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From the early breakthroughs of the telescope, which expanded the universe; from Schleiden and Schwann recognizing plant cells to the microscope, which revealed the cell; and from Rutherford defining the nucleus of the atom to the particle accelerator, science has often made significant strides through its instruments. This trend continues with the defining instrument of this era: the dataset and its companion, AI. Together, they make for a new laboratory where data is both the substance and the means for discovery.
This is the story that launches with our new series, The Data Frontier of Science, which explores how data-oriented approaches are revitalizing science and engineering. The current article marks the beginning of that series, zooming in on its transition from observation to simulation. It examines examples across a wide range of fields. In analyzing how scientists are learning to trust what their models predict as much as what their tools record, we consider what this shift implies for the future of scientific discovery.
The Changing Nature of Scientific Discovery
With so much data and powerful AI models at their fingertips, researchers are doing more and more of their work inside machines. Across many fields, experiments that once started in a lab now begin on a screen. AI and simulation have flipped the order of discovery. In many cases, the lab has become the final step, not the first.
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You can see this happening in almost every area of science. Instead of testing one idea at a time, researchers now run thousands of simulations to figure out which ones are worth trying in real life. Whether they’re working with new materials, brain models, or climate systems, the pattern is clear: computation has become the proving ground for discovery.
Lawrence Berkeley National Laboratory’s Materials Project was developed to test new compounds through the computer. Scientists run thousands of simulations to see how a material might act instead of mixing chemicals and seeing what happens. The system can predict anything from electrical conductivity to thermal limits to chemical stability. This happens all before it is ever manufactured. Only candidates that seem extremely promising are selected.
The Human Brain Project’s EBRAINS allows scientists to simulate brain circuits—testing how neurons will respond to medications or stimulation without depending on animal studies or highly invasive testing. NVIDIA’s Earth-2 is already being developed to model the effects of climate change with such detail that entire scenarios can be tested thoroughly and quickly.
This isn’t simply a race. It’s not just about more investigations or more chances to fail, but more opportunities to learn. If something fails, it doesn’t waste weeks of labor—it becomes data for the next iteration. The lab isn’t where reseachers try things anymore. It’s where reseachers get answers.
The New Instruments of Science
Data changed how science works at a fundamental level. The guess-and-check rhythm of traditional experimentation has been replaced. Rather than starting from a petri dish, discovery begins with data. Instead of contemplating which hypotheses to test, researchers let the model show the way.
Tools like Open Catalyst, from Meta and Carnegie Mellon, help scientists figure out how molecules might react—before running any lab tests. The system simulates chemical reactions on a computer, which saves time and cuts down on expensive trial-and-error. It’s especially useful for finding better materials for clean energy, like new catalysts for hydrogen fuel or carbon capture.
In the life sciences, DeepMind’s AlphaFold predicts how proteins fold based on their amino acid sequences—something that once required many years of lab work. The results are now used to guide everything from experimental plans to drug targeting, via a public database hosted by EMBL-EBI. For many biologists, AlphaFold is now the first step in their research.
Simulations are also taking over physics, where observation was once untouchable. Scientists use the Aurora supercomputer at Argonne National Lab to simulate conditions that can’t be replicated directly—such as plasma behavior, star formation, or what happened moments after the Big Bang. These aren’t just visualizations—they stand in for real experiments.
The microscope hasn’t vanished. The telescope still counts. But in this new environment, they’re rarely the first tools used. More often than not, the model leads—and the lab follows.
Digital Twins and Synthetic Data: The New Fuel for Discovery
Science used to start with the question: what can we observe? Now it often starts with a different one: what can we simulate?
Across the sciences, the first draft of discovery is no longer happening in a notebook or on a lab bench. It’s happening inside a model. Digital twins—software-based replicas of physical systems—and synthetic datasets are quickly becoming the tools researchers reach for first. They let you rehearse an experiment before reality gets involved. If it doesn’t look promising in simulation? You don’t bother taking it into the real world.
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At NASA’s aero research, this is becoming a standard practice. New aircraft designs don’t go straight into wind tunnels, instead, they live for weeks or months inside computational fluid dynamics simulators. Engineers test how air flows across the wings, how pressure shifts in turbulence, how drag affects lift. If something fails, they tweak it and run it again. Data enables them to not worry about mistakes or wasted materials. By the time they build a prototype, they’ve already watched it fly.
In energy, the same logic plays out underground. Shell and BP model rock formations and pressure systems using synthetic seismic data. They map out virtual wells and simulate how the earth might respond before a single drill touches soil. It’s still science. It’s just the kind that happens first in code.
Even agriculture has gotten in on this shift. Companies like OneSoil and PEAT are building digital fields, like entire farms, virtually recreated from satellite imagery and climate data. They simulate what’ll happen if you plant early, or irrigate less, or skip pesticide altogether. These models aren’t abstract. They’re tied to actual fields, real farmers, real seasons. It’s just that the trials happen in a few seconds, not a few months.
What makes all of this so powerful isn’t just speed or scale. It’s the filtering effect. In the past, the lab was where you started. Now it’s where you go once the simulations give you a reason. The real world hasn’t gone away, but it’s earned a new role of being the validator of the virtual.
The Scientist’s New Role in a Simulated World
Yes, the job’s changing. Scientists aren’t just testing hypotheses or peering into microscopes anymore. More and more, they’re managing systems — trying to stop models from drifting, tracking what changed and when, making sure what comes out actually means something. They’ve gone from running experiments to building the environment where those experiments even happen.
And whether they’re at DeepMind, Livermore, NOAA, or just some research team spinning up models, it’s the same kind of work. They’re checking whether the data is usable, figuring out who touched it last, wondering if the labels are even accurate. AI can do a lot, but it doesn’t know when it’s wrong. It just keeps going. That’s why this still depends on the human in the loop.
They’re still curious. Still chasing insight. But now a big part of the job is just keeping the system honest. Because the model output will look right. It will look clean. But unless you’ve followed every step it took to get there, you can’t be sure it’s real. That call — the gut check — that’s still on you – the human. This is still science. It’s just happening further upstream.
What We Lose and Gain When Reality Becomes Code
There’s a lot you get when science moves into simulation. It’s fast. You can scale ideas like never before. Models don’t get tired. You can run a thousand experiments before you even finish your coffee. You get cleaner outputs, tighter control. On paper, it all looks like progress. And it is.
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However, you lose something too.
When everything happens inside a machine, you don’t get the odd smells, the broken glass, the weird reactions that don’t belong. You lose the little things that used to raise eyebrows in a lab. The gut checks. The accidents that turned into discoveries. Models don’t give you that. They do what they’re told.
So yeah, you gain precision. But you give up a bit of the feel. You get control. But context slips. Reality is messy, but it pushes back. Models don’t. Not unless you make them. You have to tell them where to look. When to stop. What not to trust.
That’s still on the scientist. The tools have changed. The terrain’s different. But the job? Still about knowing when something’s off — even when the numbers look perfect. Especially then.
In the next part of this series, we’re diving into the models — the ones trained on papers, lab data, and decades of scientific work. In the later parts, we’ll look at the infrastructure behind it all, and then the reproducibility problem that’s still haunting AI-powered science research. It all comes back to data — how it’s built, trusted, and used. Subscribe and follow so you don’t miss it.
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