For greater than a decade, MIT Associate Professor Rafael Gómez-Bombarelli has used synthetic intelligence to create new supplies. As the know-how has expanded, so have his ambitions.
Now, the newly tenured professor in supplies science and engineering believes AI is poised to rework science in methods by no means earlier than potential. His work at MIT and past is dedicated to accelerating that future.
“We’re at a second inflection point,” Gómez-Bombarelli says. “The first one was around 2015 with the first wave of representation learning, generative AI, and high-throughput data in some areas of science. Those are some of the techniques I first brought into my lab at MIT. Now I think we’re at a second inflection point, mixing language and merging multiple modalities into general scientific intelligence. We’re going to have all the model classes and scaling laws needed to reason about language, reason over material structures, and reason over synthesis recipes.”
Gómez Bombarelli’s analysis combines physics-based simulations with approaches like machine studying and generative AI to find new supplies with promising real-world purposes. His work has led to new supplies for batteries, catalysts, plastics, and natural light-emitting diodes (OLEDs). He has additionally co-founded a number of corporations and served on scientific advisory boards for startups making use of AI to drug discovery, robotics, and extra. His newest firm, Lila Sciences, is working to construct a scientific superintelligence platform for the life sciences, chemical, and supplies science industries.
All of that work is designed to make sure the way forward for scientific analysis is extra seamless and productive than analysis right now.
“AI for science is one of the most exciting and aspirational uses of AI,” Gómez-Bombarelli says. “Other applications for AI have more downsides and ambiguity. AI for science is about bringing a better future forward in time.”
From experiments to simulations
Gómez-Bombarelli grew up in Spain and gravitated towards the bodily sciences from an early age. In 2001, he gained a Chemistry Olympics competitors, setting him on an educational monitor in chemistry, which he studied as an undergraduate at his hometown faculty, the University of Salamanca. Gómez-Bombarelli caught round for his PhD, the place he investigated the perform of DNA-damaging chemical compounds.
“My PhD started out experimental, and then I got bitten by the bug of simulation and computer science about halfway through,” he says. “I started simulating the same chemical reactions I was measuring in the lab. I like the way programming organizes your brain; it felt like a natural way to organize one’s thinking. Programming is also a lot less limited by what you can do with your hands or with scientific instruments.”
Next, Gómez-Bombarelli went to Scotland for a postdoctoral place, the place he studied quantum results in biology. Through that work, he linked with Alán Aspuru-Guzik, a chemistry professor at Harvard University, whom he joined for his subsequent postdoc in 2014.
“I was one of the first people to use generative AI for chemistry in 2016, and I was on the first team to use neural networks to understand molecules in 2015,” Gómez-Bombarelli says. “It was the early, early days of deep learning for science.”
Gómez-Bombarelli additionally started working to remove guide elements of molecular simulations to run extra high-throughput experiments. He and his collaborators ended up operating a whole bunch of hundreds of calculations throughout supplies, discovering a whole bunch of promising supplies for testing.
After two years within the lab, Gómez-Bombarelli and Aspuru-Guzik began a general-purpose supplies computation firm, which finally pivoted to give attention to producing natural light-emitting diodes. Gómez-Bombarelli joined the corporate full-time and calls it the toughest factor he’s ever performed in his profession.
“It was amazing to make something tangible,” he says. “Also, after seeing Aspuru-Guzik run a lab, I didn’t want to become a professor. My dad was a professor in linguistics, and I thought it was a mellow job. Then I saw Aspuru-Guzik with a 40-person group, and he was on the road 120 days a year. It was insane. I didn’t think I had that type of energy and creativity in me.”
In 2018, Aspuru-Guzik advised Gómez-Bombarelli apply for a brand new place in MIT’s Department of Materials Science and Engineering. But, with his trepidation a couple of college job, Gómez-Bombarelli let the deadline cross. Aspuru-Guzik confronted him in his workplace, slammed his fingers on the desk, and instructed him, “You need to apply for this.” It was sufficient to get Gómez-Bombarelli to place collectively a proper utility.
Fortunately at his startup, Gómez-Bombarelli had spent a number of time fascinated by the right way to create worth from computational supplies discovery. During the interview course of, he says, he was drawn to the power and collaborative spirit at MIT. He additionally started to understand the analysis prospects.
“Everything I had been doing as a postdoc and at the company was going to be a subset of what I could do at MIT,” he says. “I was making products, and I still get to do that. Suddenly, my universe of work was a subset of this new universe of things I could explore and do.”
It’s been 9 years since Gómez Bombarelli joined MIT. Today his lab focuses on how the composition, construction, and reactivity of atoms influence materials efficiency. He has additionally used high-throughput simulations to create new supplies and helped develop instruments for merging deep studying with physics-based modeling.
“Physics-based simulations make data and AI algorithms get better the more data you give them,” Gómez Bombarelli’s says. “There are all sorts of virtuous cycles between AI and simulations.”
The analysis group he has constructed is solely computational — they don’t run bodily experiments.
“It’s a blessing because we can have a huge amount of breadth and do lots of things at once,” he says. “We love working with experimentalists and try to be good partners with them. We also love to create computational tools that help experimentalists triage the ideas coming from AI .”
Gómez-Bombarelli can also be nonetheless targeted on the real-world purposes of the supplies he invents. His lab works intently with corporations and organizations like MIT’s Industrial Liaison Program to know the fabric wants of the non-public sector and the sensible hurdles of business growth.
Accelerating science
As pleasure round synthetic intelligence has exploded, Gómez-Bombarelli has seen the sector mature. Companies like Meta, Microsoft, and Google’s DeepMind now commonly conduct physics-based simulations paying homage to what he was engaged on again in 2016. In November, the U.S. Department of Energy launched the Genesis Mission to speed up scientific discovery, nationwide safety, and power dominance utilizing AI.
“AI for simulations has gone from something that maybe could work to a consensus scientific view,” Gómez-Bombarelli says. “We’re at an inflection point. Humans think in natural language, we write papers in natural language, and it turns out these large language models that have mastered natural language have opened up the ability to accelerate science. We’ve seen that scaling works for simulations. We’ve seen that scaling works for language. Now we’re going to see how scaling works for science.”
When he first got here to MIT, Gómez-Bombarelli says he was blown away by how non-competitive issues have been between researchers. He tries to convey that very same positive-sum pondering to his analysis group, which is made up of about 25 graduate college students and postdocs.
“We’ve naturally grown into a really diverse group, with a diverse set of mentalities,” Gomez-Bombarelli says. “Everyone has their own career aspirations and strengths and weaknesses. Figuring out how to help people be the best versions of themselves is fun. Now I’ve become the one insisting that people apply to faculty positions after the deadline. I guess I’ve passed that baton.”