DOE OSTI · 3013819
Modeling the behavior of concentrated aqueous HNO 3 using machine learning interatomic potentials
Abstract
We develop two multi-defect machine learning interatomic potentials (MLIPs) trained at the BLYP-D2 and PBE-D3 density functional theories using the DeepMD-kit, allowing for the investigation of structural and thermodynamic properties of nitric acid over a wide range of concentrations via molecular dynamics (MD) simulations. We directly compute the degree of dissociation, α, and pK a from MD simulations, revealing that HNO 3 behaves as a weaker acid at higher concentrations, noting that our standard-state pK a value is in excellent agreement with the experimental one. In general, good agreement is observed with experimental results such as α and density outside the training dataset, with only modest deviations at low-to-medium concentrations. We benchmark our custom multi-defect DeepMD MLIPs against foundational models MACE-MP0 and MACE-OFF23. The foundation models capture some aspects of HNO 3 /NO 3 − solvation in concentrated nitric acid but show noticeable density errors and miss subtle structural features relevant to spectroscopy, whereas the bespoke DeepMD MLIPs yield more compact solvation shells, reproduce density-concentration trends, and run ∼12–15× faster than MACE-MP0. Although classical FFs are still more efficient and match experimental densities better, they lack chemical reactivity and thus cannot predict α or pK a , underscoring the need for system-specific reactive MLIPs beyond universal MLIPs.
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Dinpajooh, Mohammadhasan [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)] (ORCID:0000000215470334), Lacount, Michael D. [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)] (ORCID:0000000335902031), Muller, Scott E. [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)] (ORCID:0000000203361480), Henson, Neil J. [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); Washington State Univ., Pullman, WA (United States)] (ORCID:0000000218427884), Mejia-Rodriguez, Daniel [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)] (ORCID:0000000203502941), Gomez, Axel [Centre National de la Recherche Scientifique (CNRS), Paris (France). Chimie Physique et Chimie du Vivant (CPCV); École Normale Supérieure (ENS), Paris (France); Paris Sciences et Lettres Univ. (PSL) (France); Sorbonne Univ., Paris (France); Princeton Univ., NJ (United States)] (ORCID:0000000203784352), Mundy, Christopher J. [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); Univ. of Washington, Seattle, WA (United States)] (ORCID:0000000313785241), Ritzmann, Andrew M. [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)] (ORCID:0000000292917208). 2026-01-06. Modeling the behavior of concentrated aqueous HNO 3 using machine learning interatomic potentials. https://doi.org/10.1063/5.0303907
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