Enforced Freedom: Electric-Field-Induced Declustering of Ionic-Liquid Ions in the Electrical Double Layer

Ions in the bulk of solvent-free ionic liquids bind into ion pairs and clusters.The competition between the propensity of ions to stay in a bound state,and the reduction of the energy when unbinding in electric field,determines the portion of free ions in the electrical double layer.We present the simplest possible mean-field theory to study this effect."Cracking"of ion pairs into free ions in electric field is accompanied by the change of the dielectric response of the ionic liquid.The predictions from the theory are verified and further explored by molecular dynamics simulations.A particular finding of the theory is that the differential capacitance vs potential curve displays a bell shape,despite the low concentration of free charge carriers,because the dielectric response reduces the threshold concentration for the bell-to camelshape transition.The presented theory does not take into account overscreening and oscillating charge distributions in the electrical double layer.But in spite of the simplicity of the model,its findings demonstrate a clear physical effect:a preference to be a charged monopole rather than a dipole(or higher order multipole)in strong electric field.

Identifying the ground state structures of point defects in solids

Point defects are a universal feature of crystals.Their identification is addressed by combining experimental measurements with theoretical models.The standard modelling approach is,however,prone to missing the ground state atomic configurations associated with energy-lowering reconstructions from the idealised crystallographic environment.Missed ground states compromise the accuracy of calculated properties.To address this issue,we report an approach to navigate the defect configurational landscape using targeted bond distortions and rattling.Application of our workflow to eight materials(CdTe,GaAs,Sb_(2)S_(3),Sb_(2)Se_(3),CeO_(2),In_(2)O_(3),ZnO,anatase-TiO_(2))reveals symmetry breaking in each host crystal that is not found via conventional local minimisation techniques.The point defect distortions are classified by the associated physico-chemical factors.We demonstrate the impact of these defect distortions on derived properties,including formation energies,concentrations and charge transition levels.Our work presents a step forward for quantitative modelling of imperfect solids.

Accelerating GW calculations through machine-learned dielectric matrices

The GW approach produces highly accurate quasiparticle energies,but its application to large systems is computationally challenging due to the difficulty in computing the inverse dielectric matrix.To address this challenge,we develop a machine learning approach to efficiently predict density–density response functions(DDRF)in materials.An atomic decomposition of the DDRF is introduced,as well as the neighborhood density–matrix descriptor,both of which transform in the same way under rotations.The resulting DDRFs are then used to evaluate quasiparticle energies via the GW approach.To assess the accuracy of this method,we apply it to hydrogenated silicon clusters and find that it reliably reproduces HOMO–LUMO gaps and quasiparticle energy levels.The accuracy of the predictions deteriorates when the approach is applied to larger clusters than those in the training set.These advances pave the way for GW calculations of complex systems,such as disordered materials,liquids,interfaces,and nanoparticles.

High-throughput calculations of charged point defect properties with semi-local density functional theory— performance benchmarks for materials screening applications

Calculations of point defect energetics with Density Functional Theory(DFT)can provide valuable insight into several optoelectronic,thermodynamic,and kinetic properties.These calculations commonly use methods ranging from semi-local functionals with a-posteriori corrections to more computationally intensive hybrid functional approaches.For applications of DFT-based high-throughput computation for data-driven materials discovery,point defect properties are of interest,yet are currently excluded from available materials databases.This work presents a benchmark analysis of automated,semi-local point defect calculations with a-posteriori corrections,compared to 245“gold standard”hybrid calculations previously published.We consider three different a-posteriori correction sets implemented in an automated workflow,and evaluate the qualitative and quantitative differences among four different categories of defect information:thermodynamic transition levels,formation energies,Fermi levels,and dopability limits.We highlight qualitative information that can be extracted from high-throughput calculations based on semi-local DFT methods,while also demonstrating the limits of quantitative accuracy.

Automated high-throughput Wannierisation

Maximally-localised Wannier functions(MLWFs)are routinely used to compute from first-principles advanced materials properties that require very dense Brillouin zone integration and to build accurate tight-binding models for scale-bridging simulations.At the same time,high-throughput(HT)computational materials design is an emergent field that promises to accelerate reliable and cost-effective design and optimisation of new materials with target properties.The use of MLWFs in HT workflows has been hampered by the fact that generating MLWFs automatically and robustly without any user intervention and for arbitrary materials is,in general,very challenging.We address this problem directly by proposing a procedure for automatically generating MLWFs for HT frameworks.Our approach is based on the selected columns of the density matrix method and we present the details of its implementation in an AiiDA workflow.We apply our approach to a dataset of 200 bulk crystalline materials that span a wide structural and chemical space.We assess the quality of our MLWFs in terms of the accuracy of the band-structure interpolation that they provide as compared to the band-structure obtained via full first-principles calculations.Finally,we provide a downloadable virtual machine that can be used to reproduce the results of this paper,including all first-principles and atomistic simulations as well as the computational workflows.

