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When theory came first: a review of theoretical chemical predictions ahead of experiments

  • Mario Barbatti ORCID logo EMAIL logo
Veröffentlicht/Copyright: 26. Mai 2025

Abstract

For decades, computational theoretical chemistry has provided critical insights into molecular behavior, often anticipating experimental discoveries. This review surveys twenty notable examples from the past fifteen years in which computational chemistry successfully predicted molecular structures, reaction mechanisms, and material properties before experimental confirmation. By spanning fields such as bioinorganic chemistry, materials science, catalysis, and quantum transport, these case studies illustrate how quantum chemical methods have become essential for multidisciplinary molecular sciences. The impact of theoretical predictions across disciplines shows the indispensable role of computational chemistry in guiding experiments and driving scientific discovery.


Corresponding author: Mario Barbatti, Aix Marseille University, CNRS, ICR, 13397 Marseille, France; and Institut Universitaire de France, 75231 Paris, France, e-mail:
Article note: A collection of invited papers to celebrate the UN’s proclamation of 2025 as the International Year of Quantum Science and Technology.

Award Identifier / Grant number: AMIDEX AMX-22-REAB-173

Award Identifier / Grant number: AMX-22-IN1-48

Award Identifier / Grant number: grant agreement 832237

Acknowledgments

I’m thankful to W. T. Borden, G. A. Cisneros, J. M. Cuerva, D. Dave, T. P. Fay, R. C. Fortenberry, I. Funes-Ardoiz, M. H. Garner, A. Gorden, D. G. Green, M. Huix-Rotllant, J. H. Jensen, C. Laconsay, P.-O. Norrby, G. M. Pavan, R. Poranne, R. Ramakrishnan, R. Rana, T. Santaloci, G. C. Solomon, B. Space, T. Stuyver, M. Swart, J. M. Toldo, M. Torrent-Sucarrat, R. G. Uceda, A. H. Winter, and A. Zaccone, who gave feedback on the manuscript and pointed me out to many relevant works for this review.

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: The author has accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The author states no conflict of interest.

  6. Research funding: This work received support from the French government under the France 2030 investment plan as part of the Initiative d’Excellence d’Aix-Marseille Université (A*MIDEX AMX-22-REAB-173 and AMX-22-IN1-48) and from the European Research Council (ERC) Advanced Grant SubNano (grant agreement 832237).

  7. Data availability: Not applicable.

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Received: 2025-03-07
Accepted: 2025-05-06
Published Online: 2025-05-26
Published in Print: 2025-09-25

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