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
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$32.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
$119.00 per year
only $9.92 per issue
Buy this article
- Purchase on SpringerLink
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout

References
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.
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.
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.
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.
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.
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
About this article
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
Published:
Issue date:
DOI: https://doi.org/10.1038/s43588-025-00859-w