It is one of society’s greatest paradoxes: the political will to save resources and the climate when the most popular technologies are energy-greedy and dependent on rare resources and materials. A common case in point is obviously smartphones and their reliance on rare earth metals. But the same goes for catalysts – substances increasing the rate of chemical reactions that happen to hold the keys to green energy conversion technologies. The catalyst problem was at the heart of the project CritCat (Towards Replacement of Critical Catalyst Materials by Improved Nanoparticle Control and Rational Design). Its objective: substituting rare critical metals used in heterogeneous and electrochemical catalysis (usually platinum group metals (PGMs)) with earth-abundant materials. Over 3 years, the project consortium explored the properties of ultra-small transition metal (TM) nanoparticles to enable emerging energy conversion technologies. To do so, the team consecutively split water into hydrogen and oxygen to identify TM alternatives to catalytic PGMs, synthetised samples, characterised them and used them in chemical benchmark reactions to measure their catalytic activity. Eventually, they developed prototype electrolysers from the most promising candidates to investigate their performance.
No more trial and error?
The project’s innovation, however, doesn’t lie solely in the identification of new, cheaper materials. Instead of using the traditional lab-based trial-and-error process, the team opted for artificial intelligence and computer simulations, in the form of a materials modelling platform for catalyst design. “We provide a full ecosystem from electronic structure simulations (density functional theory (DFT)) to real reactions, and we incorporate machine learning algorithms to streamline and handle complex potential energy surfaces (PES) for reaction energetics,” explains Jaakko Akola, coordinator of CritCat on behalf of Tampere University. He continues: “The platform first requires input data from DFT, so that the algorithms can be trained to predict PES. The next step is to introduce other machine learning algorithms to identify the characteristic properties (descriptors) that are intimately linked with catalytic activity. As the DFT database increases, the capacity of the platform to predict the properties of new materials also increases.”
Towards hydrogen-based energy solutions
The project’s electrolyser prototype is an important step towards green energy production based on earth-abundant materials. It could eventually solve, for instance, the problem of the intermittent supply of solar/wind energy by converting electricity to hydrogen. Equally, the project’s modelling platform has yet to reveal its full potential. First – although the modelling platform has yet to become more effective than trial and error – the fact that its predictive power increases as the database of studied materials grows and the machine learning tools keep being developed, is very promising. The acquired chemical insight can also be reused later, unlike trial and error. Secondly, the team has only scratched the surface of potential applications, as Akola points out: “The platform has first and foremost been designed for hydrogen evolution reaction (HER), which is relevant for the production of hydrogen energy by splitting water. However, our goal has been to produce a modelling infrastructure that can be applied for other chemical reactions – both gas phase and electrochemistry – easily.” This is precisely what the project team has been working on since CritCat’s completion in June 2019. They are currently working on catalysts and prototype devices for fuel cells, as well as the conversion of CO2 to synthetic fuels.
CritCat, catalyst, rare metal, earth-abundant, PGMs, transition metal, hydrogen, energy conversion, modelling platform