Unveiling the projection nature of solvation interactions yields a robust PMPA-MD method for efficient modeling of liquid-phase reactions: A case study on H2PtCl6 hydrolysis

While practically essential yet technically challenging, the lack of understanding of the nature of solvation interactions hinders the development of accurate and universal simulation methods for liquid-phase reactions. Here, we introduce an innovative concept of projecting intermolecular solvation interactions onto each atom/species, analogous to the way of quantifying covalent bonding interactions, and thereby developing a powerful tool of the Projective Multi-Point Averaging Molecular Dynamics (PMPA-MD), realizing consistent treatment of diverse solvation scenarios covering both liquid/solid interfaces and homogeneous solution reactions. Taking the multistep H2PtCl6 hydrolysis as an example, PMPA-MD demonstrates good accuracy with slight energy deviations <0.1 eV and reduces computational costs by approximately one order of magnitude, as compared with the benchmark constrained MD simulations. We show that the process is typically exothermic (except for the first hydrolysis step) and proceeds with surmountable reaction barriers following a Brønsted-Evans-Polanyi relationship. The substitution site is governed by the trans effect, while the substituent group (-OH or -H2O) exhibits piecewise pH dependence. Furthermore, elucidating the dynamic hydrolysis mechanism enables exploring the subsequent process of hydrolysate adsorption and reductive nucleation on the support surface, thereby shedding light on the morphology control of deposited Pt catalysts during experimental synthesis. This work advances both the concept and methodology for liquid-phase studies.

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Publication Details

Journal
The Journal of Chemical Physics
Published
2026-04-01
DOI
https://doi.org/10.1063/5.0324948
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00

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article

Unveiling the projection nature of solvation interactions yields a robust PMPA-MD method for efficient modeling of liquid-phase reactions: A case study on H2PtCl6 hydrolysis

Yi Wang, Fei Li, Xuezhi Duan, Haosheng Niu et al.
The Journal of Chemical Physics
Machine Learning in Materials Science
article

Unveiling the projection nature of solvation interactions yields a robust PMPA-MD method for efficient modeling of liquid-phase reactions: A case study on H2PtCl6 hydrolysis

Yi Wang, Fei Li, Xuezhi Duan, Haosheng Niu, Dong Wang
article en

Abstract

While practically essential yet technically challenging, the lack of understanding of the nature of solvation interactions hinders the development of accurate and universal simulation methods for liquid-phase reactions. Here, we introduce an innovative concept of projecting intermolecular solvation interactions onto each atom/species, analogous to the way of quantifying covalent bonding interactions, and thereby developing a powerful tool of the Projective Multi-Point Averaging Molecular Dynamics (PMPA-MD), realizing consistent treatment of diverse solvation scenarios covering both liquid/solid interfaces and homogeneous solution reactions. Taking the multistep H2PtCl6 hydrolysis as an example, PMPA-MD demonstrates good accuracy with slight energy deviations <0.1 eV and reduces computational costs by approximately one order of magnitude, as compared with the benchmark constrained MD simulations. We show that the process is typically exothermic (except for the first hydrolysis step) and proceeds with surmountable reaction barriers following a Brønsted-Evans-Polanyi relationship. The substitution site is governed by the trans effect, while the substituent group (-OH or -H2O) exhibits piecewise pH dependence. Furthermore, elucidating the dynamic hydrolysis mechanism enables exploring the subsequent process of hydrolysate adsorption and reductive nucleation on the support surface, thereby shedding light on the morphology control of deposited Pt catalysts during experimental synthesis. This work advances both the concept and methodology for liquid-phase studies.

The Journal of Chemical PhysicsVol. 164(13)
East China University of Science and Technology (CN), State Key Laboratory of Chemical Engineering (CN)
National Natural Science Foundation of China, Shanghai Rising-Star Program, Postdoctoral Research Foundation of China
Affordable and clean energy
Openalex Percentile: Top 28%
Machine Learning in Materials Science
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