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(Bloomberg) -- Ever since ChatGPT went virallast fall, companieshave touted many waysartificial intelligencecan make ourlives easier. Theyve promised superhuman virtual assistants, tutors, lawyers and doctors.

What about a superhuman chemical engineer?

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London-based startupOrbital Materials would like tocreate just that. The startup is working to applygenerative AI the method behind tools like ChatGPT expresslyforaccelerating the development ofclean energytechnologies. Essentially, the idea is to make computer models powerful and sharp enough to identify the best formulas for products likesustainable jet fuelor batteries free of rare-earth minerals.

Jonathan Godwin, anOrbital Materials co-founder, imagines a system thats as accessible and effective as the software engineers use today to model designs for things likeairplane wings and household furniture.

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That, historically, has just been too difficult for molecular science, he said.

ChatGPT works because its adept at predicting text heres the next word or sentence that makes sense. For the same idea to work in chemistry, an AI system would need to predict how a new molecule would behave, not just in a lab but in the real world.

Several researchers and companies have deployedAI to hunt for newer, greener materials. Symyx Technologies, a materials discovery company formed in 1990s,wound down after a sale. More recentcompanies have gained traction makingpetrochemical alternativesandprogramming cells.

Still, for many materials needed to decarbonize the planet, the technology isntthere yet.

It cantake decadesfor a new advanced material to move from discovery to the market. That timeline is way too slow for the businesses and nations looking to rapidly cut emissions as they race to meetnet zero targets.

That needs to happen in the next 10 years, or sooner, said Aaike van Vugt, co-founder of material science startup VSParticle.

AI researchers think they can help.Before launching Orbital Materials, Godwin spent three years researching advanced material discovery atDeepMind, Googles AI lab. That lab releasedAlphaFold, amodel to predictprotein structures that could speed up the search for new drugs and vaccines.That, coupled with the rapidtakeoff of tools like ChatGPT, convinced him that AI would soon be capable of conquering the material world.

What I thought would take 10 years was happening in a matter of 18 months, he said. Things are getting better and better and better.

Godwin compareshis method withOrbital Materials toAI image generators like Dall-E and Stable Diffusion. Those models are created using billions of online images so that when users type in a text prompt, a photorealistic creation appears. Orbital Materials plans to trainmodels with loads of data on the molecular structure ofmaterials. Type in some desired property and material say, an alloy that can withstand very high heat and the model spits out a proposed molecular formula.

In theory, this approach is effective because it can both imagine new moleculesandmeasure howthey will work, said Rafael Gomez-Bombarelli, an assistant professor at MIT, who advisedOrbital Materials. (He said he is not an investor.)

Right now, many tech investors are prowling for companies that can turn a profit byimproving greener material production.Thats particularly the case in Europe, where regulatorsare forcing manufacturersto lower carbon emissions or face stiff fines. The markets for advanced materials in sectors like renewable energy, transportation and agriculture are set togrow by tens of billions of dollars in the coming years.

Some researchers, like those at theUniversity of Toronto, have set up self-driving labs that pair AI systems with robots to search for new materials at unparalleled speeds. Dutch startup VSParticle makes machinery used to develop components for gas sensors and green hydrogen.

Think of it like aDNA sequencer in a genomics lab, said co-founder van Vugt,who believes his equipment can help shorten the 20-year time horizon of advanced materials to one year, and, eventually,a couple of months. His company is currently raising investment capital.

Orbital Materials, which raised $4.8 million in previously undisclosed initial funding, is planning to start withturning its AI gaze towardcarbon capture. The startup is working on an algorithmic model that designsmolecular sieves, ortiny pellets installedwithin a device that can sift CO2 and other noxious chemicals from other emissions,more efficiently than current methods.(Godwin said the startup, which has several AI researchers, plans to publish peer-reviewed results on this tech soon.) Carbon capture has failed to work at scale to date, though thanks to a slew of government incentives,particularly in the US, interest in deploying the technology is rapidly ramping up.

Eventually, Godwin said Orbital Materials would like tomove into areas like fuel and batteries. He imagines mirroring thebusiness model ofsynthetic biology and drug discovery companies: develop the brainpower, then license out the software or novel materials to manufacturers. Its going to take us a little bit of time to get to market," said Godwin. "But once youre there, it happens very quickly.

But getting the AI right is only half the battle. Actually making advanced materialsin areas like battery and fuel production requires working with huge incumbent enterprises and messy supply chains. This can be even costlier than developing new drugs,argued MITs Gomez-Bombarelli.

The economics and de-risking make it just way harder, he said.

Heather Redman, a managing partner with Flying Fish Partners, which backedOrbital Materials, said most tech investors chasing the shiny penny of generative AI have failed to look at its applications outside of chatbots.She acknowledged the risks of startups working in the energy sector, butbelievesthe $1 trillion potential of markets like batteries and carbon capture are worth the investing risk.

We love big hills as long as theres a big gigantic market and opportunity at the top, she said.

Gomez-Bombarelli is aware how big these hills can be. He helped start a similar company to Orbital Materials in 2015, calledCalculario, which used AI and quantum chemistry to speed up the discovery process for a range ofnew materials.It didnt get enough traction and had to focus on the OLED industry.

Maybe we didnt make our case, he said. Or maybe the market wasnt ready.

Whether it is now is an open question. But there are encouraging signs.Computing certainly has improved. Newcomers might also have an easier time selling AI because would-be customers could more easily graspthe potential. Gomez-Bombarelli said the pitch is relatively simple:Look at ChatGPT. Wecan do the same thing for chemistry.

Excerpt from:
DeepMind Alum Wants to Use AI to Speed the Development of ... - Data Center Knowledge