Quantitative modeling of acid-base equilibria
Abstract
Simulating acid-base equilibria from first principles is a tremendous challenge, as it requires modeling bond-breaking events in a complex hydrogen-bonding environment. The quantum mechanical behavior of the protons is often important, complicating simulations even further. We show that by combining state-of-the-art machine-learning potentials, accelerated sampling of the dissociation process, and an efficient estimation of nuclear quantum corrections it is possible to reliably compute the dissociation constants of different species in water. We demonstrate the transferability of this workflow, applying it successfully to the autoionization of water and to the entire ladder of the aqueous dissociation of phosphoric acid. The definition of the dissociation constant, the choice of order parameters, sampling artifacts and finite-size effects, as well as quantum nuclear effects all play a non-trivial role in determining the outcome of the calculation, so that cutting corners is likely to introduce biases that can only be compensated by fortuitous cancellations of errors.
Related
- Thermodynamic and Molecular Characterization of Adsorption on Zeolites: A Unified Framework Combining Inverse Gas Chromatography, Hamaker Theory, and Nonlinear Lewis Acid–Base Modeling
- Bicarbonate/carbonic acid equilibria in aqueous solutions: Conformational Dynamics, Ion-Pair Speciation, and Free Energy Landscapes from Ab Initio Molecular Dynamics and Metadynamics
- The Importance of Base and Solvent in the Ni-Mediated Decarboxylation of Pentafluorobenzoic Acid
- An Unexpected Application of the Negative Root Arising from the Use of the Systematic Treatment of Equilibrium in Calculating pH Contribution from the Autoprotolysis of Water in Highly Diluted Strong Acid or Base Solutions
- An Unexpected Application of the Negative Root Arising from the Use of the Systematic Treatment of Equilibrium in Calculating pH contribution from the Autoprotolysis of Water in Highly Diluted Strong Acid or Base Solutions
BibTeX
@misc{xu2026quantitative,
title = {{Quantitative modeling of acid-base equilibria}},
author = {Qianjun Xu and Simin Liu and Cesare Malosso and Philip Loche and Michele Ceriotti},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.13265},
eprint = {2610.13265},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.13265},
}
RIS
TY - UNPB
TI - Quantitative modeling of acid-base equilibria
AU - Qianjun Xu
AU - Simin Liu
AU - Cesare Malosso
AU - Philip Loche
AU - Michele Ceriotti
PY - 2026
DA - 2026/10/09
PB - arXiv
DO - 10.48550/arxiv.2610.13265
UR - https://arxiv.org/abs/2610.13265
AB - Simulating acid-base equilibria from first principles is a tremendous challenge, as it requires modeling bond-breaking events in a complex hydrogen-bonding environment. The quantum mechanical behavior of the protons is often important, complicating simulations even further. We show that by combining state-of-the-art machine-learning potentials, accelerated sampling of the dissociation process, and an efficient estimation of nuclear quantum corrections it is possible to reliably compute the dissociation constants of different species in water. We demonstrate the transferability of this workflow, applying it successfully to the autoionization of water and to the entire ladder of the aqueous dissociation of phosphoric acid. The definition of the dissociation constant, the choice of order parameters, sampling artifacts and finite-size effects, as well as quantum nuclear effects all play a non-trivial role in determining the outcome of the calculation, so that cutting corners is likely to introduce biases that can only be compensated by fortuitous cancellations of errors.
ER -