複雑な電解質溶液における化学種分布を予測（Toward Predictive Speciation in Complex Electrolyte Solutions）

2026-09-23 パシフィック・ノースウェスト国立研究所（PNNL）

PNNL（米国パシフィック・ノースウェスト国立研究所）の研究チームは、硝酸水溶液とランタノイド―硝酸イオン系を対象に、複雑な電解質溶液中で分子種（スペシエーション）がどのように変化するかを予測するための計算手法を検討した。硝酸については、量子化学計算で学習した機械学習原子間ポテンシャル（MLIP）を開発し、プロトン移動を含む分子構造変化を追跡。その結果、濃度が高くなるほど硝酸の解離度が低下し、局所的な溶媒和環境も大きく変化することを示した。ランタノイド系では、量子化学モデルと水溶液全体を扱う分子シミュレーションを組み合わせ、硝酸イオンの配位と水和殻の再編成が密接に関係することを明らかにした。これらの成果は、重要鉱物の分離・回収を左右する複雑な溶液化学を、AI・量子化学・分子シミュレーションによって予測するための基礎となる。

A custom-trained machine learning interatomic potential captures proton transfer, showing that the strong acid becomes progressively less dissociated as concentration increases. (Image by Hadi Dinpajooh | Pacific Northwest National Laboratory)

＜関連情報＞

機械学習による原子間ポテンシャルを用いた濃硝酸水溶液の挙動モデリング Modeling the behavior of concentrated aqueous HNO3 using machine learning interatomic potentials

Mohammadhasan Dinpajooh;Michael D. Lacount;Scott E. Muller;Neil J. Henson;Daniel Mejia-Rodriguez;Axel Gomez;Christopher J. Mundy;Andrew M. Ritzmann
The Journal of Chemical Physics Published:January 06 2026
DOI:https://doi.org/10.1063/5.0303907

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 HNO₃ 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 HNO₃/NO₃^–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.

ランタニド-硝酸塩錯体の水溶液中における安定度定数：理論的研究 Stability constants of lanthanide–nitrate complexes in aqueous solutions: a theoretical study

Mohammadhasan Dinpajooh;Niranjan Govind;Andrew M. Ritzmann;Nicolas E. Uhnak
Physical Chemistry Chemical Physics Published:30 June 2026
DOI:https://doi.org/10.1039/d6cp00838k

Calculating stability constants for lanthanide–nitrate complexes in aqueous solution is challenging due to the complex free-energy landscapes of the participating species. In this work, we independently compare cluster-continuum solvation at a density functional theory level and condensed-phase approaches using universal machine learning interatomic potentials (MLIPs) for determining the stability constants of lanthanide–nitrate complexes in aqueous solutions. Within the cluster–continuum solvation framework at the B3LYP level of theory, reactions involving lanthanide coordination numbers of both 8 and 9 are found to be relevant. After an empirical linear free-energy correction, the cluster–continuum results fall on the same order-of-magnitude scale as the experimental stability constants. By contrast, condensed-phase simulations using MACE-MP0 MLIP underestimates lanthanide hydration numbers and provides potential of mean forces resulting in stability constants with large deviations from experiment, whereas MACE-MATPES-R2SCAN improves both hydration structure and the stability-constant scale but still does not quantitatively reproduce the detailed lanthanide trend. Across both approaches, nitrate binding is generally viewed as a labile coordination motif involving interconverting mono- or bidentate structures, with hydration-shell structure influencing which configurations are favored. Overall, the cluster–continuum calculations provide a practical semi-quantitative baseline for the experimental stability-constant scale, while the explicit-solvent MLIP benchmarks show clear progress from MACE-MP0 to MACE-MATPES-R2SCAN but also highlight the need for lanthanide-targeted training, or improved long-range and polarization treatments in condensed-phase simulations to obtain predictive thermodynamics for complex aqueous lanthanide chemistry.