Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Research Briefing
  • Published:

A digital twin that interprets and refines chemical mechanisms

An integrated platform, Digital Twin for Chemical Science (DTCS), is developed to connect first-principles theory with spectroscopic measurements through a bidirectional feedback loop. By predicting and refining chemical reaction mechanisms before, during and after experiments, DTCS enables the interpretation of spectra and supports real-time decision-making in chemical characterization.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Overall workflow for DTCS.

References

  1. Angeli, D. A tutorial on chemical reaction network dynamics. Eur. J. Control. 15, 398–406 (2009). This paper introduces the mathematical foundations of CRN modeling.

    Article  MathSciNet  MATH  Google Scholar 

  2. Qian, J. et al. Initial steps in forming the electrode–electrolyte interface: H₂O adsorption and complex formation on the Ag(111) surface from combining quantum mechanics calculations and ambient pressure X-ray photoelectron spectroscopy. J. Am. Chem. Soc. 141, 6946–6954 (2019). This paper reports the formation mechanisms of interfacial water complexes on Ag(111), providing theoretical and experimental benchmarks used for DTCS validation.

    Article  Google Scholar 

  3. Xu, Q., dos Anjos Cunha, L., Xin, H., Head-Gordon, M. & Qian, J. Real-space pseudopotential method for the calculation of third-row elements X-ray photoelectron spectroscopic signatures. J. Chem. Theory Comput. 20, 6134–6143 (2024). This paper reports a real-space DFT method for predicting APXPS core-level shifts, enabling key spectral modeling capabilities in the theory twin component of DTCS.

  4. Ko, T. W. & Ong, S. P. Recent advances and outstanding challenges for machine learning interatomic potentials. Nat. Comput. Sci. 3, 998–1000 (2023). A review article that presents the potential and challenges of MLIPs, which could accelerate rate constant estimation in future DTCS workflows.

    Article  Google Scholar 

  5. Hirono, Y., Okada, T., Miyazaki, H. & Hidaka, Y. Structural reduction of chemical reaction networks based on topology. Phys. Rev. Res. 3, 043123 (2021). This paper reports a topological-based strategy for reducing CRN dimensionalities.

    Article  Google Scholar 

Download references

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This is a summary of: Qian, J. et al. Digital Twin for Chemical Science: a case study on water interactions on the Ag(111) surface. Nat. Comput. Sci. https://doi.org/10.1038/s43588-025-00857-y (2025).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

A digital twin that interprets and refines chemical mechanisms. Nat Comput Sci 5, 713–714 (2025). https://doi.org/10.1038/s43588-025-00859-w

Download citation

  • Published:

  • Issue date:

  • DOI: https://doi.org/10.1038/s43588-025-00859-w

Search

Quick links

Nature Briefing AI and Robotics

Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: AI and Robotics