Benchmarking on-the-Fly Machine Learning Force Fields for Ion Hydration: Structure, Coordination, and Exchange Dynamics across Monovalent and Divalent Cations.
- 1Department of Chemistry, School of Physical and Chemical Sciences, Queen Mary University of London, Mile End Road, LondonE1 4NS, U.K.
Abstract
Machine learning force fields (MLFFs) promise ab initio accuracy at a fraction of the computational cost of first-principles simulation, yet their fidelity for aqueous ion hydration across chemically diverse cation series remains systematically underexplored. Here, we benchmark an on-the-fly MLFF (OT-MLFF) framework, implemented in VASP via kernel-based Bayesian regression with adaptive DFT calls triggered when the estimated force error exceeds a set threshold, together with the pretrained, equivariant MACE foundation model applied off-the-shelf, against ab initio molecular dynamics (AIMD; CP2K, PBE-D3) and classical MD (CMD; Madrid-2019/TIP4P-2005) for aqueous solutions of Li+, Na+, K+, Cs+, Mg2+, and Ca2+. Across all six ions, the OT-MLFF reproduces a bulk water structure and the systematic increase of first-shell M-O distances down both cation series with near-AIMD accuracy, outperforming CMD. Coordination-number distributions reveal a mild OT-MLFF overcoordination of Li+ (modal CN 5 versus 4 in both AIMD and MACE) and Cs+, whereas the two divalent shells, including the previously problematic Ca2+, are reproduced by both machine-learned potentials, all three methods placing the Ca2+ coordination number at ≈6. MACE tracks AIMD most closely overall, its principal bias being a residual overcoordination of K+ and undercoordination of Cs+ in the diffuse monovalent shells. Angular distribution functions confirm accurate reproduction of the rigid octahedral geometry of Mg2+ but reveal systematic OT-MLFF smoothing of three-body features for more labile shells, including the O-Na-O shoulder near 65-70° and the O-Ca-O peak angle. The largest failures are kinetic: the OT-MLFF generates spurious first-shell water exchange for Mg2+, absent in AIMD, and completely suppresses the exchange of the dynamic Ca2+ shell, even though it reproduces the equilibrium coordination number of both ions, a clear dissociation between structural and dynamical fidelity. Velocity autocorrelation functions reveal a related failure, the OT-MLFF underestimating the cooperative cage confinement of the large monovalent ions Cs+ and K+. In each of these cases, the pretrained MACE model recovers the AIMD behavior that the on-the-fly potential misses. Collectively, these results establish that the OT-MLFF is robust for an equilibrium solvation structure but fails for three-body angular sensitivity and exchange kinetics in chemically distinct edge cases and that a deeper, higher-body-order, pretrained model such as MACE systematically narrows these gaps even without system-specific training. Targeted improvements such as augmented transition-state sampling, higher angular-resolution descriptors, and long-range electrostatic corrections are identified as high-priority directions for kinetic fidelity in next-generation electrolyte force fields.
Related
- Synthesis and crystal structure of a one-dimensional Hg(II) coordination polymer with two distinct metal centers and its selective fluorescent sensing of Ni²⁺
- Design and Benchmarking of CK2α′-Preferring Inhibitors for Huntington’s Disease: The Limits of Structure-Based Isoform Selectivity
- Mercury(II) Coordination Compounds Based on Pyridine-2,5-Dicarboxylic Acid: Synthesis, Crystal Structure, Morphological Characterization, and Biological Activities.
- Comparative Analysis of Basic Machine Learning and Rigorous Free-Energy Calculation on Benchmarking Datasets of Protein-Ligand Binding Affinity
- Two-Dimensional C60 Fullerene Networks (Graphullerene): Structure–Property Relations, Quantitative Benchmarking and Future Directions
BibTeX
@article{salha2026benchmarking,
title = {{Benchmarking on-the-Fly Machine Learning Force Fields for Ion Hydration: Structure, Coordination, and Exchange Dynamics across Monovalent and Divalent Cations.}},
author = {Mohammed S Salha and Wuyang Lin and Gregory Chass and Devis Di Tommaso},
journal = {The journal of physical chemistry. B},
year = {2026},
month = sep,
doi = {10.1021/acs.jpcb.6c02837},
url = {https://europepmc.org/article/MED/42915327},
note = {CC BY},
}
RIS
TY - JOUR
TI - Benchmarking on-the-Fly Machine Learning Force Fields for Ion Hydration: Structure, Coordination, and Exchange Dynamics across Monovalent and Divalent Cations.
AU - Mohammed S Salha
AU - Wuyang Lin
AU - Gregory Chass
AU - Devis Di Tommaso
PY - 2026
DA - 2026/09/01
JO - The journal of physical chemistry. B
DO - 10.1021/acs.jpcb.6c02837
UR - https://europepmc.org/article/MED/42915327
AB - Machine learning force fields (MLFFs) promise ab initio accuracy at a fraction of the computational cost of first-principles simulation, yet their fidelity for aqueous ion hydration across chemically diverse cation series remains systematically underexplored. Here, we benchmark an on-the-fly MLFF (OT-MLFF) framework, implemented in VASP via kernel-based Bayesian regression with adaptive DFT calls triggered when the estimated force error exceeds a set threshold, together with the pretrained, equivariant MACE foundation model applied off-the-shelf, against ab initio molecular dynamics (AIMD; CP2K, PBE-D3) and classical MD (CMD; Madrid-2019/TIP4P-2005) for aqueous solutions of Li+, Na+, K+, Cs+, Mg2+, and Ca2+. Across all six ions, the OT-MLFF reproduces a bulk water structure and the systematic increase of first-shell M-O distances down both cation series with near-AIMD accuracy, outperforming CMD. Coordination-number distributions reveal a mild OT-MLFF overcoordination of Li+ (modal CN 5 versus 4 in both AIMD and MACE) and Cs+, whereas the two divalent shells, including the previously problematic Ca2+, are reproduced by both machine-learned potentials, all three methods placing the Ca2+ coordination number at ≈6. MACE tracks AIMD most closely overall, its principal bias being a residual overcoordination of K+ and undercoordination of Cs+ in the diffuse monovalent shells. Angular distribution functions confirm accurate reproduction of the rigid octahedral geometry of Mg2+ but reveal systematic OT-MLFF smoothing of three-body features for more labile shells, including the O-Na-O shoulder near 65-70° and the O-Ca-O peak angle. The largest failures are kinetic: the OT-MLFF generates spurious first-shell water exchange for Mg2+, absent in AIMD, and completely suppresses the exchange of the dynamic Ca2+ shell, even though it reproduces the equilibrium coordination number of both ions, a clear dissociation between structural and dynamical fidelity. Velocity autocorrelation functions reveal a related failure, the OT-MLFF underestimating the cooperative cage confinement of the large monovalent ions Cs+ and K+. In each of these cases, the pretrained MACE model recovers the AIMD behavior that the on-the-fly potential misses. Collectively, these results establish that the OT-MLFF is robust for an equilibrium solvation structure but fails for three-body angular sensitivity and exchange kinetics in chemically distinct edge cases and that a deeper, higher-body-order, pretrained model such as MACE systematically narrows these gaps even without system-specific training. Targeted improvements such as augmented transition-state sampling, higher angular-resolution descriptors, and long-range electrostatic corrections are identified as high-priority directions for kinetic fidelity in next-generation electrolyte force fields.
SN - 1520-6106
SN - 1520-5207
ER -