Self-driving laboratory for accelerated discovery of thin-film materials.
Benjamin P. MacLeod,Fraser G. L. Parlane,Thomas D. Morrissey,Florian Häse,Loïc M. Roch,Kevan E. Dettelbach,Raphaell Moreira,Lars P. E. Yunker,Michael B. Rooney,Joseph R. Deeth,Veronica Lai,Gordon J. Ng,Henry Situ,Ray H. Zhang,Michael S. Elliott,Ted H. Haley,David J. Dvorak,Alán Aspuru-Guzik,Jason E. Hein,Curtis P. Berlinguette +19 more
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TLDR
In this article, a modular robotic platform driven by a model-based optimization algorithm is used to optimize optical and electronic properties of thin-film materials by modifying the film composition and processing conditions.Abstract:
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.read more
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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,Andrew I. Cooper +13 more
TL;DR: A mobile robot autonomously operates analytical instruments in a wet chemistry laboratory, performing a photocatalyst optimization task much faster than a human would be able to.
Journal ArticleDOI
Big-Data Science in Porous Materials: Materials Genomics and Machine Learning.
TL;DR: In this article, the authors present a review of the application of machine learning techniques to metal-organic frameworks (MOFs) in the field of gas storage and separation, the stability of these materials, their electronic properties, and their synthesis.
Journal ArticleDOI
Quantum Machine Learning in Chemical Compound Space.
TL;DR: The case is made for quantum machine learning: An inductive molecular modeling approach which can be applied to quantum chemistry problems.
Journal ArticleDOI
On-the-fly closed-loop materials discovery via Bayesian active learning.
A. Gilad Kusne,A. Gilad Kusne,Heshan Yu,Changming Wu,Huairuo Zhang,Jason R. Hattrick-Simpers,Brian L. DeCost,Suchismita Sarker,Corey Oses,Cormac Toher,Stefano Curtarolo,Albert V. Davydov,Ritesh Agarwal,Leonid A. Bendersky,Mo Li,Apurva Mehta,Ichiro Takeuchi +16 more
TL;DR: An autonomous materials discovery methodology for functional inorganic compounds is demonstrated which allow scientists to fail smarter, learn faster, and spend less resources in their studies, while simultaneously improving trust in scientific results and machine learning tools.
Journal ArticleDOI
Data-Driven Strategies for Accelerated Materials Design.
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 +10 more
TL;DR: The most recent contributions of this group in this thriving field of machine learning for material science are reviewed, focusing on small molecules as organic electronic materials and crystalline materials and the data-driven approaches they employed to speed up discovery and derive material design strategies.
References
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Organic and solution-processed tandem solar cells with 17.3% efficiency
Lingxian Meng,Yamin Zhang,Xiangjian Wan,Chenxi Li,Xin Zhang,Yanbo Wang,Xin Ke,Zuo Xiao,Liming Ding,Ruoxi Xia,Hin-Lap Yip,Yong Cao,Yongsheng Chen +12 more
TL;DR: In this article, a semi-empirical model analysis and using the tandem cell strategy to overcome the low charge mobility of organic materials, leading to a limit on the active-layer thickness and efficient light absorption was performed.
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A combinatorial approach to materials discovery.
Xiao-Dong Xiang,Xiao-Dong Sun,Gabriel Briceno,Yulin Lou,Kai-An Wang,Hauyee Chang,William G. Wallace-Freedman,Sung-Wei Chen,Peter G. Schultz +8 more
TL;DR: The ability to generate and screen combinatorial libraries of solid-state compounds, when coupled with theory and empirical observations, may significantly increase the rate at which novel electronic, magnetic, and optical materials are discovered and theoretical predictions tested.
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Functional genomic hypothesis generation and experimentation by a robot scientist
Ross D. King,Kenneth E. Whelan,Ffion M. Jones,Philip G. K. Reiser,Christopher H. Bryant,Stephen Muggleton,Douglas B. Kell,Stephen G. Oliver +7 more
TL;DR: A physically implemented robotic system that applies techniques from artificial intelligence to carry out cycles of scientific experimentation and shows that an intelligent experiment selection strategy is competitive with human performance and significantly outperforms, with a cost decrease of 3-fold and 100-fold, both cheapest and random-experiment selection.
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Controlling an organic synthesis robot with machine learning to search for new reactivity
TL;DR: An organic synthesis robot is presented that can perform chemical reactions and analysis faster than they can be performed manually, as well as predict the reactivity of possible reagent combinations after conducting a small number of experiments, thus effectively navigating chemical reaction space.
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Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments
Fang Ren,Logan Ward,Logan Ward,Travis Williams,Kevin J. Laws,Chris Wolverton,Jason R. Hattrick-Simpers,Apurva Mehta +7 more
TL;DR: This work trains a machine learning model on previously reported observations, parameters from physiochemical theories, and makes it synthesis method–dependent to guide high-throughput experiments to find a new system of metallic glasses in the Co-V-Zr ternary, and provides a quantitatively accurate, synthesis method-sensitive predictor for metallic glasses that improves performance with use and thus promises to greatly accelerate discovery of many new metallic glasses.