Molecular dynamics simulations of proton conducting media containing phosphoric acid

Jinnouchi, Ryosuke

doi:10.1039/d2cp00484d

Cited by 9 publications

(23 citation statements)

References 63 publications

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“…The results displayed in Figure 4c show ∼97% probability for forming less than three Grotthuss chains of four water molecules. The length of the chains is much shorter than in the FPMD result 70 and the MLFF result 62 on liquid phosphoric acid at 383 and 473 K, respectively. These exhibit a maximum chain length of six phosphate species (n = 5).…”

Section: The Journal Of Physical Chemistry Lettersmentioning

confidence: 68%

“…To determine an appropriate set of parameters and training conditions, we examine learning curves for four sets of parameters named A, A′, B and B′, tabulated in Table S1 in SI. The parameter sets A and A′ provide the conventional power spectrum. , Parameter set A′ adopts a smaller L max of 2 as compared to the past studies. ,− Thus, the angular resolution of A′ is coarser. Unlike these parameter sets, B and B′ explicitly separate the two- and three-body descriptors to make their weights controllable .…”

mentioning

confidence: 99%

“…In order to compare with available experimental data, all production MD simulations for Nafion and H 2 SO 4 solution are performed at 298 K for comparison with experiments. For liquid H 3 PO 4 , which exhibits high proton conductivity at higher temperature, , the temperature is set to 473 K. Figure a shows the simulated density as a function of the water uptake λ (water number per sulfonate group). The density decreases with the water swelling, similarly to the experiment .…”

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confidence: 99%

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Proton Transport in Perfluorinated Ionomer Simulated by Machine-Learned Interatomic Potential

Jinnouchi

Minami

Karsai

et al. 2023

J. Phys. Chem. Lett.

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Polymers are a class of materials that are highly challenging to deal with using first-principles methods. Here, we present an application of machine-learned interatomic potentials to predict structural and dynamical properties of dry and hydrated perfluorinated ionomers. An improved active-learning algorithm using a small number of descriptors allows to efficiently construct an accurate and transferable model for this multielemental amorphous polymer. Molecular dynamics simulations accelerated by the machine-learned potentials accurately reproduce the heterogeneous hydrophilic and hydrophobic domains formed in this material as well as proton and water diffusion coefficients under a variety of humidity conditions. Our results reveal pronounced contributions of Grotthuss chains consisting of two to three water molecules to the high proton mobility under strongly humidified conditions.

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Section: The Journal Of Physical Chemistry Lettersmentioning

confidence: 68%

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confidence: 99%

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confidence: 99%

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Proton Transport in Perfluorinated Ionomer Simulated by Machine-Learned Interatomic Potential

Jinnouchi

Minami

Karsai

et al. 2023

J. Phys. Chem. Lett.

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“…This is certainly sufficient, given that the lifetime of H + hopping between phosphoric acid anions is 100−300 fs in ZnPIm. 40 Note that the τ av of Rubpz ranges between 27 and 520 ns in aqueous solution. 23 Conductivity in ZnPIm is mainly contributed by the migration of H + through either phosphate chains or phosphate−imidazolium pathways; 24,26 therefore, we utilized 1 H MAS solid-state NMR (20 kHz) to study their mobilities (Figures S11 to S13).…”

Section: ■ Results and Discussionmentioning

confidence: 99%

Photoexcited Anhydrous Proton Conductivity in Coordination Polymer Glass

Impeng

Bureekaew

et al. 2023

J. Am. Chem. Soc.

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Optically switchable proton-conductive materials will enable the development of artificial ionic circuits. However, most switchable platforms rely on conformational changes in crystals to alter the connectivity of guest molecules. Guest dependency, low transmittance, and poor processability of polycrystalline materials hinder overall light responsiveness and contrast between on and off states. Here, we optically control anhydrous proton conductivity in a transparent coordination polymer (CP) glass. Photoexcitation of tris(bipyrazine)ruthenium(II) complex in CP glass causes reversible increases in proton conductivity by a factor of 181.9 and a decrease in activation energy barrier from 0.76 eV to 0.30 eV. Modulating light intensity and ambient temperature enables total control of anhydrous protonic conductivity. Spectroscopies and density functional theory studies reveal the relationship between the presence of proton deficiencies and the decreasing activation energy barrier for proton migrations.

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“…In general, strict trade-off between the resolutions and spatiotemporal scales exist, and the different methods have mostly segregated spatiotemporal scales. Reactive MD simulations require remarkably larger computational cost than non-reactive counterparts, restricting the scales to a few nanometers and sub-nanosecond to probe electrolyte-electrode interfacial reactions, [104,110,282,283] proton conduction in fuel cells [289] or protein buffer systems. [287] Simulations incorporating explicit electronic polarization occurring at the electrode surface (i.e., constant potential MD) [18,66,68] or at the molecular constituents [37,87] are of particular importance in modeling the electrostatic correlation under a heterogeneous dielectric environment, [136] but often are limited in approachable timescale due to the smaller timestep required for the rapidly changing electronic response.…”

Section: Discussionmentioning

confidence: 99%

Predictive Molecular Models for Charged Materials Systems: From Energy Materials to Biomacromolecules

Jeong

Lee

et al. 2022

Advanced Materials

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Electrostatic interactions play a dominant role in charged materials systems. Understanding the complex correlation between macroscopic properties with microscopic structures is of critical importance to develop rational design strategies for advanced materials. But the complexity of this challenging task is augmented by interfaces present in the charged materials systems, such as electrode–electrolyte interfaces or biological membranes. Over the last decades, predictive molecular simulations that are founded in fundamental physics and optimized for charged interfacial systems have proven their value in providing molecular understanding of physicochemical properties and functional mechanisms for diverse materials. Novel design strategies utilizing predictive models have been suggested as promising route for the rational design of materials with tailored properties. Here, an overview of recent advances in the understanding of charged interfacial systems aided by predictive molecular simulations is presented. Focusing on three types of charged interfaces found in energy materials and biomacromolecules, how the molecular models characterize ion structure, charge transport, morphology relation to the environment, and the thermodynamics/kinetics of molecular binding at the interfaces is discussed. The critical analysis brings two prominent field of energy materials and biological science under common perspective, to stimulate crossover in both research field that have been largely separated.

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Molecular dynamics simulations of proton conducting media containing phosphoric acid

Abstract: An anhydrous proton conducting electrolyte is a key material to realise medium-temperature fuel cells that can drastically simplify heat radiation systems in transportation applications. However, practical applications are limited by...

Cited by 9 publications

References 63 publications

Proton Transport in Perfluorinated Ionomer Simulated by Machine-Learned Interatomic Potential

Proton Transport in Perfluorinated Ionomer Simulated by Machine-Learned Interatomic Potential

Photoexcited Anhydrous Proton Conductivity in Coordination Polymer Glass

Predictive Molecular Models for Charged Materials Systems: From Energy Materials to Biomacromolecules

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