| By Mark Bergen Ever since ChatGPT went viral last fall, companies have touted many ways artificial intelligence can make our lives easier. They've promised superhuman virtual assistants, tutors, lawyers and doctors. What about a superhuman chemical engineer? London-based startup Orbital Materials would like to create just that. The startup is working to apply generative AI — the method behind tools like ChatGPT — expressly for accelerating the development of clean energy technologies. Essentially, the idea is to make computer models powerful and sharp enough to identify the best formulas for products like sustainable jet fuel or batteries free of rare-earth minerals. Jonathan Godwin, an Orbital Materials co-founder, imagines a system that's as accessible and effective as the software engineers use today to model designs for things like airplane wings and household furniture. "That, historically, has just been too difficult for molecular science," he said. Photographer: Josep Lago/AFP via Getty Images ChatGPT works because it's adept at predicting text — here's the next word or sentence that might make 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. For many materials needed to decarbonize the planet, the technology isn't there yet. It can take decades for 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 meet net zero targets. Before launching Orbital Materials, Godwin spent three years researching advanced material discovery at DeepMind, Google's AI lab. That lab released AlphaFold, a model to predict protein structures that could speed up the search for new drugs and vaccines. That, coupled with the rapid takeoff 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 compares his method with Orbital Materials to AI 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 train models with loads of data on the molecular structure of materials. 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. National climate policy gets attention but local and regional governments can make a huge difference, especially in larger countries. This week on Zero, Akshat Rathi speaks with three US governors – Jay Inslee of Washington, Michelle Lujan Grisham of New Mexico and Eric Holcomb of Indiana – about how they navigate partisan politics and the need for climate action. Are they taking the right steps to make sure the US economy decarbonizes on time? Find out on this week's episode of Zero — and subscribe now on Apple, Spotify, or Google to get new episodes every Thursday. In theory, this approach is effective because it can both imagine new molecules and measure how they will work, said Rafael Gomez-Bombarelli, an assistant professor at MIT, who advised Orbital Materials. (He said he is not an investor.) Some researchers, like those at the University 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 a DNA 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 with turning its AI gaze toward carbon capture. The startup is working on an algorithmic model that designs molecular sieves, or tiny pellets installed within 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 the scale needed, 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 to move into areas like fuel and batteries. He imagines mirroring the business model of synthetic biology and drug discovery companies: develop the brainpower, then license out the software or novel materials to manufacturers. But getting the AI right is only half the battle. Actually making advanced materials in 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 MIT's Gomez-Bombarelli. "The economics and de-risking make it just way harder," he said. Click here to read the full version of this story on Bloomberg.com. |
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