Optically Probing Spin and Charge Interactions in an Tunable Artificial Molecule

We optically probe and electrically control a single artificial molecule containing a well defined number of electrons. Charge and spin dependent inter-dot quantum couplings are probed optically by adding a single electron-hole pair and detecting the emission from negatively charged exciton states. Coulomb and Pauli blockade effects are directly observed and hybridization and electrostatic charging energies are independently measured. The inter-dot quantum coupling is confirmed to be mediated predominantly by electron tunneling. Our results are in excellent accord with calculations that provide a complete picture of negative excitons and few electron states in quantum dot molecules.Comment: shortened version: 6 pages, 3 figures, 1 table, to appear in Phys. Rev. Let

Epitaxial Core-Shell Oxide Nanoparticles: First-Principles Evidence for Increased Activity and Stability of Rutile Catalysts for Acidic Oxygen Evolution

Using first-principles density-functional theory calculations combined with ab initio thermodynamics, we introduce a design protocol for RuO2-based core-shell catalysts which exhibit enhanced stability and activity under oxygen evolution reaction (OER) operating conditions. Due to their high activity and favorable stability in acidic electrolytes, Ir and Ru oxides are primary catalysts for the oxygen evolution reaction (OER) in proton-exchange membrane (PEM) electrolyzers. For a future large-scale application, core-shell nanoparticles are an appealing route to minimize the demand for these precious oxides. Here, we employ first-principles density-functional theory (DFT) and ab initio thermodynamics to assess the feasibility of encapsulating a cheap rutile-structured TiO2 core with coherent, monolayer-thin IrO2 or RuO2 films. Resulting from a strong directional dependence of adhesion and strain, a wetting tendency is only obtained for some low-index facets under typical gas-phase synthesis conditions. Thermodynamic stability in particular of lattice-matched RuO2 films is instead indicated for more oxidizing conditions. Intriguingly, the calculations also predict an enhanced activity and stability of such epitaxial RuO2/TiO2 core-shell particles under OER operation

A model-free sparse approximation approach to robust formal reaction kinetics

Accurate and transferable models of reaction kinetics are of key importance for chemical reactors on both laboratory and industrial scale. Usually, setting up such models requires a detailed mechanistic understanding of the reaction process and its interplay with the reactor setup. We present a data driven approach which analyzes the influence of process parameters on the reaction rate to identify locally approximated effective rate laws without prior knowledge and assumptions. The algorithm we propose determines relevant model terms from a polynomial ansatz employing well established statistical methods. For the optimization of the model parameters special emphasize is put on the robustness of the results by taking not only the quality of the fit but also the distribution of errors into account in a multi-objective optimization. We demonstrate the flexibility of this approach based on artificial kinetic data sets from microkinetic models. This way, we show that the kinetics of both the classical HBr reaction and a prototypical catalytic cycle are automatically reproduced. Further, combining our approach with experimental screening designs we illustrate how to efficiently explore kinetic regimes by using the example of the catalytic oxidation of CO

Sum-over-states vs quasiparticle pictures of coherent correlation spectroscopy of excitons in semiconductors; femtosecond analogues of multidimensional NMR

Two-dimensional correlation spectroscopy (2DCS) based on the nonlinear optical response of excitons to sequences of ultrafast pulses, has the potential to provide some unique insights into carrier dynamics in semiconductors. The most prominent feature of 2DCS, cross peaks, can best be understood using a sum-over-states picture involving the many-body eigenstates. However, the optical response of semiconductors is usually calculated by solving truncated equations of motion for dynamical variables, which result in a quasiparticle picture. In this work we derive Green's function expressions for the four wave mixing signals generated in various phase-matching directions and use them to establish the connection between the two pictures. The formal connection with Frenkel excitons (hard-core bosons) and vibrational excitons (soft-core bosons) is pointed out.Comment: Accepted to Phys. Rev.

Accessing Structural, Electronic, Transport and Mesoscale Properties of Li-GICs via a Complete DFTB Model with Machine-Learned Repulsion Potential

