ConspectusPolymer electrolytes constitute a promising type of material for solid-state batteries. However, one the bottlenecks their practical implementation lies in transport properties, often including restricted Li+ self-diffusion and conductivity low cationic transference numbers. This calls molecular understanding ion polymer which dynamics (MD) simulation can provide both new physical insights quantitative predictions. Although efforts have been made this area qualitative pictures...

The transport coefficients, in particular the transference number, of electrolyte solutions are important design parameters for electrochemical energy storage devices. recent observation negative numbers PEO–LiTFSI under certain conditions has generated much discussion about its molecular origins, by both experimental and theoretical means. However, one overlooked factor these efforts is importance reference frame (RF). This creates a non-negligible gap when comparing experiment simulation...

Abstract Batteries and supercapacitors are electrochemical energy storage systems which involve multiple time‐scales length‐scales. In terms of the electrolyte serves as ionic conductor, a molecular‐level understanding corresponding transport phenomena, (thermal) stability interfacial properties is crucial for optimizing device performance achieving safety requirements. To this end, atomistic machine learning promising technology bridging microscopic models macroscopic phenomena. Here, we...

Reactive molecular dynamics simulations show how ion-paring, cross-correlated ion motions and proton transfer contribute to the ionic conductivity in concentrated NaOH solutions at elevated temperatures.

Atomic neural networks (ANNs) constitute a class of machine learning methods for predicting potential energy surfaces and physicochemical properties molecules materials. Despite many successes, developing interpretable ANN architectures implementing existing ones efficiently are still challenging. This calls reliable, general-purpose, open-source codes. Here, we present python library named PiNN as solution toward this goal. In PiNN, designed new high-performing graph convolutional network...

Electrochemical energy storage always involves the capacitive process. The prevailing electrode model used in molecular simulation of polarizable electrode-electrolyte systems is Siepmann-Sprik developed for perfect metal electrodes. This has been recently extended to study metallicity by including Thomas-Fermi screening length. Nevertheless, a further extension heterogeneous models requires introducing chemical specificity, which does not have any analytical recipes. Here, we address this...

Abstract The field of battery research has advanced significantly in the past 50 years. Despite importance electrolyte solutions for these devices, community's perception this essential component arguably aligns more with 19 th century reasoning than 20 centuries advancements. This paper traces historical evolution theories, emphasizing consequences an overly ion‐pairing‐centric view, and benefits a nuanced analysis. A quantitative example is provided. It will be shown that association...

Deviations from the Nernst-Einstein relation are commonly attributed to ion-ion correlation and ion pairing. Despite fact that these deviations can be quantified by either experimental measurements or molecular dynamics simulations, there is no rule of thumb tell extent deviations. Here, we show proportional inverse viscosity exploring finite-size effect on transport properties under periodic boundary conditions. This conclusion in accord with established results ionic liquids.

Abstract Response of the electronic density at electrode–electrolyte interface to external field (potential) is fundamental in electrochemistry. In density-functional theory, this captured by so-called charge response kernel (CRK). Projecting CRK its atom-condensed form an essential step for obtaining atoms. work, learnt from molecular polarizability using machine learning (ML) models and subsequently used response-charge prediction under (potential). As machine-learnt shows a physical...

Transference number is a key design parameter for electrolyte materials used in electrochemical energy storage systems. However, the determination of true transference from experiments rather demanding. On other hand, Bruce-Vincent method widely lab to approximately measure numbers polymer electrolytes, which becomes exact limit infinite dilution. Therefore, theoretical formulations treat and on an equal footing are clearly needed. Here, we show how concentrated solutions can be derived...

Influences of branch content (BC) and length (BL) on isothermal crystallization precisely branched polyethylene are studied by molecular dynamics simulation. Branch acts as a defect both in nucleation crystal growth process. BC affects not only kinetics but also final morphologies. Crystallization rate crystallinity decrease increases. Morphology Regimes change from lamellae to bundle at critical (20/1000 C) because different folding pattern. 50 CH 2 is the methyl sequence form crystal....

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, ion dynamical phenomenon. Here we construct model poly(ethylene oxide)–bis(trifluoromethane)sulfonimide lithium salt systems with different degrees of by tuning solvent polarity and examine relation between cation–anion distinct σ+–d lifetime pairs τ+– using molecular dynamics simulations. It found that...

The molecular mechanism of short-chain branching (SCB), especially the effects methylene sequence length (MSL) and distribution (SCBD) on initial stage nucleation, crystallization process, particularly tie chain formation process bimodal polyethylene (BPE), were explored using dynamics simulation. This work constructed two kinds BPE models in accordance with commercial pipe resins: SCB incorporated long or short chains. nucleation was determined by MSL system, as critical for a branched to...

By means of molecular dynamics simulations, extensional flow was performed on five polyethylene models with different weight distributions (MWDs) precisely designed in view Grubbs, metallocene, Ziegler-Natta, and chromium-based catalysts, while ignoring the sequence short branches to shed light mechanism MWD shish-kebab formation. The formation crystallites can be divided into three stages: emergence precursors, evolution from precursors shish nuclei, lamellar crystallites. results...

One hidden yet important issue for developing neural network potentials (NNPs) is the choice of training algorithm. In this article, we compare performance two popular algorithms, adaptive moment estimation algorithm (Adam) and extended Kalman filter (EKF), using Behler-Parrinello publicly accessible datasets liquid water [Morawietz et al., Proc. Natl. Acad. Sci. U. S. A. 113, 8368-8373, (2016) Cheng 116, 1110-1115, (2019)]. This achieved by implementing EKF in TensorFlow. It found that NNPs...

This work presents a DFT study on the effects of atomic defects in MgCl 2 -supported Ziegler–Natta catalyst. The adsorption behaviours TiCl 4 and internal donors ideal defective (104) (110) surfaces were investigated.

The zinc-promoted silylation method is of great importance to synthesize high-performance silicon-containing arylacetylene (PSA) resins in the industry. However, it difficult eliminate accompanied by-product terminal alkenes due lack mechanistic understanding silylation. initiation facilitated by interaction between zinc and phenylacetylene. Our DFT calculations indicated that intermolecular hydrogen transfer phenylacetylene follows an ionic pathway, which generates a anion corresponding...

Understanding the selectivity and kinetics: a unified ionic model of Grignard reagent formation.

The molecular dynamics simulations in this work were aimed to provide a insight into chain structure effects on non-isothermal crystallisation of polyethylene (PE) chains. behaviours influenced by length and cooling rate linear PE crystallisation: C100 C150 unable fold crystals. From C1000 C3000, abilities became stronger as increased. C5000 C14000, had no influence abilities. Final morphologies changed from rotator phase single crystal domain, multi domains formation with longer was easier...

Bimodal HDPE models were designed for extension-induced crystallization imitating the architecture of industrial bimodal copolymerized with ethylene and 1-butene, 1-hexene, or 1-octene.

PiNNAcLe is an implementation of our adaptive learn-on-the-fly algorithm for running machine-learning potential (MLP)-based molecular dynamics (MD) simulations -- emerging approach to simulate the large-scale and long-time systems where empirical forms PES are difficult obtain. The aims solve challenge parameterizing MLPs large-time-scale MD simulations, by validating simulation results at time intervals. This eliminates need uncertainty quantification methods labelling new data, thus avoids...