Next-Generation Experimentation with Self-Driving Laboratories

By Florian Hase, Loïc M. Roch, Alán Aspuru-Guzik. Trends in Chemistry, 2019

307 citations recorded by the source corpus.

The ever-growing demand for advanced functional materials requires disruption of conventional approaches to experimentation and acceleration of the discovery process. State-of-the-art approaches to scientific discovery are inherently slow, capital intensive, and have arguably reached a plateau. Significant advances are possible when rethinking and redesigning the traditional experimentation process. Self-driving laboratories promise to substantially accelerate the discovery process by augmenting automated experimentation platforms with artificial intelligence (AI). AI methods actively search for promising experimental procedures by hypothesizing about their outcomes based on previous experiments. This feedback loop is crucial to reduce the number of experiments needed for discovery. Supplying automated platforms with AI enables self-driving laboratories to fully embrace the vision of autonomous experimentation.

Subfields: Biomedical Engineering, Materials Chemistry

View Paper: https://www.semanticscholar.org/paper/53b7828566d5ddec06d9f6ea2f321b1dd7cf7ee5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Materials Chemistry

1. Ryan Jacobs
2. Dane Morgan

Citations in the corpus

• Opportunities and Challenges for Machine Learning in Materials Science (2020, background): “Aggour et al. 2019 213 Artificial intelligence/machine learning in manufacturing and inspection: A GE perspective Schmidt et al. 2019 130 Recent advances and applications of machine learning in solid-state materials science Arroyave and McDowell 2019 67 Systems Approaches to Materials Design: Past, ”