PrISMa Overcomes the “Valley of Death”

She explains: “Over the past decade, there has been a huge amount of effort devoted to identifying promising materials capable of capturing CO₂.

“Chemists have proposed thousands of novel porous materials, but we did not have the tools to quickly evaluate if any materials are promising for a carbon capture process. Evaluating such materials requires a lot of experimental data and detailed knowledge of the capture process. And a careful evaluation of the economics and life-cycle assessment of the process.

“We cannot expect chemists to have all that knowledge. Here is where PrISMa can make a huge difference. The PrISMa platform is a modeling tool that integrates different aspects of carbon capture, including materials, process design, economic analysis, and life cycle assessment. We use quantum chemistry, molecular simulation, and Machine Learning to predict, for new materials, all the data that is needed to design a process. Alternatively, we can use the experimental data from materials synthesized in a lab. The platform then evaluated their performance in over 60 different case studies from around the world.”

Professor Garcia continues: “This innovative approach accelerates the discovery of top-performing materials for carbon capture, surpassing traditional trial-and-error methods. The platform can also inform the different stakeholders by providing engineers with options to identify economically and environmentally challenging factors in the design phase of optimal capture technologies, molecular design targets for chemists and environmental hotspots for materials, local integration benefits for CO₂ producers, and the best locations for investors.”

PrISMa: A Tool Transforming Carbon Capture

PrISMa is already yielding impressive results with the platform having been used to accurately simulate the implementation of carbon capture technologies in cement plants located in different regions of the world. It found suitable materials for each location, cutting costs by half when compared with previous technologies.

PrISMa also offers an interactive tool that allows users to explore the potential of over 1,200 materials for carbon capture applications.

Expanding Technological Frontiers With Machine Learning

“Identifying more top-performing carbon capture materials increases the likelihood of advancing some of them to the next Technological Readiness Level,” continues Professor Garcia.

Fergus Mcilwaine, a PhD student leading the Machine Learning activities in Professor Garcia’s team, added: ” Screening such a large number of materials requires huge amounts of computational time. We developed a Machine Learning model that significantly accelerates this process, allowing us to discover cost-effective materials from enormous chemical design spaces.”

PrISMa has been led by Heriot-Watt University in partnership with scientists from the Swiss Federal Institute of Technology Lausanne (EPFL) and ETH Zurich, Lawrence Berkeley National Laboratory and the University of California Berkeley in the US, and the Institut des Matériaux Poreux de Paris in France. The project has received funding from the ACT Programme, the Grantham Foundation for the Protection of the Environment and the Industrial Decarbonisation Research and Innovation Centre (IDRIC).

Professor Garcia concluded: “This study highlights the need to follow a holistic approach when evaluating technologies to achieve our net-zero targets. The platform speeds up materials discovery for carbon capture applications and focuses Research and Development efforts towards achievable performance targets at scale.

“The tool can help tremendously our current industrial decarbonisation efforts. It can play a key role in informing investment strategies and policy decisions on more sustainable and cost-effective carbon-capture solutions.”

Reference: “A holistic platform for accelerating sorbent-based carbon capture” by Charithea Charalambous, Elias Moubarak, Johannes Schilling, Eva Sanchez Fernandez, Jin-Yu Wang, Laura Herraiz, Fergus Mcilwaine, Shing Bo Peh, Matthew Garvin, Kevin Maik Jablonka, Seyed Mohamad Moosavi, Joren Van Herck, Aysu Yurdusen Ozturk, Alireza Pourghaderi, Ah-Young Song, Georges Mouchaham, Christian Serre, Jeffrey A. Reimer, André Bardow, Berend Smit and Susana Garcia, 17 July 2024, Nature.
DOI: 10.1038/s41586-024-07683-8