Empirical Research Assistance (ERA): From Nature publication to catalyzing Computational Discovery
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Empirical Research Assistance (ERA): From Nature publication to catalyzing Computational Discovery
May 19, 2026<br>Lizzie Dorfman, Product Manager, and Michael Brenner, Research Scientist, Google Research
Published today in Nature, Empirical Research Assistance (ERA) is an AI tool for expert-level scientific coding that helped build the Computational Discovery prototype, now available through a trusted tester program in Google Labs.
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Nature paper
Github code
ERA introductory blog post
ERA applications blog post
Gemini for Science
Epidemiology
CO2 mapping
Snow runoff
Solar energy design
Cosmology
Neuroscience
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One of AI’s greatest potential benefits to humanity is increasing the speed and scope of scientific discovery. Empirical Research Assistance (ERA), a Google-developed research tool that uses Gemini to write and optimize scientific code, addresses one of the most time-consuming parts of scientific research: iteratively testing and refining computational experiments. It is described in "AI system designed to help scientists write expert-level empirical software”, published today in the journal Nature.<br>As part of our wider science announcements at I/O today, we are also making this technology accessible as a tool that can begin to help scientists around the world. ERA is one of the systems used to build Computational Discovery, a new experimental tool that is starting to roll out more broadly today through Gemini for Science.
Introducing ERA as a versatile tool for scientific coding
We first shared the design and performance of ERA in the fall, when the preprint was released. Given a scientific problem and a measure of success, ERA can search scientific literature, write code, explore solutions, combine techniques and evaluate the results. ERA considers thousands of options, using a tree search approach to optimize its output code against its given goal.<br>Our Nature publication describes testing ERA on benchmark problems spanning a variety of disciplines: genomics, public health, satellite imagery analysis, neuroscience prediction, a general time-series forecasting benchmark, and mathematics. Results show ERA achieves expert-level performance across all of these benchmarks, potentially democratizing future access to expert-level computational modeling and expanding the capabilities of current experts.
Applying ERA to open scientific questions
Over the past six months, Google Research scientists and our collaborators have been actively experimenting with ERA. In late April, we shared examples of four projects we’d worked on that use ERA to investigate current open problems in science.<br>We now have a total of eight manuscripts that apply ERA to specific scientific problems, including the five newly released papers described below. Collectively, these results show how ERA can help drive progress in several domains with immediate scientific impact and public benefit.
Google scientists and collaborators published an analysis of their work using ERA for epidemiological forecasting, predicting U.S. hospital admissions at a state level up to four weeks in advance for flu, COVID-19 and RSV. ERA forecasts consistently rank at or near the top of public Centers for Disease Control (CDC) leaderboards for all three respiratory viruses, and employ techniques that can easily be replicated for other countries and diseases.
Left : Google’s forecasted weekly hospital admissions across California for flu, COVID-19, and RSV, starting when each forecast began through the end of May. The black line shows observed hospital admissions. Right : The ranking of different models shows that Google’s forecasts (blue ) performed the best for season-averaged accuracy in all three respiratory viruses. CDC’s ensemble forecasts (striped bars) are given a relative Weighted Interval Score of 1. Other research groups’ forecasts are solid red bars (only the best-performing models are shown).
We used ERA to create a forecasting model of seasonal runoff across California's snow‑fed river basins — a vital resource for the state's population and agricultural sector. The resulting model produces significantly more accurate early predictions of spring runoff than Bulletin 120 (B120), the state's official seasonal water supply outlook, potentially improving management of this scarce resource.
We shared new results that use ERA to map atmospheric carbon dioxide (CO2) concentration with unprecedented spatial and temporal resolution using data from a geostationary weather satellite. The ERA-developed model captures changes in CO2 concentration resulting from human activity, including distinct urban enhancements. The model-derived estimates also show how crops and other plants absorb CO2 as they grow, causing CO2 concentration to dip during the day, and how other...