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| 2. |
- Dufils, Thomas, et al.
(författare)
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PiNNwall : Heterogeneous Electrode Models from Integrating Machine Learning and Atomistic Simulation
- 2023
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Ingår i: Journal of Chemical Theory and Computation. - : American Chemical Society (ACS). - 1549-9618 .- 1549-9626. ; 19:15, s. 5199-5209
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Tidskriftsartikel (refereegranskat)abstract
- Electrochemical energy storage always involves the capacitive process. The prevailing electrode model used in the molecular simulation of polarizable electrode–electrolyte systems is the Siepmann–Sprik model developed for perfect metal electrodes. This model has been recently extended to study the metallicity in the electrode by including the Thomas–Fermi screening length. Nevertheless, a further extension to heterogeneous electrode models requires introducing chemical specificity, which does not have any analytical recipes. Here, we address this challenge by integrating the atomistic machine learning code (PiNN) for generating the base charge and response kernel and the classical molecular dynamics code (MetalWalls) dedicated to the modeling of electrochemical systems, and this leads to the development of the PiNNwall interface. Apart from the cases of chemically doped graphene and graphene oxide electrodes as shown in this study, the PiNNwall interface also allows us to probe polarized oxide surfaces in which both the proton charge and the electronic charge can coexist. Therefore, this work opens the door for modeling heterogeneous and complex electrode materials often found in energy storage systems.
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| 3. |
- Gudla, Harish, et al.
(författare)
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Importance of the Ion-Pair Lifetime in Polymer Electrolytes
- 2021
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Ingår i: The Journal of Physical Chemistry Letters. - : American Chemical Society (ACS). - 1948-7185. ; 12:35, s. 8460-8464
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Tidskriftsartikel (refereegranskat)abstract
- Ion pairing is commonly considered as a culprit for the reduced ionic conductivity in polymer electrolyte systems. However, this simple thermodynamic picture should not be taken literally, as ion pairing is a dynamical phenomenon. Here we construct model poly(ethylene oxide)-bis(trifluoromethane)sulfonimide lithium salt systems with different degrees of ion pairing by tuning the solvent polarity and examine the relation between the cation-anion distinct conductivity sigma(d)(+-) and the lifetime of ion pairs tau(+-) using molecular dynamics simulations. It is found that there exist two distinct regimes where sigma(d)(+-) scales with 1/tau(+-) and tau(+-), respectively, and the latter is a signature of longer-lived ion pairs that contribute negatively to the total ionic conductivity. This suggests that ion pairs are kinetically different depending on the solvent polarity, which renders the ion-pair lifetime highly important when discussing its effect on ion transport in polymer electrolyte systems.
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| 5. |
- Shao, Yunqi, et al.
(författare)
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Bruce-Vincent transference numbers from molecular dynamics simulations
- 2023
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Ingår i: Journal of Chemical Physics. - : American Institute of Physics (AIP). - 0021-9606 .- 1089-7690. ; 158:16
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Tidskriftsartikel (refereegranskat)abstract
- Transference number is a key design parameter for electrolyte materials used in electrochemical energy storage systems. However, the determination of the true transference number from experiments is rather demanding. On the other hand, the Bruce-Vincent method is widely used in the lab to approximately measure transference numbers of polymer electrolytes, which becomes exact in the limit of infinite dilution. Therefore, theoretical formulations to treat the Bruce-Vincent transference number and the true transference number on an equal footing are clearly needed. Here, we show how the Bruce-Vincent transference number for concentrated electrolyte solutions can be derived in terms of the Onsager coefficients, without involving any extrathermodynamic assumptions. By demonstrating it for the case of poly(ethylene oxide)-lithium bis(trifluoromethane)sulfonimide system, this work opens the door to calibrating molecular dynamics (MD) simulations via reproducing the Bruce-Vincent transference number and using MD simulations as a predictive tool for determining the true transference number.
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| 6. |
- Shao, Yunqi, et al.
