Accelerating science with AI and simulations

For more than a decade, MIT Associate Professor Rafael Gómez-Bombarelli has been at the forefront of using artificial intelligence to revolutionize materials science. What began as a niche intersection of chemistry and computation has now blossomed into a transformative movement that promises to reshape how humanity discovers and develops the building blocks of modern technology.

Now, as the newly tenured professor in materials science and engineering surveys the landscape of 2026, he sees something profound taking shape: AI is poised to transform science in ways never before possible. His work at MIT and beyond is devoted to accelerating that future.

“We’re at a second inflection point,” Gómez-Bombarelli says, his voice carrying the weight of someone who has witnessed the evolution firsthand. “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.”

The Promise of AI-Driven Discovery

Gómez-Bombarelli’s vision extends far beyond incremental improvements to existing methods. He envisions a future where AI systems can reason across multiple domains simultaneously—understanding natural language scientific literature, predicting material structures, and optimizing synthesis recipes with unprecedented accuracy.

“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,” he explains. This convergence, he believes, will unlock scientific discoveries at a pace that would have seemed impossible just a few years ago.

His research combines physics-based simulations with cutting-edge approaches like machine learning and generative AI to discover new materials with promising real-world applications. The results speak for themselves: his work has already led to breakthrough materials for batteries, catalysts, plastics, and organic light-emitting diodes (OLEDs).

But Gómez-Bombarelli isn’t content to remain in the ivory tower of academia. He has co-founded multiple companies and served on scientific advisory boards for startups applying AI to drug discovery, robotics, and more. His latest venture, Lila Sciences, is working to build what he calls a “scientific superintelligence platform” for the life sciences, chemical, and materials science industries.

From Chemistry Olympics to AI Pioneer

Gómez-Bombarelli’s journey to becoming one of the world’s leading voices in AI for science began in Spain, where he grew up with an early fascination for the physical sciences. In 2001, he won a Chemistry Olympics competition—an achievement that set him on an academic track in chemistry. He studied as an undergraduate at his hometown college, the University of Salamanca, and stayed for his PhD, where he initially investigated the function of DNA-damaging chemicals through traditional experimental methods.

“My PhD started out experimental, and then I got bitten by the bug of simulation and computer science about halfway through,” he recalls. “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.”

This transition from wet lab to computational science would prove pivotal. Next, Gómez-Bombarelli went to Scotland for a postdoctoral position, where he studied quantum effects in biology. Through that work, he connected with Alán Aspuru-Guzik, a chemistry professor at Harvard University, whom he joined for his next postdoc in 2014.

The Early Days of Deep Learning for Science

It was at Harvard that Gómez-Bombarelli found himself at the bleeding edge of a revolution. “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,” he says. “It was the early, early days of deep learning for science.”

During this period, Gómez-Bombarelli also began working to eliminate manual parts of molecular simulations to run more high-throughput experiments. He and his collaborators ended up running hundreds of thousands of calculations across materials, discovering hundreds of promising materials for testing—a scale of discovery that would have been impossible through traditional methods.

After two years in the lab, Gómez-Bombarelli and Aspuru-Guzik started a general-purpose materials computation company, which eventually pivoted to focus on producing organic light-emitting diodes. Gómez-Bombarelli joined the company full-time and calls it the hardest thing he’s ever done in his career.

“It was amazing to make something tangible,” he reflects. “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.”

The MIT Chapter Begins

In 2018, Aspuru-Guzik suggested Gómez-Bombarelli apply for a new position in MIT’s Department of Materials Science and Engineering. But, with his trepidation about a faculty job, Gómez-Bombarelli let the deadline pass. What happened next would change the trajectory of his career.

Aspuru-Guzik confronted him in his office, slammed his hands on the table, and told him, “You need to apply for this.” It was enough to get Gómez-Bombarelli to put together a formal application.

Fortunately at his startup, Gómez-Bombarelli had spent a lot of time thinking about how to create value from computational materials discovery. During the interview process, he says, he was attracted to the energy and collaborative spirit at MIT. He also began to appreciate the research possibilities.

“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.”

Building a New Kind of Research Lab

It has been nine years since Gómez-Bombarelli joined MIT. Today his lab focuses on how the composition, structure, and reactivity of atoms impact material performance. He has also used high-throughput simulations to create new materials and helped develop tools for merging deep learning with physics-based modeling.

“Physics-based simulations make data and AI algorithms get better the more data you give them,” Gómez-Bombarelli explains. “There are all sorts of virtuous cycles between AI and simulations.”

The research group he has built is solely computational—a deliberate choice that offers unique advantages. “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 is also still focused on the real-world applications of the materials he invents. His lab works closely with companies and organizations like MIT’s Industrial Liaison Program to understand the material needs of the private sector and the practical hurdles of commercial development.

The Consensus View Emerges

As excitement around artificial intelligence has exploded, Gómez-Bombarelli has seen the field mature dramatically. Companies like Meta, Microsoft, and Google’s DeepMind now regularly conduct physics-based simulations reminiscent of what he was working on back in 2016. In November, the U.S. Department of Energy launched the Genesis Mission to accelerate scientific discovery, national security, and energy dominance using AI.

“AI for simulations has gone from something that maybe could work to a consensus scientific view,” Gómez-Bombarelli observes. “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.”

A Culture of Collaboration

When he first came to MIT, Gómez-Bombarelli says he was blown away by how non-competitive things were between researchers. He tries to bring that same positive-sum thinking to his research group, which is made up of about 25 graduate students and postdocs.

“We’ve naturally grown into a really diverse group, with a diverse set of mentalities,” he 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.”

Looking Ahead: The Future of Scientific Discovery

For Gómez-Bombarelli, the work is about more than academic achievement or commercial success. “AI for science is one of the most exciting and aspirational uses of AI,” he says. “Other applications for AI have more downsides and ambiguity. AI for science is about bringing a better future forward in time.”

As we stand at this second inflection point he describes, the implications are staggering. Materials that could enable better batteries for electric vehicles, more efficient catalysts for industrial processes, biodegradable plastics to address environmental concerns, and advanced OLEDs for next-generation displays—all discovered not through years of trial-and-error experimentation, but through AI systems that can reason across the vast landscape of possible materials.

The journey from a young chemistry student in Spain to a tenured professor at MIT leading the charge in AI-driven materials discovery is a testament to the transformative power of following one’s curiosity. For Rafael Gómez-Bombarelli, the destination has always been less important than the journey of discovery itself.

And as AI continues to evolve, that journey is only accelerating.

This article was reported by the ArtificialDaily editorial team. For more information about Professor Gómez-Bombarelli’s research, visit the MIT Department of Materials Science and Engineering.