Accelerated catalyst layer design and fabrication: Materials discovery through machine learning

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Decarbonizing the transport and energy sectors is critical for mitigating anthropogenic climate change, as transportation alone accounts for 23% of global energy-related carbon dioxide emissions. While current fuel cells and electrolyzers are already reaching state of the art performances to be used in commercial passenger cars and light vehicles, the widespread use of these technologies demands a substantial reduction in catalyst loading to reduce the high costs of precious group metals. Moreover, the catalyst materials for CO2 electrolyzers, which convert atmospheric CO2 into useful forms of fuel such as syngas, require a substantial amount of work in terms of reducing the loading due to its complicated ion-electron-reactant transport mechanisms. A catalyst layer is the core component in all electrochemical energy conversion devices including polymer electrolyte membrane (PEM) fuel cells, water electrolyzers, and CO2 electrolyzers. Due to the high catalytic activity required, precious group metals (PGMs) such as Platinum or Iridium are typically used as catalyst materials; however, the costs are prohibitive for widespread adoption, and ultra-low loading catalyst layers would drastically reduce the costs for these clean electrochemical energy conversion technologies. We will apply machine learning to stochastic material generation and multiphase flow simulations of porous materials to identify the key parameters and material architectures for optimal transport behaviour. The requested system will be used to investigate new catalyst layer designs to achieve reduced catalyst loadings with enhanced stability. We are requesting funds to support a fabrication and testing system for designing catalyst layers with reduced loadings and high stability for fuel cells and electrolyzers. Specifically, the funds will be used for an ultrasonic based catalyst coating system as well as a characterization tool for a segmented electrochemical cell, which will be coupled with Prof. Bazylak's in situ X-ray tomography system to establish a fabrication, testing, and imaging laboratory for catalyst materials. This laboratory will provide Prof. Bazylak and her trainees with an internationally unique system for machine learning based materials discovery for high throughput, concurrent in operando testing and characterization of novel catalyst layers. The impact will be the discovery of ultra-low loading catalyst layer architectures for affordable fuel cells and electrolyzers. The research proposed here will lead to new catalyst designs and manufacturing processes for fuel cells and electrolyzers that will be disseminated to the wider research community. The outcomes of this research will strengthen Canada's leadership in catalyst development and attract industrial and academic partners for the advancement of clean electrochemical energy conversion.