Swiss CAT+, a Data-driven Infrastructure for Accelerated Catalysts Discovery and Optimization

The Catalysis Hub – Swiss CAT+ is a new infrastructure project funded by ETH-domain, co-headed by EPFL and ETHZ. It offers the scientific community a unique integrated technology platform combining automated and high-throughput experimentation with advanced computational data analysis to accelerate the discoveries in the field of sustainable catalytic technologies. Divided into two hubs of expertise, homogeneous catalysis at EPFL and heterogeneous catalysis at ETHZ, the platform is open to academic and private research groups. Following a multi-year investment plan, both hubs have acquired and developed several high-end robotic platforms devoted to the synthesis, characterization, and testing of large numbers of molecular and solid catalysts. The hardware is associated with a fully digitalized experimental workflow and a specific data management strategy to support closed-loop experimentation and advanced computational data analysis

Swiss CAT+, a Data-driven Infrastructure for Accelerated Catalysts Discovery and Optimization

The Catalysis Hub - Swiss CAT+, is a new infrastructure project funded by the ETH Domain, co -head-ed by EPFL and ETHZ. It offers the scientific community a unique integrated technology platform combining automated and high-throughput experimentation with advanced computational data analysis to accelerate the discoveries in the field of sustainable catalytic technologies. Divided into two hubs of expertise, homogeneous catalysis at EPFL and heterogeneous catalysis at ETHZ, the platform is open to academic and private research groups. Following a multi-year investment plan, both hubs have acquired and developed several high-end robotic platforms devoted to the synthesis, characterization, and testing of large numbers of molecular and solid cata-lysts. The hardware is associated with a fully digitalized experimental workflow and a specific data management strategy to support closed-loop experimentation and advanced computational data analysis.ISSN:0009-429