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Next-Generation Experimentation with Self-Driving Laboratories
Florian Häse, Loïc M. Roch, Alán Aspuru-Guzik Trends in Chemistry Volume 1, Issue 3, Pages (June 2019) DOI: /j.trechm Copyright © 2019 Elsevier Inc. Terms and Conditions
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Figure 1 Experiment Planning Strategies. The experiment planning procedure aims to identify experimental conditions for which the resulting properties satisfy desired targets. (A) High-throughput screening iterations consist of scanning a set of a priori selected conditions without refinements. (B) Optimization strategies that plan experiments based on a fixed number of prior iterations. (C) Search strategies that plan experiments based on a surrogate model constructed from all prior experiments. Trends in Chemistry 2019 1, DOI: ( /j.trechm ) Copyright © 2019 Elsevier Inc. Terms and Conditions
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Figure 2 The Closed-Loop Approach (Box 1). The closed-loop approach uses an experiment planning algorithm based on machine learning (ML) and a number of automated robotics platforms to execute experiments. Several ML methods can be used to construct surrogate models, such as Bayesian neural networks (illustrated earlier), Gaussian processes, or random forests. A control software schedules experiments and directs experimental feedback. Trends in Chemistry 2019 1, DOI: ( /j.trechm ) Copyright © 2019 Elsevier Inc. Terms and Conditions
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Figure 3 The Modules Involved in Next-Generation Self-Driving Laboratories. They are empowered by artificial intelligence for experiment planning, device scheduling, data analysis, and researcher communication. A control software (top right) orchestrates individual modules of the self-driving laboratories. Modules highlighted in gray are directly involved in the experimentation process, either to plan new experiments or to execute recommended experiments. Modules highlighted in yellow improve the user-friendliness and practicality of self-driving laboratories and facilitate long-term data storage or communication with researchers. Trends in Chemistry 2019 1, DOI: ( /j.trechm ) Copyright © 2019 Elsevier Inc. Terms and Conditions
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Figure I Opportunities for Enriching Features toward User-Friendliness. Recent developments in the AI community provide tools which can improve the practicality of a self-driving laboratory. Trends in Chemistry 2019 1, DOI: ( /j.trechm ) Copyright © 2019 Elsevier Inc. Terms and Conditions
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