(174a) Accelerated Materials Discovery Enabled By an End-to-End Autonomous Fluidic Lab

Despite the intriguing size-dependent properties and widespread applications of semiconductor nanocrystals in energy and chemical technologies, their discovery and development are still time-, resource-, and labor-intensive. Existing materials development strategies using manual experimentation techniques and batch reactors fall short in comprehensive exploration of the vast synthesis space of energy materials, resulting in a slow (>10 years) and expensive discovery and development. Recent advances in flow chemistry and autonomous experimentation provide an exciting opportunity to accelerate the discovery and development of semiconductor nanocrystals from 10+ years to less than 1 month.¹ In this talk, I will present an end-to-end self-driving fluidic lab for accelerated discovery and development of semiconductor nanocrystals, through integration of flow chemistry, colloidal nanoscience, and in-situ characterization with data science.² I will discuss how standardization and modularization of the hardware of the self-driving fluidic labs can enable data intensification (>100 experiments/day) while minimizing chemical consumption to achieve fast-tracked navigation through high dimensional experimental design space of energy materials (>10²⁰ possible experimental conditions). Example applications of the end-to-end self-driving fluidic lab for the synthetic route discovery of energy materials will be presented. Finally, I will present a fully autonomous fluidic lab operated by reinforcement learning to rapidly discover multi-step chemistry of heteronanostructures with no prior knowledge within 30 days of continuous operation.³

References

1. Abolhasani, M.; Kumacheva, E., The rise of self-driving labs in chemical and materials sciences. Nature Synthesis 2023. https://doi.org/10.1038/s44160-022-00231-0%20Download%20citation
2. Volk, A. A.; Abolhasani, M., Autonomous flow reactors for discovery and invention. Trends in Chemistry 2021, 3 (7), 519-522.
3. Volk, A. A.; Epps, R. W.; Yonemoto, D. T.; Masters, B. S.; Castellano, F. N.; Reyes, K. G.; Abolhasani, M., AlphaFlow: autonomous discovery and optimization of multi-step chemistry using a self-driven fluidic lab guided by reinforcement learning. Nature Communications 2023, 14 (1), 1403.