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| 2. |
- Kanai, M, et al.
(författare)
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- 2023
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swepub:Mat__t
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| 3. |
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| 4. |
- Gudla, Harish
(författare)
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Using Molecular Dynamics Simulations to Explore Critical Property Relationships in Polymer Electrolytes : Polarity, Coordination, Ionic transport, Ion-pairing, and Ion-ion Correlations
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- While ion transport in solid polymer electrolytes (SPEs) has been explored for decades, there still remains controversies about its fundamental properties, often correlated with gaps between experimental and computational studies. Using molecular dynamics simulations to understand the complex transport mechanisms and also to fill these gaps is the main goal of this thesis. This is achieved by critically examining the relationships between different properties in SPE systems: polarity, coordination, ion-pairing, and ion-ion correlations, which highly influence the ionic transport mechanism. Firstly, the relation between polarity, ion-pairing, and ion-ion correlations was explored. The solvent polarity (εp) of poly(ethylene oxide) (PEO) doped with LiTFSI system is modulated using a charge scaling method. When separating the effects of solvent polarity and glass transition temperature, a maximum in the Li-ion diffusion coefficient with respect to εp is observed. This is attributed to the transitions in the transport mechanisms and an optimal solvating ability of Li-ion at intermediate values of εp. The solvent polarity also plays a critical role in the formation of charge-neutral ion pairs, which is commonly considered detrimental for ionic conductivity. The relation between cation−anion distinct conductivity and the lifetime of ion pairs was thereby examined, where it is found that short-lived ion pairs actually contribute positively to the ionic conductivity. Moreover, the origins of the recently observed negative transference numbers were scrutinized. A strong dependence of the reference frame in the estimation of the transference numbers is found, which explains observed differences between experiments and computations. Secondly, the role of coordination chemistry and its influence on ion transport mechanisms and conduction properties in SPEs was studied. The change in the cation coordination with both polymers and anions was used to study the dominant transport mechanisms at different molecular weights and salt concentrations for PEO and a polyester-based SPE, which shows that essentially very little true hopping occurs in these materials. In this context, the coordination and ionic transport properties of three resemblant carbonyl-coordinating polymers are also investigated: polyketones, polyesters, and polycarbonates. The extra main-chain oxygens for the latter polymers are shown to decrease the electrostatic energy between Li-ion and the carbonyl group, and the cationic transference numbers are thus found to be increasing as the coordination strength decrease.
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| 5. |
- Li, Xiaobin, et al.
(författare)
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Knowledge graph based OPC UA information model automatic construction method for heterogeneous devices integration
- 2024
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Ingår i: Robotics and Computer-Integrated Manufacturing. - : Elsevier BV. - 0736-5845 .- 1879-2537. ; 88
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Tidskriftsartikel (refereegranskat)abstract
- Emergent manufacturing paradigms, including ubiquitous manufacturing, social manufacturing, and agile manufacturing, facilitate the advancement of the manufacturing industry. However, these paradigms suffer from the difficulty of interoperability due to the heterogeneity of field devices and diverse communication protocols. The OLE for Process Control Unified Architecture (OPC UA) technique, which is featured with cross-platform, is considered as the pivotal technology for addressing this issue. However, there still exist a large number of heterogeneous devices which do not support OPC UA protocol. The communication protocols of these heterogeneous devices need to be converted into OPC UA based on information model, nevertheless, the manual construction of OPC UA information model is cumbersome and inefficient. In this paper, an integrated architecture based on reasoning over the OPC UA information model is proposed to realize rapid integration of heterogeneous devices to achieve interoperability. To realize the proposed integrated architecture, an automatic device information model construction method is developed simultaneously. The method first identifies the type of the newly accessed device based on the character-level TextCNN (CTCNN) model, which utilizes the word sequence extracted from the corresponding device data frame as input. Subsequently an open-world knowledge completion model is adopted to link the unseen entities in the device data frame to the information model knowledge graph(KG) to support the information model automatic construction for unknown device. For evaluation purpose, two industrial datasets are constructed and the results demonstrate the feasibility and effectiveness of the proposed method.
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| 6. |
- 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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