Digital Twin for Chemical Science: From months of analysis to minutes of insight
Berkeley Lab's new Digital Twin for Chemical Science (DTCS) is an AI-driven platform that compresses chemical characterization from weeks or months to minutes. It mirrors ambient-pressure X-ray photoelectron spectroscopy (APXPS) experiments in software, so you can observe reactions, adjust parameters, and validate hypotheses during a single run.
For researchers in energy storage, catalysis, and materials science, this means faster iteration, fewer dead ends, and decisions grounded in real-time feedback.
Key takeaways
- DTCS creates a digital replica of APXPS, enabling real-time interpretation of interfacial chemistry on working devices (e.g., batteries).
- The platform closes the loop: observe reactions, tweak conditions, and validate models within the same experiment.
- AI-guided analysis provides rapid feedback on what to measure next, cutting down trial-and-error.
- Immediate applications span energy storage, catalysis, corrosion, and broader materials discovery.
What DTCS is - and why it matters
Traditional workflows separate hypothesis, measurement, modeling, and validation into long, sequential steps. Valuable time is spent reconciling simulations with spectra after the fact. DTCS collapses that cycle.
By pairing live APXPS data with physics-based simulations and machine learning, the platform interprets evolving spectra in context. You don't just see peaks; you see the likely mechanism behind them and how it's changing over time.
How it works: Two loops, one experiment
DTCS runs on two connected pathways during an experiment:
- Forward loop: It generates simulated spectra from candidate mechanisms and matches them against real-time APXPS observations.
- Inverse loop: It infers the most probable mechanisms and kinetic parameters that explain the live data, then recommends what to probe next.
The result: on-the-fly optimization of beam time and experimental conditions, informed by models that update as the reaction unfolds.
What this adds to APXPS
APXPS is a workhorse for interfacial chemistry: it identifies species by their spectral fingerprints as reactions proceed under realistic conditions. The challenge has been using those spectra in real time to reason about which species are present, their concentrations, and how they interact at the surface.
DTCS bridges that gap. By continuously comparing experimental spectra with theory, it estimates species concentrations, chemical potentials, and the likelihood of specific local configurations at the interface - while the experiment is still running.
Validation: The silver/water interface
The team tested DTCS on a fundamental catalytic system: a silver/water interface relevant to batteries, catalysis, and corrosion science. The platform's predictions matched established experiments and theory.
Within minutes, DTCS identified when and where oxygen-containing species appeared on the silver surface and how their concentration profiles evolved. That sped up hypothesis testing and let researchers adjust the plan mid-experiment instead of waiting for post-run analysis.
Why this is useful for your lab
- Better use of beam time: Prioritize the next measurement based on model-informed value, not guesswork.
- Faster mechanism discovery: Move from "what is this peak?" to "which pathway produced it - and what happens if we change conditions?"
- Tighter theory-experiment loop: Validate or refine models in hours, not quarters.
- Scalable insights: Apply the same approach to batteries, fuel cells, catalytic surfaces, and corrosion systems.
What's under the hood
DTCS integrates facility-specific APXPS configurations with physics-based simulations and AI models trained on experimental data. It leverages high-performance computing to keep simulations and inference in step with instrument throughput.
The platform has been developed and hosted using resources that connect supercomputer-generated theoretical data with live experimental streams, enabling near real-time decision support.
Where this goes next
DTCS 2.0 is in development for broader community use with expanded training data. The team is also building digital twins for Raman and infrared spectroscopy to complement APXPS by probing chemical bonds and vibrational modes.
Taken together, these capabilities point to AI-guided, autonomous characterization - a path to discovering and optimizing materials with far fewer experimental cycles.
Practical steps to get ready
- Data plumbing: Ensure your APXPS (and related instruments) can stream time-stamped, calibrated spectra with metadata.
- Model priors: Assemble mechanism libraries and initial kinetic ranges for your systems of interest.
- Compute access: Line up GPU/CPU resources for real-time simulations and inference.
- Experiment strategy: Predefine decision rules for parameter changes based on model confidence and expected information gain.
- Governance: Log decisions, versions, and uncertainty to maintain reproducibility and traceability.
Context and facilities
DTCS enhances capabilities at major user facilities such as the Advanced Light Source, where APXPS has driven two decades of innovation in surface and interface science. That ecosystem - instrument innovation plus AI-integrated analysis - is what makes minute-scale interpretation feasible.
For background on the facilities and programs supporting this work, see the U.S. Department of Energy Office of Science and the Advanced Light Source.
If you're building capability
Integrating AI into characterization workflows takes both domain knowledge and practical training. For applied guidance on bringing AI into experimental design and analysis, explore our AI Learning Path for Research Scientists.
Bottom line
DTCS brings real-time, model-informed decision-making to chemical characterization. For researchers working on batteries, catalysts, and new materials, that means tighter loops, clearer mechanisms, and more impact per experiment.
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