A robotic assembly line will churn out and test thousands of experimental batteries and fuel-making devices per week, replacing the slow, manual trial-and-error that currently governs materials discovery. The problem is that the possible combinations of chemicals and structures for new batteries and electrocatalysts—devices that split water into hydrogen or turn CO₂ into fuel—are astronomically large. Researchers cannot physically make and test them all. Computational models can narrow the field, but the remaining candidates still overwhelm a human-led lab. DIGIBAT will close this gap by building three high-throughput stations: one that synthesises and characterises new materials, one that turns them into electrodes and complete devices, and one that tests their electrochemical performance. The entire pipeline runs automatically, generating rich datasets that machine learning algorithms can mine for patterns. If it works, DIGIBAT will dramatically accelerate the discovery of cheaper, longer-lasting batteries and precious-metal-free catalysts for green hydrogen and synthetic fuels. This could reshape energy storage for electric vehicles and grid balancing, and make industrial-scale production of ammonia and carbon-neutral fuels economically viable. It will also improve reproducibility by eliminating human error from routine synthesis and testing.
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Batteries and electrocatalytic devices (i.e electrolysers, fuel cells) have multiple components spanning different length scales. The materials design space in these research fields is too large to be explored empirically. While experimental work can be directed by computational modelling to make this challenge more tenable, this is time consuming, and the number of tests/syntheses is still be too large on the experimental scale. DIGIBAT will combine computational tools (e.g. atomistic and molecular modelling, process modelling, computer-aided design, machine learning algorithms, data science) and automated HT synthesis, characterisation and testing from atoms to devices to accelerate the discovery and optimisation of new batteries and electrofuels. Specifically, DIGIBAT will comprise three HT stations: Platform A dedicated to materials synthesis and characterisation, Platform B dedicated to HT electrodes manufacturing all the way to device manufacturing and Platform C dedicated to HT electrochemical testing for both batteries and electrocatalysts. DIGIBAT will be paired with materials characterisation also applied in HT, including in operando characterisation. By executing data-rich experiments, DIGIBAT will increase the pace of innovation, while enhancing reproducibility by eliminating human errors. The research enabled by ATLAS will target challenges related to: (1) the discovery and optimisation of new battery chemistries, (2) synthesising, optimising, and testing recycled battery materials; (3) Discovering precious metal free electrocatalysts for green H2 production and fuel cells; (4) Efficient N2 to ammonia and CO2 reduction to fuels and chemicals for electrocatalysts discovery
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