Autonomous experimentation for accelerated materials discovery

Figure: The closed loop workflow used to discover and synthesize new materials in the A-Lab, an autonomous platform that integrates computations with robotics.

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 has integrated these components into one fully autonomous, closed-loop system known as the A-Lab. As we continue to expand this system, we aim to achieve a rate of materials discovery that is 10-100× faster than the current standard.

Select Publications:

  1. N. J. Szymanski, B. Rendy, Y. Fei, R. E. Kumar, Y. Zeng, G. Ceder et al. An autonomous laboratory for the accelerated synthesis of novel materials. Nature (2023).
  2. 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.
  3. N. J. Szymanski, P. Nevatia, C. Bartel, Y. Zeng, and G. Ceder, Autonomous and Dynamic Precursor Selection for Solid-state Materials Synthesis, Nat Commun. 14, 6956 (2023).
  4. 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.
  5. N. J. Szymanski, C. J. Bartel, Y. Zeng, M. Diallo, H. Kim, and G. Ceder, Adaptively Driven X-ray Diffraction Guided by Machine Learning for Autonomous Phase Identification, npj Computational Materials 2023, 9:31