AI Digital Twin Speeds Chemistry Breakthroughs

Berkeley Lab scientists develop Digital Twin for Chemical Science (DTCS) to compress discovery timelines from months to minutes

Published on Feb. 20, 2026

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Berkeley Lab scientists have developed Digital Twin for Chemical Science (DTCS), an AI-powered platform that could compress discovery timelines from months to minutes, enabling researchers to observe chemical reactions, adjust experimental parameters, and validate hypotheses simultaneously during a single experiment. DTCS creates a digital replica of ambient-pressure X-ray photoelectron spectroscopy (APXPS) techniques, allowing real-time analysis of chemical compounds formed on the surface of an operating device such as a battery.

Why it matters

Understanding what complex chemical measurements reveal about materials and reactions can take weeks or months of analysis. DTCS could help overcome this bottleneck and transform chemistry research across energy storage, catalysis, and materials science applications by providing rapid feedback during experiments and enabling data-driven decisions about what to measure next.

The details

DTCS allows researchers to observe chemical reactions, adjust experimental parameters, and validate hypotheses simultaneously during a single experiment. Traditional approaches require researchers to first develop a hypothesis, design an experiment to collect data, develop theoretical models to analyze that data, and then conduct follow-up experiments to validate the model - a process that often takes months. DTCS's 'forward loop' matches simulated spectra with experimental observations, while the 'inverse loop' takes experimental data and solves for the underlying chemical mechanisms, providing real-time insights.

  • The research team is already developing DTCS 2.0, preparing it for broader community use and training its AI algorithms with new data.
  • The researchers expect to make DTCS available to other scientific institutions and user facilities within the next few years.

The players

Jin Qian

A computational chemist and staff scientist in Berkeley Lab's Chemical Sciences Division who designed the DTCS platform.

Ethan Crumlin

A staff scientist at the Advanced Light Source and program lead specializing in interface chemistry and characterization.

National Energy Research Scientific Computing Center (NERSC)

The mission computing facility for the U.S. Department of Energy Office of Science at Berkeley Lab, which has been instrumental in hosting the DTCS platform.

Advanced Light Source (ALS)

Berkeley Lab's synchrotron X-ray user facility, where the DTCS platform was developed to create a digital replica of ambient-pressure X-ray photoelectron spectroscopy (APXPS) techniques.

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What they’re saying

“A common challenge that many researchers face during complex experiments is that although we have sophisticated tools that collect data, interpreting that data is another beast. Traditionally, we collect as much data as possible, then run simulations to analyze it offline. This back-and-forth process often takes months before theory and experiment reach consensus. DTCS could help overcome this bottleneck.”

— Jin Qian, Computational chemist and staff scientist in Berkeley Lab's Chemical Sciences Division (Mirage News)

“The Digital Twin for Chemical Science platform represents a new capability for Berkeley Lab's Advanced Light Source and DOE's scientific user facilities. The idea of partnering with a computational, machine-learning construct will be the future for how science is done.”

— Ethan Crumlin, Deputy for Science in the Chemical Sciences Division and Advanced Light Source staff scientist (Mirage News)

What’s next

The research team is already developing DTCS 2.0, preparing it for broader community use and training its AI algorithms with new data. They're also building digital twins for other analytical techniques including Raman and infrared spectroscopy, which complement APXPS by providing information about chemical bonds.

The takeaway

DTCS represents a significant step toward autonomous chemical characterization, where AI-guided experiments could accelerate the timeline for discovering and characterizing new materials and chemical processes for useful applications across energy storage, catalysis, and manufacturing.