Next-Generation Experimentation with Self-Driving Laboratories Florian Häse, Loïc M. Roch, Alán Aspuru-Guzik  Trends in Chemistry  Volume 1, Issue 3, Pages 282-291 (June 2019) DOI: 10.1016/j.trechm.2019.02.007 Copyright © 2019 Elsevier Inc. Terms and Conditions

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, 282-291DOI: (10.1016/j.trechm.2019.02.007) Copyright © 2019 Elsevier Inc. Terms and Conditions

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, 282-291DOI: (10.1016/j.trechm.2019.02.007) Copyright © 2019 Elsevier Inc. Terms and Conditions

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, 282-291DOI: (10.1016/j.trechm.2019.02.007) Copyright © 2019 Elsevier Inc. Terms and Conditions

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, 282-291DOI: (10.1016/j.trechm.2019.02.007) Copyright © 2019 Elsevier Inc. Terms and Conditions