U of T Engineering-Fujitsu collaboration uses quantum-inspired computing to discover better catalysts for clean hydrogen production | Jobs Vox

U of T Engineering-Fujitsu collaboration uses quantum-inspired computing to discover better catalysts for clean hydrogen production.

Researchers at U of T Engineering and Fujitsu have developed a new way to search through ‘chemical space’ for materials with desirable properties. The technique has resulted in a promising new catalytic material that could help reduce the cost of clean hydrogen production.

The discovery represents a significant step towards more sustainable methods of energy storage, including renewable but intermittent sources such as solar and wind power.

Professor Ted Sargent (ECE), senior author on a new paper published in Matter:

Increasing the production of what we call green hydrogen is a priority for researchers around the world because it offers a carbon-free way to store electricity from any source.

“This work provides a proof-of-concept for a new approach to overcoming one of the major remaining challenges, which is the lack of highly active catalytic materials to speed up critical reactions.”

Almost all commercial hydrogen is produced from natural gas. process produces CO₂ As a byproduct: If CO₂ is vented into the atmosphere, the product is known as ‘grey hydrogen’, but if CO₂ When captured and stored, it is called ‘blue hydrogen’.

In contrast, ‘green hydrogen’ is a carbon-free method that uses a device called an electrolyzer to split water into hydrogen and oxygen gas. The hydrogen can later be burned or reacted in a fuel cell to regenerate electricity. However, the low efficiency of available electrolyzers means that most of the energy in the water-splitting stage is wasted as heat rather than being captured in hydrogen.

Researchers around the world are racing to find better catalyst materials that can improve this efficiency. But because each potential catalytic material can be made from many different chemical elements combined in various ways, the number of possible permutations quickly becomes overwhelming.

jehad abidMSE PhD candidate, one of two co-lead authors on the new paper, said:

One way to do it is human intuition, by researching what other groups have created stuff and trying something similar, but it’s very slow.

“Another approach is to use computer models to simulate the chemical properties of all possible materials starting from first principles. But in this case, the calculations get really complicated, and the computational power required to run the model becomes too much.”

To find a way, the team turned to the emerging field of quantum-inspired computing. They used a digital annealer, a device that was created as a result of a long-standing collaboration between U of T Engineering and Fujitsu Research. This collaboration has also resulted in the creation of the Fujitsu Co-Manufacturing Research Laboratory at the University of Toronto.

hidetoshi matsumuraFujitsu Consulting (Canada) Inc. Senior Researcher said:

The digital annealer is a hybrid of unique hardware and software designed to be highly efficient at solving combinatorial optimization problems.

“These problems include finding the most efficient route between multiple locations in a transportation network, or choosing a set of stocks to build a balanced portfolio. Searching through different combinations of chemical elements to find a catalyst with the desired properties is another Example, and it was the perfect challenge for our digital annealer to address.

In the paper, the researchers used a technique called cluster expansion to analyze a truly vast number of possible catalyst material designs – they estimate the total number to be on the order of hundreds of quadrillion. For perspective, a quadrillion is roughly the number of seconds that will pass in 32 million years.

The results point to a promising family of materials composed of ruthenium, chromium, manganese, antimony and oxygen, not previously explored by other research groups.

The team synthesized several of these compounds and found that the best of them demonstrated mass activity – a measure of the number of reactions that can be catalysed per mass of catalyst – compared to the best catalysts currently available. was almost eight times higher. ,

The new catalyst has other advantages, too: It works well in acidic conditions, which are a requirement for state-of-the-art electrolyzer designs. Currently, these electrolyzers rely largely on catalysts made from iridium, a rare element that is expensive to obtain. In comparison, ruthenium, the main component of the new catalyst, is more abundant and has a lower market value.

There is more work ahead for the team: for example, they aim to further optimize the stability of the new catalyst before it can be tested in an electrolyzer. Nevertheless, the latest work serves as a demonstration of the effectiveness of the new approach to chemical location finding.

Hitartha ChaubisaECE PhD candidate, the paper’s other co-lead author, said:

I think the exciting thing about this project is that it shows how you can solve really complex and important problems by combining expertise from different fields.

“For a long time, materials scientists have been looking for these more efficient catalysts, and computational scientists have been designing more efficient algorithms, but the two efforts have been disconnected. When we brought them together, we could very quickly We were able to find promising solutions. I think there are many more useful discoveries like this to be made.”

U of T Engineering-Fujitsu collaboration uses quantum-inspired computing to discover better catalysts for clean hydrogen production, December 13, 2022