A fuzzy classification framework to identify equivalent atoms in complex materials and molecules.

The nature of an atom in a bonded structure-such as in molecules, in nanoparticles, or in solids, at surfaces or interfaces-depends on its local atomic environment. In atomic-scale modeling and simulation, identifying groups of atoms with equivalent environments is a frequent task, to gain an understanding of the material function, to interpret experimental results, or to simply restrict demanding first-principles calculations. However, while routine, this task can often be challenging for complex molecules or non-ideal materials with breaks in symmetries or long-range order. To automatize this task, we here present a general machine-learning framework to identify groups of (nearly) equivalent atoms. The initial classification rests on the representation of the local atomic environment through a high-dimensional smooth overlap of atomic positions (SOAP) vector. Recognizing that not least thermal vibrations may lead to deviations from ideal positions, we then achieve a fuzzy classification by mean-shift clustering within a low-dimensional embedded representation of the SOAP points as obtained through multidimensional scaling. The performance of this classification framework is demonstrated for simple aromatic molecules and crystalline Pd surface examples.

Electrolyte contact changes nano-Li4Ti5O12 bulk properties via surface polarons.

It is of general interest to combine the faradaic processes based high energy density of a battery with the non-faradaic processes based high power density of a capacitor in one cell. Surface area and functional groups of electrode materials strongly affect these properties. For the anode material Li4Ti5O12 (LTO), we suggest a polaron based mechanism that influences Li ion uptake and mobility. Here we show electrolytes containing a lithium salt induce an observable change in the bulk NMR relaxation properties of LTO nano particles. The longitudinal 7Li NMR relaxation time of bulk LTO can change by almost an order of magnitude and, therefore, reacts very sensitively to the cation and its concentration in the surrounding electrolyte. The reversible effect is largely independent of the used anions and of potential anion decomposition products. It is concluded that lithium salt containing electrolytes increase the mobility of surface polarons. These polarons and additional lithium cations from the electrolyte can now diffuse through the bulk, induce the observed enhanced relaxation rate and enable the non-faradaic process. This picture of a Li+ ion equilibrium between electrolyte and solid may help with improving the charging properties of electrode materials.

Exploiting Nanoscale Complexion in LATP Solid-State Electrolyte via Interfacial Mg2+ Doping.

While great effort has been focused on bulk material design for high-performance All Solid-State Batteries (ASSBs), solid-solid interfaces, which typically extend over a nanometer regime, have been identified to severely impact cell performance. Major challenges are Li dendrite penetration along the grain boundary network of the Solid-State Electrolyte (SSE) and reductive decomposition at the electrolyte/electrode interface. A naturally forming nanoscale complexion encapsulating ceramic Li1+xAlxTi2-x(PO4)3 (LATP) SSE grains has been shown to serve as a thin protective layer against such degradation mechanisms. To further exploit this feature, we study the interfacial doping of divalent Mg2+ into LATP grain boundaries. Molecular Dynamics simulations for a realistic atomistic model of the grain boundary reveal Mg2+ to be an eligible dopant candidate as it rarely passes through the complexion and thus does not degrade the bulk electrolyte performance. Tuning the interphase stoichiometry promotes the suppression of reductive degradation mechanisms by lowering the Ti4+ content while simultaneously increasing the local Li+ conductivity. The Mg2+ doping investigated in this work identifies a promising route towards active interfacial engineering at the nanoscale from a computational perspective.

Tackling Structural Complexity in Li2S-P2S5 Solid-State Electrolytes Using Machine Learning Potentials.

The lithium thiophosphate (LPS) material class provides promising candidates for solid-state electrolytes (SSEs) in lithium ion batteries due to high lithium ion conductivities, non-critical elements, and low material cost. LPS materials are characterized by complex thiophosphate microchemistry and structural disorder influencing the material performance. To overcome the length and time scale restrictions of ab initio calculations to industrially applicable LPS materials, we develop a near-universal machine-learning interatomic potential for the LPS material class. The trained Gaussian Approximation Potential (GAP) can likewise describe crystal and glassy materials and different P-S connectivities PmSn. We apply the GAP surrogate model to probe lithium ion conductivity and the influence of thiophosphate subunits on the latter. The materials studied are crystals (modifications of Li3PS4 and Li7P3S11), and glasses of the xLi2S-(100 - x)P2S5 type (x = 67, 70 and 75). The obtained material properties are well aligned with experimental findings and we underscore the role of anion dynamics on lithium ion conductivity in glassy LPS. The GAP surrogate approach allows for a variety of extensions and transferability to other SSEs.

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

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.

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.

True Nature of the Transition-Metal Carbide/Liquid Interface Determines Its Reactivity.

Compound materials, such as transition-metal (TM) carbides, are anticipated to be effective electrocatalysts for the carbon dioxide reduction reaction (CO2RR) to useful chemicals. This expectation is nurtured by density functional theory (DFT) predictions of a break of key adsorption energy scaling relations that limit CO2RR at parent TMs. Here, we evaluate these prospects for hexagonal Mo2C in aqueous electrolytes in a multimethod experiment and theory approach. We find that surface oxide formation completely suppresses the CO2 activation. The oxides are stable down to potentials as low as -1.9 V versus the standard hydrogen electrode, and solely the hydrogen evolution reaction (HER) is found to be active. This generally points to the absolute imperative of recognizing the true interface establishing under operando conditions in computational screening of catalyst materials. When protected from ambient air and used in nonaqueous electrolyte, Mo2C indeed shows CO2RR activity.

