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Register a new userGaussian approximation potential modeling of lithium intercalation in carbon nanostructures
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We demonstrate how machine-learning based interatomic potentials can be used to model guest atoms in host structures. Specifically, we generate Gaussian approximation potential (GAP) models for the interaction of lithium atoms with graphene, graphite, and disordered carbon nanostructures, based on reference density-functional theory (DFT) data. Rather than treating the full Li--C system, we demonstrate how the energy and force differences arising from Li intercalation can be modeled and then added to a (prexisting and unmodified) GAP model of pure elemental carbon. Furthermore, we show the benefit of using an explicit pair potential fit to capture effective Li--Li interactions, to improve the performance of the GAP model. This provides proof-of-concept for modeling guest atoms in host frameworks with machine-learning based potentials, and in the longer run is promising for carrying out detailed atomistic studies of battery materials.
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X-ray amorphous manganese oxides were prepared by reduction of sodium permanganate by lithium iodide in aqueous medium (MnOx-I) and by decomposition of manganese carbonate at moderate temperature (MnOx-C). TEM showed that these materials are not amorphous, but nanostructured, with a prominent spinel substructure in MnOx-C. These materials intercalate lithium with capacities up to 200 mAh/g at first cycle (potential window 1.8-4.3 V) and 175 mAh/g at 100th cycle. Best performances for MnOx-C are obtained with cobalt doping. Potential electrochemical spectroscopy shows that the initial discharge induces a 2-phase transformation in MnOx-C phases, but not in MnOx-I ones. EXAFS and XANES confirm the participation of manganese in the redox process, with variations in local structure much smaller than in known long-range crystallized manganese oxides. X-ray absorption spectroscopy also shows that cobalt in MnOx-C is divalent and does not participate in the electrochemical reaction.
Machine learning driven interatomic potentials, including Gaussian approximation potential (GAP) models, are emerging tools for atomistic simulations. Here, we address the methodological question of how one can fit GAP models that accurately predict vibrational properties in specific regions of configuration space, whilst retaining flexibility and transferability to others. We use an adaptive regularization of the GAP fit that scales with the absolute force magnitude on any given atom, thereby exploring the Bayesian interpretation of GAP regularization as an expected error, and its impact on the prediction of physical properties for a material of interest. The approach enables excellent predictions of phonon modes (to within 0.1-0.2 THz) for structurally diverse silicon allotropes, and it can be coupled with existing fitting databases for high transferability. These findings and workflows are expected to be useful for GAP-driven materials modeling more generally.
Lithium-intercalated layered transition-metal oxides, LixTMO2, brought about a paradigm change in rechargeable batteries in recent decades and show promise for use in memristors, a type of device for future neural computing and on-chip storage. Thermal transport properties, although being a crucial element in limiting the charging/discharging rate, package density, energy efficiency, and safety of batteries as well as the controllability and energy consumption of memristors, are poorly managed or even understood yet. Here, for the first time, we employ quantum calculations including high-order lattice anharmonicity and find that the thermal conductivity k of LixTMO2 materials is significantly lower than hitherto believed. More specifically, the theoretical upper limit of k of LiCoO2 is 6 W/m-K, 2-6 times lower than the prior theoretical predictions. Delithiation further reduces k by 40-70% for LiCoO2 and LiNbO2. Grain boundaries, strains, and porosity are yet additional causes of thermal-conductivity reduction, while Li-ion diffusion and electrical transport are found to have only a minor effect on phonon thermal transport. The results elucidate several long-standing issues regarding the thermal transport in lithium-intercalated materials and provide guidance toward designing high-energy-density batteries and controllable memristors.
Various bandstructure engineering methods have been studied to improve the performance of graphitic transparent conductors; however none demonstrated an increase of optical transmittance in the visible range. Here we measure in situ optical transmittance spectra and electrical transport properties of ultrathin-graphite (3-60 graphene layers) simultaneously via electrochemical lithiation/delithiation. Upon intercalation we observe an increase of both optical transmittance (up to twofold) and electrical conductivity (up to two orders of magnitude), strikingly different from other materials. Transmission as high as 91.7% with a sheet resistance of 3.0 {Omega} per square is achieved for 19-layer LiC6, which corresponds to a figure of merit {sigma}_dc/{sigma}_opt = 1400, significantly higher than any other continuous transparent electrodes. The unconventional modification of ultrathin-graphite optoelectronic properties is explained by the suppression of interband optical transitions and a small intraband Drude conductivity near the interband edge. Our techniques enable the investigation of other aspects of intercalation in nanostructures.
For a wider adoption of electromobility, the market calls for fast-charging, safe, long-lasting batteries with sufficient performance. This drives the exploration of new energy storage materials, and also promotes fundamental investigations of materials already widely used. At the moment, renewed interest in anode materials is observed -- with a particular focus on graphite electrodes for lithium-ion batteries. Here, we focus on the upper limit of lithium intercalation in the morphologically quasi-ideal Highly Oriented Pyrolytic Graphite (HOPG). The state and long-term stability of a sample prepared by immersion of an HOPG crystal in liquid lithium metal at ambient pressure is investigated by static $^7$Li nuclear magnetic resonance (NMR). We resolved signatures of superdense intercalation compounds, LiC$_{6-x}$ with $x>0$, which are monitored upon calendaric ageing. {em Ab initio} thermodynamics and {em ab initio} molecular dynamics reveal the relative stabilities and kinetics of different superdense configurations, providing leads for the interpretation of the NMR results. Including these superdense structures in the conceptual design of high-energy, fast-charge electrodes might provide further insights on the failure mechanisms and performance of Li-ion batteries.
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