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Nitrous acid and nitric acids are two types of acids containing nitrogen, among which nitrous acid is weaker than nitric acid. Nitrous acid with a pKa value of 3.37 ionizes in water to give a nitrite ion and the hydronium ion.
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Modeling the behavior of concentrated aqueous HNO3 using machine learning interatomic potentials.

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|January 6, 2026
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Summary
This summary is machine-generated.

We developed machine learning potentials for nitric acid simulations, accurately predicting its acidity and structural properties across various concentrations. These potentials offer a faster and more accurate alternative to existing models for chemical research.

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Area of Science:

  • Computational Chemistry
  • Materials Science
  • Chemical Physics

Background:

  • Investigating the behavior of nitric acid (HNO3) across different concentrations is crucial for understanding its chemical properties.
  • Existing computational models often struggle to accurately capture the complex structural and thermodynamic properties of nitric acid, especially concerning its dissociation.
  • The development of accurate and efficient interatomic potentials is essential for molecular dynamics simulations.

Purpose of the Study:

  • To develop and validate multi-defect machine learning interatomic potentials (MLIPs) for nitric acid using the DeepMD-kit.
  • To investigate the structural and thermodynamic properties of nitric acid over a wide range of concentrations using molecular dynamics (MD) simulations.
  • To compare the performance of custom DeepMD MLIPs against foundational models like MACE-MP0 and MACE-OFF23.

Main Methods:

  • Training two multi-defect MLIPs using BLYP-D2 and PBE-D3 density functional theories with DeepMD-kit.
  • Performing MD simulations to investigate structural and thermodynamic properties, including the degree of dissociation (α) and pKa.
  • Benchmarking custom DeepMD MLIPs against MACE-MP0 and MACE-OFF23, and comparing with classical force fields (FFs).

Main Results:

  • The developed MLIPs accurately predict the degree of dissociation (α) and pKa of nitric acid, showing good agreement with experimental data.
  • HNO3 exhibits weaker acid behavior at higher concentrations, with a standard-state pKa matching experimental values.
  • Custom DeepMD MLIPs provide more compact solvation shells, reproduce density-concentration trends, and are significantly faster than MACE-MP0, outperforming foundational models in capturing subtle structural features.

Conclusions:

  • Bespoke, reactive MLIPs are necessary for accurately simulating chemical reactivity and properties like dissociation, going beyond universal MLIPs.
  • The developed DeepMD MLIPs offer a significant advancement for studying concentrated nitric acid systems, providing accurate predictions and improved computational efficiency.
  • While classical FFs are efficient for density, they lack the chemical reactivity crucial for predicting dissociation and acidity, highlighting the value of reactive MLIPs.