Lithium-graphite intercalation compounds (Li-GICs) are the most popular anode material for modern lithium-ion batteries and have been subject to numerous studies—both experimental and theoretical. However, the system is still far from being consistently understood in detail across the full range of state of charge (SOC). The performance of approaches based on density functional theory (DFT) varies greatly depending on the choice of functional, and their computational cost is far too high for the large supercells necessary to study dilute and non-equilibrium configurations which are of paramount importance for understanding a complete charging cycle. On the other hand, cheap machine learning methods have made some progress in predicting, e.g., formation energetics, but fail to provide the full picture, including electrostatics and migration barriers. Following up on our previous work, we deliver on the promise of providing a complete and affordable simulation framework for Li-GICs. It is based on density functional tight binding (DFTB), which is fitted to dispersion-corrected DFT data using Gaussian process regression (GPR). In this work, we added the previously neglected lithium–lithium repulsion potential and extend the training set to include superdense Li-GICs (LiC6−x; x>0) and lithium metal, allowing for the investigation of dendrite formation, next-generation modified GIC anodes, and non-equilibrium states during fast charging processes in the future. For an extended range of structural and energetic properties—layer spacing, bond lengths, formation energies and migration barriers—our method compares favorably with experimental results and with state-of-the-art dispersion-corrected DFT at a fraction of the computational cost. We make use of this by investigating some larger-scale system properties—long range Li–Li interactions, dielectric constants and domain-formation—proving our method’s capability to bring to light new insights into the Li-GIC system and bridge the gap between DFT and meso-scale methods such as cluster expansions and kinetic Monte Carlo simulations

Data-Efficient Iterative Training of Gaussian Approximation Potentials: Application to Surface Structure Determination of Rutile IrO₂ and RuO₂

Machine-learning interatomic potentials like Gaussian Approximation Potentials (GAPs) constitute a powerful class of surrogate models to computationally involved first-principles calculations. At similar predictive quality but significantly reduced cost, they could leverage otherwise barely tractable extensive sampling as in global surface structure determination (SSD). This efficiency is jeopardized though, if an a priori unknown structural and chemical search space as in SSD requires an excessive number of first-principles data for the GAP training.To this end, we present a general and data-efficient iterative training protocol that blends the creation of new training data with the actual surface exploration process. Demonstrating this protocol with the SSD of low-index facets of rutile IrO2 and RuO2 , the involved simulated annealing on the basis of the refining GAP identifies a number of unknown terminations even in the restricted sub-space of (1×1) surface unit-cells. Especially in an O-poor environment, some of these, then metal-rich terminations, are thermodynamically most stable and are reminiscent of complexions as discussed for complex ceramic materials

On the Role of Long-Range Electrostatics in Machine-Learned Interatomic Potentials for Complex Battery Materials

Modeling complex energy materials such as solid-state electrolytes (SSEs) realistically at the atomistic level strains the capabilities of state-of-the-art theoretical approaches. On one hand, the system sizes and simulation time scales required are prohibitive for first-principles methods such as the density functional theory. On the other hand, parameterizations for empirical potentials are often not available, and these potentials may ultimately lack the desired predictive accuracy. Fortunately, modern machine learning (ML) potentials are increasingly able to bridge this gap, promising first-principles accuracy at a much reduced computational cost. However, the local nature of these ML potentials typically means that long-range contributions arising, for example, from electrostatic interactions are neglected. Clearly, such interactions can be large in polar materials such as electrolytes, however. Herein, we investigate the effect that the locality assumption of ML potentials has on lithium mobility and defect formation energies in the SSE Li7P3S11. We find that neglecting long-range electrostatics is unproblematic for the description of lithium transport in the isotropic bulk. In contrast, (field-dependent) defect formation energies are only adequately captured by a hybrid potential combining ML and a physical model of electrostatic interactions. Broader implications for ML-based modeling of energy materials are discussed

Dynamic extensions of batch systems with cloud resources

Compute clusters use Portable Batch Systems (PBS) to distribute workload among individual cluster machines. To extend standard batch systems to Cloud infrastructures, a new service monitors the number of queued jobs and keeps track of the price of available resources. This meta-scheduler dynamically adapts the number of Cloud worker nodes according to the requirement profile. Two different worker node topologies are presented and tested on the Amazon EC2 Cloud service

Bandgap engineering of sol-gel synthesized amorphous Zn1-xMgxO films

Amorphous Zn1-xMgxO (alpha-Zn1-xMgxO) ternary alloy thin films across the full compositional range were synthesized by a low-cost sol-gel method on quartz substrates. The amorphous property of the alpha-Zn1-xMgxO films was verified by x-ray diffraction, and atomic force microscopy revealed a smooth surface with sub-nanometer root-mean square roughness. The current phase segregation issue limiting application of crystalline Zn1-xMgxO with 38% \u3c x \u3c 75% was completely eliminated by growing amorphous films. Optical transmission measurements showed high transmissivity of more than 90% in the visible and near infrared regions, with optical bandgap tunability from 3.3 eV to more than 6.5 eV by varying the Mg content