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Fully Autonomous Characterization and Data Collection from Crystals of Biological Macromolecules

Fully Autonomous Characterization and Data Collection from Crystals of Biological Macromolecules

Published on: March 22, 2019

Progress and prospects for accelerating materials science with automated and autonomous workflows.

Helge S Stein¹, John M Gregoire^1,2

¹Joint Center for Artificial Photosynthesis , California Institute of Technology , Pasadena , CA 91125 , USA .

Chemical Science

|March 11, 2020

Summary

Integrating automation and artificial intelligence (AI) can revolutionize materials discovery. This framework maps current automation levels and proposes advanced autonomous loops for faster material development.

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Area of Science:

Materials Science
Artificial Intelligence
Automation Engineering

Background:

Materials research acceleration is crucial for future technologies.
High-throughput methods and computational techniques have advanced individual research tasks but not fully revolutionized discovery.
A gap exists between current automation and the potential for fully autonomous materials discovery.

Purpose of the Study:

To present a framework and ontology for the materials experiment lifecycle.
To visualize materials discovery workflows and map automation levels.
To outline the next generation of autonomous loops in materials science.

Main Methods:

Developing a conceptual framework and ontology for materials research workflows.
Analyzing current automation capabilities and complexity in materials science.
Reviewing advancements in artificial intelligence and high-throughput experimentation.

Main Results:

A structured approach to understanding and visualizing materials discovery workflows.
Identification of key areas for expanding autonomous loops, including data quality and model design.
Mapping of current automation levels against scientific and automation complexity.

Conclusions:

Integrating diverse experimental techniques and automating expert decision-making are key to advancing autonomous materials discovery.
Emerging AI and high-throughput experimentation signal an impending revolution in materials development.
A comprehensive framework is essential for guiding the transition towards fully autonomous materials research.