MIT researchers using AI and robotics to find a way to artificially produce natural hydrogen from underground rocks

A team of researchers from the prestigious Massachusetts Institute of Technology (MIT) in Boston is using artificial intelligence (AI) and robotics in an attempt to find a catalyst or catalyst mixture that could artificially stimulate the production of natural hydrogen from underground rocks.

It is known that when water reacts with certain iron-rich rocks under pressure and high temperatures (as naturally found underground), hydrogen can be released. But this process can take millions of years in nature.

MIT hopes to find a way to stimulate this chemical reaction artificially, potentially producing “geologic” hydrogen on demand — which could prove to be a far cheaper option than green or blue hydrogen (made from renewables-powered water electrolysis or methane reformation with carbon capture and storage, respectively).

“We aim to optimize the reaction parameters to make the reaction faster and produce hydrogen in an economically feasible manner,” says MIT assistant professor Iwnetim Abate, who is leading the programme with the help of a $1.3m grant from the US Department of Energy (DOE).

“If you get hydrogen at a dollar a kilo, it’s competitive with natural gas on an energy-price basis,” said ARPA-E programme director Douglas Wicks.

“In geologic hydrogen, we don’t know how we can accelerate the production of it, because it’s a chemical reaction, nor do we really understand how to engineer the subsurface so that we can safely extract it.

“We’re trying to bring in the best skills of each of the different groups to work on this under the idea that the ensemble should be able to give us good answers in a fairly rapid timeframe.”

The MIT team is developing what they call a “high-throughput system” in which AI software and robotics are used to test different catalyst mixtures and “simulate what would happen when applied to rocks from various regions, with different external conditions like temperature and pressure”

“And from that we measure how much hydrogen we are producing for each possible combination,” Abate explains. “Then the AI will learn from the experiments and suggest to us, ‘Based on what I’ve learned and based on the literature, I suggest you test this composition of catalyst material for this rock’.”

The hope is that they can find a catalyst that will be cheap and simple.

“Some catalysts are very costly and hard to produce, requiring complex production or preparation,” said project researcher Yifan Gao. “A catalyst that’s inexpensive and abundant will allow us to enhance the production rate — that way, we produce it at an economically feasible rate, but also with an economically feasible yield.”

However, it is far from guaranteed that a catalyst can be found that would be able to sufficiently speed up serpentinisation.

“If we can understand how to stimulate these rocks into generating hydrogen, safely getting it up, it really unleashes the potential energy source,” says Wicks.

“As I like to say, this is enabling technology that we hope to, in a very short term, enable us to [answer the question], ‘Is there really something there?’”