9 ways AI is advancing science

Last updated: November 22, 2024

We’re living in a time when applied science, human ingenuity and new technologies are offering deep insights into some of humanity’s biggest (and oldest) questions. While we often think of scientific progress as fast and unrelenting, for many decades, progress has actually slowed. While the scientific community continues to debate the cause of this slowdown, much of today's technology — from jets to manufacturing processes — is not significantly different than half a century ago.

But in just the past few years, breakthroughs in formerly nascent fields like artificial intelligence and quantum computing have dramatically accelerated the pace of scientific discovery. And from healthcare advances to finding plastic-eating enzymes, we’re already benefiting from it.

These breakthroughs are built on decades of collaboration between researchers, technologists, policymakers, civil organizations and many people from across society. And they offer a blueprint for how applying AI to science can dramatically improve human life.

It’s with this in mind that today The Royal Society in partnership with Google DeepMind is cohosting the first AI for Science Forum. This event in London brings together the scientific community, policymakers, and industry leaders to look at the transformative potential of AI to accelerate science and the role of public-private partnerships in innovation.

To explore how we got here and where we can go next, here’s a look at nine recent moments that have set the stage for so much of the scientific progress on the horizon:

1. Cracking the 50-year “grand challenge” of protein structure prediction

Experts have described demystifying protein folding as a "grand challenge" for decades. In 2022, Google DeepMind shared the predicted structures of 200 million proteins from their AlphaFold 2 model. Previously, determining the 3D structure of a single protein typically took a year or more — AlphaFold can predict these shapes with remarkable accuracy in minutes. By releasing the protein structure predictions in a free database, this has enabled scientists around the world to accelerate progress in areas like developing new medicines, fighting antibiotic resistance and tackling plastic pollution. As a next step, the AlphaFold 3 model builds on AlphaFold 2 to predict the structure and interaction of all of life’s molecules.

2. Showing the human brain in unprecedented detail, to support health research

Few things have held more mystery throughout time than the human brain. Developed over 10 years of connectomics research, Google partnered with others, including the the Lichtman Lab at Harvard, to map a tiny piece of the human brain to a level of detail never previously achieved. This project, released in 2024, revealed never-before-seen structures within the human brain. And the full dataset, including AI-generated annotations for each cell, has been made publicly available to help accelerate research.

3. Saving lives with accurate flood forecasting

When Google’s flood forecasting project began in 2018, many believed it was impossible to accurately deliver flood forecasting at scale, given the scarcity of data. But researchers were able to develop an AI model that achieves reliability in predicting extreme riverine events in ungauged watersheds at up to a five-day lead time with reliability matching or exceeding that of nowcasts (zero-day lead time). In 2024, Google Research expanded this coverage to 100 countries and 700 million people worldwide — and improved the AI model so it offers the same accuracy at a seven-day lead time as the previous model had at five.

4. Spotting wildfires earlier to help firefighters stop them faster

Wildfires are increasingly upending communities around the world due to hotter and drier climates. In 2024, Google Research partnered with the U.S. Forest Service to develop FireSat, an AI model and new global satellite constellation designed specifically to detect and track wildfires the size of a classroom by providing higher-resolution imagery within 20 minutes. This will allow fire authorities to respond more quickly, potentially saving lives, property and natural resources.

5. Predicting weather faster and with more accuracy

In 2023, Google DeepMind launched and open sourced the model code for GraphCast, a machine learning research model that predicts weather conditions up to 10 days in advance more accurately and much faster than the industry gold-standard weather simulation system (HRES). GraphCast can also predict the tracks of cyclones (and associated risks like flooding) with greater accuracy, and accurately predicted Hurricane Lee would hit Nova Scotia three days before traditional models.

6. Advancing the frontier of mathematical reasoning

AI has always struggled with complex math due to a lack of data and reasoning skills. Then, in 2024, Google DeepMind announced AlphaGeometry, an AI system that solved complex geometry problems at a level approaching a human Olympiad gold-medalist — a breakthrough in AI performance and the pursuit of more advanced general AI systems. The subsequent Gemini-trained model, AlphaGeometry 2, was then combined with a new model AlphaProof, and together they solved 83% of all historical International Mathematical Olympiad (IMO) geometry problems from the past 25 years. In demonstrating AI’s growing ability to reason, and potentially solve problems beyond current human abilities, this moved us closer to systems that can discover and verify new knowledge.

7. Using quantum computing to accurately predict chemical reactivity and kinetics

Google researchers worked with UC Berkeley and Columbia University to perform the largest chemistry simulations to date on a quantum computer. The results, published in 2022, were not only competitive with classical methods, but they also did not require the burdensome error mitigation typically associated with quantum computing. The ability to conduct these simulations will offer even more accurate predictions of chemical reactivity and kinetics, which is a precursor for applying chemistry in new ways to help solve real-world challenges.

8. Accelerating materials science and the potential for more sustainable solar cells, batteries and superconductors

In 2023, Google DeepMind announced Graph Networks for Materials Exploration (GNoME), a new AI tool that has already discovered 380,000 materials that are stable at low temperatures, according to simulations. At a time when our world is looking for new approaches to energy, processing power and materials science, this work could pave the way to better solar cells, batteries and potential superconductors. Plus, to help this technology benefit everyone, Google DeepMind made GNoME’s most stable predictions available via the Materials Project on their open database.

9. Taking a meaningful step toward nuclear fusion — and abundant clean energy

As the old joke goes, “Fusion is the energy of the future — and it always will be.” Controlling and using the energy that fuels stars (including our own sun) has been beyond the realm of science. In 2022, Google DeepMind announced that it developed AI that can control the plasma inside a nuclear fusion reactor autonomously. By collaborating with the Swiss Plasma Center at EPFL, Google DeepMind built the first Reinforcement Learned system capable of autonomously stabilizing and shaping the plasma within an operational fusion reactor, opening up new pathways toward stable fusion and abundant clean energy for everyone.