(författare)
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Finite-field coupling via learning the charge response kernel
- 2022
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Ingår i: Electronic Structure. - : Institute of Physics Publishing (IOPP). - 2516-1075. ; 4:1
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Tidskriftsartikel (refereegranskat)abstract
- Response of the electronic density at the electrode–electrolyte interface to the external field (potential) is fundamental in electrochemistry. In density-functional theory, this is captured by the so-called charge response kernel (CRK). Projecting the CRK to its atom-condensed form is an essential step for obtaining the response charge of atoms. In this work, the atom-condensed CRK is learnt from the molecular polarizability using machine learning (ML) models and subsequently used for the response-charge prediction under an external field (potential). As the machine-learnt CRK shows a physical scaling of polarizability over the molecular size and does not (necessarily) require the matrix-inversion operation in practice, this opens up a viable and efficient route for introducing finite-field coupling in the atomistic simulation of electrochemical systems powered by ML models.
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| 7. |
- Shao, Yunqi, et al.
(författare)
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Modelling Bulk Electrolytes and Electrolyte Interfaces with Atomistic Machine Learning
- 2021
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Ingår i: Batteries & Supercaps. - : John Wiley & Sons. - 2566-6223. ; 4:4, s. 585-595
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Forskningsöversikt (refereegranskat)abstract
- Batteries and supercapacitors are electrochemical energy storage systems which involve multiple time-scales and length-scales. In terms of the electrolyte which serves as the ionic conductor, a molecular-level understanding of the corresponding transport phenomena, electrochemical (thermal) stability and interfacial properties is crucial for optimizing the device performance and achieving safety requirements. To this end, atomistic machine learning is a promising technology for bridging microscopic models and macroscopic phenomena. Here, we provide a timely snapshot of recent advances in this area. This includes technical considerations that are particularly relevant for modelling electrolytes as well as specific examples of both bulk electrolytes and associated interfaces. A perspective on methodological challenges and new applications is also discussed.
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| 8. |
- Shao, Yunqi, et al.
(författare)
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PiNN : A Python Library for Building Atomic Neural Networks of Molecules and Materials
- 2020
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Ingår i: Journal of Chemical Information and Modeling. - : AMER CHEMICAL SOC. - 1549-9596 .- 1549-960X. ; 60:3, s. 1184-1193
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Tidskriftsartikel (refereegranskat)abstract
- Atomic neural networks (ANNs) constitute a class of machine learning methods for predicting potential energy surfaces and physicochemical properties of molecules and materials. Despite many successes, developing interpretable ANN architectures and implementing existing ones efficiently are still challenging. This calls for reliable, general-purpose, and open-source codes. Here, we present a python library named PiNN as a solution toward this goal. In PiNN, we designed a new interpretable and high-performing graph convolutional neural network variant, PiNet, as well as implemented the established Behler-Parrinello neural network. These implementations were tested using datasets of isolated small molecules, crystalline materials, liquid water, and an aqueous alkaline electrolyte. PiNN comes with a visualizer called PiNNBoard to extract chemical insight "learned" by ANNs. It provides analytical stress tensor calculations and interfaces to both the atomic simulation environment and a development version of the Amsterdam Modeling Suite. Moreover, PiNN is highly modularized, which makes it useful not only as a standalone package but also as a chain of tools to develop and to implement novel ANNs. The code is distributed under a permissive BSD license and is freely accessible at https://github.com/Teoroo-CMC/PiNN/with full documentation and tutorials.
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| 10. |
- Shao, Yunqi, et al.
(författare)
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Role of Viscosity in Deviations from the Nernst-Einstein Relation
- 2020
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Ingår i: Journal of Physical Chemistry B. - : AMER CHEMICAL SOC. - 1520-6106 .- 1520-5207. ; 124:23, s. 4774-4780
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Tidskriftsartikel (refereegranskat)abstract
- Deviations from the Nernst-Einstein relation are commonly attributed to ion-ion correlation and ion pairing. Despite the fact that these deviations can be quantified by either experimental measurements or molecular dynamics simulations, there is no rule of thumb to tell the extent of deviations. Here, we show that deviations from the Nernst-Einstein relation are proportional to the inverse viscosity by exploring the finite-size effect on transport properties under periodic boundary conditions. This conclusion is in accord with the established experimental results of ionic liquids.
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