho Kim, Tran Doan Huan, Sridevi Krishnan, and Rampi Ramprasad · 2017
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GPflow: A Gaussian Process Library using TensorFlow
Alexander G. de G. Matthews, Mark van der Wilk, Tom Nickson, Keisuke Fujii, Alexis Boukouvalas, Pablo Le{\’o}n-Villagr{\’a}, Zoubin Ghahramani, and James Hensman · 2017
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A hybrid organic-inorganic perovskite dataset
Chiho Kim, Tran Doan Huan, Sridevi Krishnan, and Rampi Ramprasad · 2017
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Machine learning for molecular and materials science
Keith T. Butler, Daniel W. Davies, Hugh Cartwright, Olexandr Isayev, and Aron Walsh · 2018
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“Found in Translation”: predicting outcomes of complex organic chemistry reactions using neural sequence-to-sequence models
Philippe Schwaller, Théophile Gaudin, Dávid Lányi, Costas Bekas, and Teodoro Laino · 2018
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Predicting reaction performance in C–N cross-coupling using machine learning
Derek T. Ahneman, Jesús G. Estrada, Shishi Lin, Spencer D. Dreher, and Abigail G. Doyle · 2018
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Florian Häse, Loïc M. Roch, and Alán Aspuru-Guzik · 2020
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Materials Acceleration Platforms: On the way to autonomous experimentation
Martha M. Flores-Leonar, Luis M. Mejía-Mendoza, Andrés Aguilar-Granda, Benjamin Sanchez-Lengeling, Hermann Tribukait, Carlos Amador-Bedolla, and Alán Aspuru-Guzik · 2020
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A Bayesian experimental autonomous researcher for mechanical design
Aldair E. Gongora, Bowen Xu, Wyatt Perry, Chika Okoye, Patrick Riley, Kristofer G. Reyes, Elise F. Morgan, and Keith A. Brown · 2020
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A mobile robotic chemist
Benjamin Burger, Phillip M. Maffettone, Vladimir V. Gusev, Catherine M. Aitchison, Yang Bai, Xiaoyan Wang, Xiaobo Li, Ben M. Alston, Buyi Li, Rob Clowes, Nicola Rankin, Brandon Harris, Reiner Sebastian Sprick, and Andrew I. Cooper · 2020
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Self-driving laboratory for accelerated discovery of thin-film materials
B. P. MacLeod, F. G. L. Parlane, T. D. Morrissey, F. Häse, L. M. Roch, K. E. Dettelbach, R. Moreira, L. P. E. Yunker, M. B. Rooney, J. R. Deeth, V. Lai, G. J. Ng, H. Situ, R. H. Zhang, M. S. Elliott, T. H. Haley, D. J. Dvorak, A. Aspuru-Guzik, J. E. Hein, and C. P. Berlinguette · 2020
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Summit: Benchmarking Machine Learning Methods for Reaction Optimisation
Kobi Felton, Jan Rittig, and Alexei Lapkin · 2020
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Olympus: a benchmarking framework for noisy optimization and experiment planning
Florian Häse, Matteo Aldeghi, Riley J. Hickman, Loïc M. Roch, Melodie Christensen, Elena Liles, Jason E. Hein, and Alán Aspuru-Guzik · 2020
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Benchmarking the acceleration of materials discovery by sequential learning
Santosh S Suram, Brian Rohr, Helge S Stein, Dan Guevarra, Yu Wang, Joel A Haber, Muratahan Aykol, and John Gregoire · 2020
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sMF-BO-2CoGP: A Sequential Multi-Fidelity Constrained Bayesian Optimization Framework for Design Applications
Anh Tran, Tim Wildey, and Scott McCann · 2020
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Multi-fidelity machine-learning with uncertainty quantification and Bayesian optimization for materials design: Application to ternary random alloys
Anh Tran, Julien Tranchida, Tim Wildey, and Aidan P. Thompson · 2020
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Cost-effective materials discovery: Bayesian optimization across multiple information sources
Henry C. Herbol, Matthias Poloczek, and Paulette Clancy · 2020
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Metal-Free Hybrid Organic–Inorganic Perovskites for Photovoltaics
Tianmin Wu, Xian Chen, and Jian Wang · 2020
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A review on fundamentals for designing oxygen evolution electrocatalysts
Jiajia Song, Chao Wei, Zhen-Feng Huang, Chuntai Liu, Lin Zeng, Xin Wang, and Zhichuan J. Xu · 2020
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