Benchmark of Approximate Quantum Chemical and Machine Learning Potentials for Biochemical Proton Transfer Reactions
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61388963%3A_____%2F25%3A00637596" target="_blank" >RIV/61388963:_____/25:00637596 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1021/acs.jctc.5c00690" target="_blank" >https://doi.org/10.1021/acs.jctc.5c00690</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1021/acs.jctc.5c00690" target="_blank" >10.1021/acs.jctc.5c00690</a>
Alternative languages
Result language
angličtina
Original language name
Benchmark of Approximate Quantum Chemical and Machine Learning Potentials for Biochemical Proton Transfer Reactions
Original language description
Proton transfer reactions are among the most common chemical transformations and are central to enzymatic catalysis and bioenergetic processes. Their mechanisms are often investigated using DFT or approximate quantum chemical methods, whose accuracy directly impacts the reliability of the simulations. Here, a comprehensive set of semiempirical molecular orbital and tight-binding DFT approaches, along with recently developed machine learning (ML) potentials, are benchmarked against high-level MP2 reference data for a curated set of proton transfer reactions representative of biochemical systems. Relative energies, geometries, and dipole moments are evaluated for isolated reactions. Microsolvated reactions are also simulated using a hybrid QM/MM partition. Traditional DFT methods offer high accuracy in general but show markedly larger deviations for proton transfers involving nitrogen-containing groups. Among approximate models, RM1, PM6, PM7, DFTB2-NH, DFTB3, and GFN2-xTB show reasonable accuracy across properties, though their performance varies by chemical group. The ML-corrected (Delta-learning) model PM6-ML improves accuracy for all properties and chemical groups and transfers well to QM/MM simulations. Conversely, standalone ML potentials perform poorly for most reactions. These results provide a basis for evaluating approximate methods and selecting potentials for proton transfer simulations in complex environments.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10403 - Physical chemistry
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Journal of Chemical Theory and Computation
ISSN
1549-9618
e-ISSN
1549-9626
Volume of the periodical
21
Issue of the periodical within the volume
14
Country of publishing house
US - UNITED STATES
Number of pages
11
Pages from-to
7149-7159
UT code for WoS article
001520227300001
EID of the result in the Scopus database
2-s2.0-105009531759