Autonomous experimentation for accelerated materials discovery

Figure: Schematic showing the closed-loop workflow used to guide and perform experiments for autonomous materials synthesis and characterization.

There is an urgent need to rapidly identify low-cost and high-performance materials that will enable a global transition to sustainable energy conversion and storage technologies. However, the current paradigm of materials design is still too slow; from idea conceptualization to market often takes over 10 years, in large part due to the manual and labor-intensive process of traditional experimentation. To address these challenges, the Ceder group is actively developing tools that can accelerate materials design and development through the automation of three key aspects: (1) robotic execution of synthesis experiments and characterization processes, (2) machine-learned interpretation of characterization data, and (3) decision making enabled by artificial intelligence. Our group is working to integrate these components into one fully autonomous, closed-loop system called the β€œA-Lab”. With the realization of this system, we aim to achieve a rate of materials discovery that is 10-100Γ— faster than the current standard.

Select Publications:

  1. N. J. Szymanski, Y. Zeng, H. Huo, C. Bartel, H. Kim, and G. Ceder, Toward Autonomous Design and Synthesis of Novel Inorganic Materials. Mater. Horiz. 2021, 8 (8), 2169–2198.
  2. N. J. Szymanski, C. J. Bartel, Y. Zeng, Q. Tu, and G. Ceder, Probabilistic Deep Learning Approach to Automate the Interpretation of Multi-phase Diffraction Spectra, Chem. Mater. 2021, 33 (11), 4204–4215.
  3. N. J. Szymanski, C. J. Bartel, Y. Zeng, M. Diallo, H. Kim, and G. Ceder, Adaptively driven X-ray diffraction guided by machinelearning for autonomous phase identification, npj Computational Materials 2023, 9:31