Crowd-sourcing materials-science challenges with the NOMAD 2018 Kaggle competition

A public data-analytics competition was organized by the Novel Materials Discovery(NOMAD)Centre of Excellence and hosted by the online platform Kaggle by using a dataset of 3,000(Al_(x)GayIn_(1-x-y))_(2)O_(3) compounds.Its aim was to identify the best machinelearning(ML)model for the prediction of two key physical properties that are relevant for optoelectronic applications:the electronic bandgap energy and the crystalline formation energy.Here,we present a summary of the top-three ranked ML approaches.The first-place solution was based on a crystal-graph representation that is novel for the ML of properties of materials.The second-place model combined many candidate descriptors from a set of compositional,atomic-environment-based,and average structural properties with the light gradient-boosting machine regression model.The third-place model employed the smooth overlap of atomic position representation with a neural network.The Pearson correlation among the prediction errors of nine ML models(obtained by combining the top-three ranked representations with all three employed regression models)was examined by using the Pearson correlation to gain insight into whether the representation or the regression model determines the overall model performance.Ensembling relatively decorrelated models(based on the Pearson correlation)leads to an even higher prediction accuracy.

Ferroelectric control of single-molecule magnetism in 2D limit

The electric control of magnetic properties based on magnetoelectric effect is crucial for the development of future data storage devices.Here,based on first-principles calculations,a strong magnetoelectric effect is proposed to effectively switch on/off the magnetic states as well as alter the in-plane/perpendicular easy axes of metal-phthalocyanine molecules(MPc)by reversing the electric polarization of the underlying two-dimensional(2D)ferroelectric a-In2Se3 substrate with the application of an external electric field.The mechanism originates from the different hybridization between the molecule and the ferroelectric substrate in which the different electronic states of surface Se layer play a dominant role.Moreover,the magnetic moments and magnetic anisotropy energies(MAE)of OsPc/In2Se3 can be further largely enhanced by a functionalized atom atop the OsPc molecule.The I-OsPc/In2Se3 system possesses large MAE up to 30 meV at both polarization directions,which is sufficient for room-temperature applications.These findings provide a feasible scheme to realize ferroelectric control of magnetic states in 2D limit,which have great potential for applications in nanoscale electronics and spintronics.

Nanoparticle meta-grid for enhanced light extraction from light-emitting devices

Based on a developed theory,we show that introducing a meta-grid of sub-wavelength-sized plasmonic nanoparticles(NPs)into existing semiconductor light-emitting-devices(LEDs)can lead to enhanced transmission of light across the LED-chip/encapsulant interface.This results from destructive interference between light reflected from the chip/encapsulant interface and light reflected by the NP meta-grid,which conspicuously increase the efficiency of light extraction from LEDs.The“meta-grid”,should be inserted on top of a conventional LED chip within its usual encapsulating packaging.As described by the theory,the nanoparticle composition,size,interparticle spacing,and distance from the LED-chip surface can be tailored to facilitate maximal transmission of light emitted from the chip into its encapsulating layer by reducing the Fresnel loss.The analysis shows that transmission across a typical LEDchip/encapsulant interface at the peak emission wavelength can be boosted up to ~99%,which is otherwise mere~84% at normal incidence.The scheme could provide improved transmission within the photon escape cone over the entire emission spectrum of an LED.This would benefit energy saving,in addition to increasing the lifetime of LEDs by reducing heating.Potentially,the scheme will be easy to implement and adopt into existing semiconductor-device technologies,and it can be used separately or in conjunction with other methods for mitigating the critical angle loss in LEDs.

Computational design of bimetallic core-shell nanoparticles for hot-carrier photocatalysis

Computational design can accelerate the discovery of new materials with tailored properties,but applying this approach to plasmonic nanoparticles with diameters larger than a few nanometers is challenging as atomistic first-principles calculations are not feasible for such systems.In this paper,we employ a recently developed material-specific approach that combines effective mass theory for electrons with a quasistatic description of the localized surface plasmon to identify promising bimetallic core-shell nanoparticles for hot-electron photocatalysis.Specifically,we calculate hot-carrier generation rates of 100 different core-shell nanoparticles and find that systems with an alkali-metal core and a transition-metal shell exhibit high figures of merit for water splitting and are stable in aqueous environments.Our analysis reveals that the high efficiency of these systems is related to their electronic structure,which features a two-dimensional electron gas in the shell.Our calculations further demonstrate that hotcarrier properties are highly tunable and depend sensitively on core and shell sizes.The design rules resulting from our work can guide experimental progress towards improved solar energy conversion devices.

Frontier orbitals and quasiparticle energy levels in ionic liquids

Ionic liquids play an important role in many technological applications and a detailed understanding of their frontier molecular orbitals is required to optimize interfacial barriers,reactivity and stability with respect to electron injection and removal.In this work,we calculate quasiparticle energy levels of ionic liquids using first-principles many-body perturbation theory within the GW approximation and compare our results to various mean-field approaches,including semilocal and hybrid density-functional theory and Hartree-Fock.We find that the mean-field results depend qualitatively and quantitatively on the treatment of exchange-correlation effects,while GW calculations produce results that are in excellent agreement with experimental photoelectron spectra of gas phase ion pairs and ionic liquids.These results establish the GW approach as a valuable tool for understanding the electronic structures of ionic liquids.