Currently available solid polymer electrolytes for Li-ion cells require deeper understanding and significant improvement in ionic transport properties to enable their use in high-power batteries. We ...use molecular dynamics simulations to model the solid amorphous polymer electrolyte system comprising poly(ethylene) oxide (PEO), lithium, and bis(trifluoromethane)sulfonimide anion (TFSI), exploring effects of high salt concentrations relevant for battery applications. Using statistical analysis of ion distribution and transport, we investigate the significant effect that salt concentration has on ion mobility. At practical salt concentrations, a previously undetected ensemble of Li–TFSI clusters emerges where Li ions have significantly lower coordination by the polymer, and this results in their significantly lower mobility as compared to Li ions coordinated by the polymer. We also find the tendency for cation–anion clusters to be asymmetrical, with the anions in greater number than Li cations, which may further affect the transport properties of this material. The existence of such negatively charged clusters has been recently speculated to explain the experimentally observed negative transference number at high LiTFSI concentrations in PEO. Our methodology enables us to suggest strategies for improvement of transport properties and can be generalized to other polymer–Li–salt combinations.
This work presents Neural Equivariant Interatomic Potentials (NequIP), an E(3)-equivariant neural network approach for learning interatomic potentials from ab-initio calculations for molecular ...dynamics simulations. While most contemporary symmetry-aware models use invariant convolutions and only act on scalars, NequIP employs E(3)-equivariant convolutions for interactions of geometric tensors, resulting in a more information-rich and faithful representation of atomic environments. The method achieves state-of-the-art accuracy on a challenging and diverse set of molecules and materials while exhibiting remarkable data efficiency. NequIP outperforms existing models with up to three orders of magnitude fewer training data, challenging the widely held belief that deep neural networks require massive training sets. The high data efficiency of the method allows for the construction of accurate potentials using high-order quantum chemical level of theory as reference and enables high-fidelity molecular dynamics simulations over long time scales.
We show that strong cation–anion interactions in a wide range of lithium-salt/ionic liquid mixtures result in a negative lithium transference number, using molecular dynamics simulations and rigorous ...concentrated solution theory. This behavior fundamentally deviates from that obtained using self-diffusion coefficient analysis and explains well recent experimental electrophoretic nuclear magnetic resonance measurements, which account for ion correlations. We extend these findings to several ionic liquid compositions. We investigate the degree of spatial ionic coordination employing single-linkage cluster analysis, unveiling asymmetrical anion–cation clusters. We formulate a way to compute the effective lithium charge and show that lithium-containing clusters carry a negative charge over a remarkably wide range of compositions and concentrations. This finding has significant implications for the overall performance of battery cells based on ionic liquid electrolytes. It also provides a rigorous prediction recipe and design protocol for optimizing transport properties in next-generation highly correlated electrolytes.
Strong ionic interactions in concentrated ionic liquids is shown to result in significant correlations and deviations from ideal solution behavior. We use rigorous concentrated solution theory ...coupled with molecular dynamics simulations to compute and explain the unusual dependence of transport properties on cation concentration in the Na+-Pyr13+-FSI− ionic liquid electrolyte. When accounting for intra- and inter-species correlation, beyond the commonly used uncorrelated Nernst-Einstein equation, an anomalously low and even negative transference number emerges for xNaFSI lower than 0.2. With increasing concentration the transference number monotonically increases, approaching unity, while the total conductivity decreases as the system transitions to a state resembling a single-ion solid-state electrolyte. The degree of spatial ionic association is explored further by employing unsupervised single-linkage clustering algorithm. We emphasize that strong ion-ion coupling in the electrolyte significantly impacts the trade-off between key electrolyte transport properties, and consequently governs the power density of the battery.
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•Presence of strong ionic correlation makes dilute solution theory unreliable.•Anomalously low and even negative transference number is predicted.•Asymmetrical cation-anion clusters are linked to the transport properties.
The power conversion efficiency of solar cells based on copper (I) oxide (Cu2O) is enhanced by atomic layer deposition of a thin gallium oxide (Ga2O3) layer. By improving band‐alignment and ...passivating interface defects, the device exhibits an open‐circuit voltage of 1.20 V and an efficiency of 3.97%, showing potential of over 7% efficiency.
Abstract
Recently, machine learning (ML) has been used to address the computational cost that has been limiting ab initio molecular dynamics (AIMD). Here, we present GNNFF, a graph neural network ...framework to directly predict atomic forces from automatically extracted features of the local atomic environment that are translationally-invariant, but rotationally-covariant to the coordinate of the atoms. We demonstrate that GNNFF not only achieves high performance in terms of force prediction accuracy and computational speed on various materials systems, but also accurately predicts the forces of a large MD system after being trained on forces obtained from a smaller system. Finally, we use our framework to perform an MD simulation of Li
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, a superionic conductor, and show that resulting Li diffusion coefficient is within 14% of that obtained directly from AIMD. The high performance exhibited by GNNFF can be easily generalized to study atomistic level dynamics of other material systems.
We demonstrate a tunable electron-blocking layer to enhance the performance of an Earth-abundant metal-oxide solar-cell material. A 5 nm thick amorphous ternary metal-oxide buffer layer reduces ...interface recombination, resulting in sizable open-circuit voltage and efficiency enhancements. This work emphasizes the importance of interface engineering in improving the performance of Earth-abundant solar cells.
A promising approach for upgrading the performance of an established low-bandgap solar technology without adding much cost is to deposit a high bandgap polycrystalline semiconductor on top to make a ...tandem solar cell. We use a transparent silver nanowire electrode on perovskite solar cells to achieve a semi-transparent device. We place the semi-transparent cell in a mechanically-stacked tandem configuration onto copper indium gallium diselenide (CIGS) and low-quality multicrystalline silicon (Si) to achieve solid-state polycrystalline tandem solar cells with a net improvement in efficiency over the bottom cell alone. This work paves the way for integrating perovskites into a low-cost and high-efficiency (>25%) tandem cell.
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