Highlights

In brief

A unique combined workflow of machine learning models, high-throughput synthesis and standardised electrochemical testing helps identify stable, effective layered double hydroxide catalysts for water-splitting reactions more efficiently than complex molecular simulations.

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Computer-crafted clays catalyse clean power

11 Oct 2024

Departing from costly simulations and trial-and-error methods, researchers harness machine learning and automation to efficiently discover new recipes for green hydrogen catalysts.

Green hydrogen: a prime candidate among eco-friendly alternatives to fossil fuels. When burnt, all it emits is steam; when produced from renewable energy sources, it causes far less greenhouse gas emissions than oil and gas production. These features make green hydrogen a promising clean means of storing energy, powering vehicles and feeding factories.

Catalysts are key to making green hydrogen as they facilitate the water-splitting reaction, which shatters water (H2O) molecules into hydrogen (H2) and oxygen (O2). Currently, most catalysts for the purpose use some of the Earth’s rarest metals, such as platinum, palladium and iridium. Their scarcity drives up costs and complicates supply chains, limiting green hydrogen’s wider adoption.

Yee-Fun Lim, a Principal Scientist at A*STAR’s Institute of Sustainability for Chemicals, Energy and Environment (ISCE2), points to layered double hydroxides (LDHs) as a more accessible option for water-splitting catalysts. Essentially made of clay, LDHs aren’t just structurally versatile—holding a variety of elements while keeping their unique layered and charged structure—but are also easily manufactured at scale from chemical solutions.

“Most research to discover new LDH compositions with higher catalytic activity still relies on lab-based trial and error,” said Lim. “We decided to test if machine learning (ML) optimisation methods, combined with high-throughput synthesis and standardised electrochemical testing, could more efficiently find optimal LDHs for green hydrogen electrocatalysis.”

A team comprised of Lim, ISCE2 Senior Scientist Albertus Denny Handoko and collaborators from ISCE2, A*STAR’s Institute of Materials Research and Engineering (IMRE) and the University of Toronto, Canada, set out to develop an ML-based method that could not only predict the best-performing LDH electrocatalysts within a certain range of raw materials, but also the optimal parameters required to craft them.

The team initially synthesised and tested a small sample of 53 LDH catalysts to produce a training dataset for their ML algorithms, which were built based on gradient boosting and neural network frameworks. The trained algorithms were then paired with Bayesian optimisation algorithms to suggest a new set of LDH material combinations. An automated cycle of synthesis, testing and optimisation was repeated until the catalysts hit a performance ceiling.

“While we could have used more complex molecular structural simulations, those would take hours to days to run even on supercomputers, due to the structural flexibility of LDHs,” said Lim. “However, our approach relies solely on understanding a catalyst’s compositional ratio, not its structure; as such, model training takes just about an hour on a personal computer.”

The team's final optimised catalyst was a novel composition based on cobalt and iron with added molybdate (CoFeMo LDH), which efficiently split water with minimal energy loss and prolonged stability. To their surprise, the catalyst lacked nickel—a core element in the first documented LDH with good water-splitting performance.

The team plans to scale up their CoFeMo LDH recipe for commercial applications, and also to explore how their automated synthesis and ML optimisation workflow can optimise other catalysts or industrial chemical processes, broadening the impact of their work.

The A*STAR-affiliated researchers contributing to this research are from the Institute of Sustainability for Chemicals, Energy and Environment (ISCE2) and the Institute of Materials Research and Engineering (IMRE).

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References

Lim, C.Y.J., Made, R.I., Khoo, Z.H.J., Ng, C.K., Bai, Y., et al. Machine learning-assisted optimization of multi-metal hydroxide electrocatalysts for overall water splitting. Materials Horizons 10, 5022-5031 (2023). | article

About the Researchers

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Yee-Fun Lim

Principal Scientist and Deputy Director (Catalysis and Green Process Engineering)

Institute of Sustainability for Chemicals, Energy and Environment (ISCE2)
Yee-Fun Lim joined the Institute of Materials Research and Engineering (IMRE) after graduating with a PhD in Applied Physics from Cornell University in 2011. He is a Principal Scientist at ISCE2. His research spans electrochemistry, materials synthesis, high-throughput experimentation and AI-driven machine learning optimisation. As co-PI in the Accelerated Catalyst Development Platform (ACDP), which includes ISCE2, IMRE and IHPC, he has developed automated synthesis workstations and established workflows for advanced machine learning optimisation. He has also effectively demonstrated the ACDP platform's capabilities in electrocatalyst discovery and optimisation.
Albertus Denny Handoko is a Materials Engineer by training. He gained his core competency in hydrothermal method and X-ray characterisation during his PhD studies at Nanyang Technological University in 2011. He gained research experience and developed keen interest in electro and photochemistry during his research career at University College London (UCL), National University of Singapore (NUS), and Institute of Materials Research and Engineering (IMRE). His current research effort in Institute of Sustainability for Chemicals, Energy and Environment (ISCE2) is dedicated to advance flow electrochemistry systems for electrosynthesis and conversion reactions such as CO2 conversion, plasma electrochemistry, electrofluorination and epoxidation chemistry, water splitting and energy storage.

This article was made for A*STAR Research by Wildtype Media Group