Abstract
Active learning—the field of machine learning (ML) dedicated to optimal experiment design—has played a part in science as far back as the 18th century when Laplace used it to guide his discovery of celestial mechanics. In this work, we focus a closed-loop, active learning-driven autonomous system on another major challenge, the discovery of advanced materials against the exceedingly complex synthesis-processes-structure-property landscape. We demonstrate an autonomous materials discovery methodology for functional inorganic compounds 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. This robot science enables science-over-the-network, reducing the economic impact of scientists being physically separated from their labs. The real-time closed-loop, autonomous system for materials exploration and optimization (CAMEO) is implemented at the synchrotron beamline to accelerate the interconnected tasks of phase mapping and property optimization, with each cycle taking seconds to minutes. We also demonstrate an embodiment of human-machine interaction, where human-in-the-loop is called to play a contributing role within each cycle. This work has resulted in the discovery of a novel epitaxial nanocomposite phase-change memory material.
Highlights
Active learning—the field of machine learning (ML) dedicated to optimal experiment design —has played a part in science as far back as the 18th century when Laplace used it to guide his discovery of celestial mechanics
Closed-loop autonomous system for materials exploration and optimization (CAMEO) offers a new materials research paradigm to truly harness the accelerating potential of ML, setting the stage for the 21st-century paradigm of materials research—the autonomous materials research lab run under the supervision of a robot scientist or artificial scientist[4]
CAMEO uses a materials-specific active-learning campaign that combines the joint objectives of maximizing knowledge of the phase map P(x) with hunting for materials x∗ that correspond to property F(x) extrema
Summary
Active learning—the field of machine learning (ML) dedicated to optimal experiment design —has played a part in science as far back as the 18th century when Laplace used it to guide his discovery of celestial mechanics. Autonomous systems and machine learning driven research have been demonstrated for optimizing process and system operation[18,19,20] sample characterization[21], and tuning chemical reactions of known polymers and organic molecules for technological applications[22,23,24], using off-the-shelf optimization schemes Taking another step and placing active learning in real-time control of solid-state materials exploration labs promises to accelerate materials discovery while rapidly and efficiently illuminating complex materials-property relationships. Such potential innovation has been discussed in recent prospectives[25,26], with a primary focus on autonomous chemistry[27,28,29]
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have