Revised Atomic Charges for OPLS Force Field Model of Poly(Ethylene Oxide): Benchmarks and Applications in Polymer Electrolyte.

Poly(ethylene oxide) (PEO)-based polymers are common hosts in solid polymer electrolytes (SPEs) for high-power energy devices. Molecular simulations have provided valuable molecular insights into structures and ion transport mechanisms of PEO-based SPEs. The calculation of thermodynamic and kinetic properties rely crucially on the dependability of the molecular force fields describing inter- and intra-molecular interactions with the target system. In this work, we reparametrized atomic partial charges for the widely applied optimized potentials for liquid simulations (OPLS) force field of PEO. The revised OPLS force field, OPLSR, improves the calculations of density, thermal expansion coefficient, and the phase transition of the PEO system. In particular, OPLSR greatly enhances the accuracy of the calculated dielectric constant of PEO, which is critical for simulating polymer electrolytes. The reparameterization method was further applied to SPE system of PEO/LiTFSI with O:Li ratio of 16:1. Based on the reparametrized partial charges, we applied separate charge-scaling factors for PEO and Li salts. The charge-rescaled OPLSR model significantly improves the resulting kinetics of Li+ transport while maintaining the accurate description of coordination structures within PEO-based SPE. The proposed OPLSR force field can benefit the future simulation studies of SPE systems.

DFTB Modeling of Lithium-Intercalated Graphite with Machine-Learned Repulsive Potential.

Lithium ion batteries have been a central part of consumer electronics for decades. More recently, they have also become critical components in the quickly arising technological fields of electric mobility and intermittent renewable energy storage. However, many fundamental principles and mechanisms are not yet understood to a sufficient extent to fully realize the potential of the incorporated materials. The vast majority of concurrent lithium ion batteries make use of graphite anodes. Their working principle is based on intercalation, the embedding and ordering of (lithium-) ions in two-dimensional spaces between the graphene sheets. This important process, it yields the upper bound to a battery's charging speed and plays a decisive role in its longevity, is characterized by multiple phase transitions, ordered and disordered domains, as well as nonequilibrium phenomena, and therefore quite complex. In this work, we provide a simulation framework for the purpose of better understanding lithium-intercalated graphite and its behavior during use in a battery. To address large system sizes and long time scales required to investigate said effects, we identify the highly efficient, but semiempirical density functional tight binding (DFTB) as a suitable approach and combine particle swarm optimization (PSO) with the machine learning (ML) procedure Gaussian process regression (GPR) as implemented in the recently developed GPrep package for DFTB repulsion fitting to obtain the necessary parameters. Using the resulting parametrization, we are able to reproduce experimental reference structures at a level of accuracy which is in no way inferior to much more costly ab initio methods. We finally present structural properties and diffusion barriers for some exemplary system states.

Data-efficient iterative training of Gaussian approximation potentials: Application to surface structure determination of rutile IrO2 and RuO2.

Machine-learning interatomic potentials, such as Gaussian Approximation Potentials (GAPs), constitute a powerful class of surrogate models to computationally involved first-principles calculations. At a 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. Particularly 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.

IrO_{2} Surface Complexions Identified through Machine Learning and Surface Investigations.

A Gaussian approximation potential was trained using density-functional theory data to enable a global geometry optimization of low-index rutile IrO_{2} facets through simulated annealing. Ab initio thermodynamics identifies (101) and (111) (1×1) terminations competitive with (110) in reducing environments. Experiments on single crystals find that (101) facets dominate and exhibit the theoretically predicted (1×1) periodicity and x-ray photoelectron spectroscopy core-level shifts. The obtained structures are analogous to the complexions discussed in the context of ceramic battery materials.

Formation and stability of small polarons at the lithium-terminated Li4Ti5O12 (LTO) (111) surface.

Zero strain insertion, high cycling stability, and a stable charge/discharge plateau are promising properties rendering Lithium Titanium Oxide (LTO) a possible candidate for an anode material in solid state Li ion batteries. However, the use of pristine LTO in batteries is rather limited due to its electronically insulating nature. In contrast, reduced LTO shows an electronic conductivity several orders of magnitude higher. Studying bulk reduced LTO, we could show recently that the formation of polaronic states can play a major role in explaining this improved conductivity. In this work, we extend our study toward the lithium-terminated LTO (111) surface. We investigate the formation of polarons by applying Hubbard-corrected density functional theory. Analyzing their relative stabilities reveals that positions with Li ions close by have the highest stability among the different localization patterns.

Interface between graphene and liquid Cu from molecular dynamics simulations.

Controllable synthesis of defect-free graphene is crucial for applications since the properties of graphene are highly sensitive to any deviations from the crystalline lattice. We focus here on the emerging use of liquid Cu catalysts, which have high potential for fast and efficient industrial-scale production of high-quality graphene. The interface between graphene and liquid Cu is studied using force field and ab initio molecular dynamics, revealing a complete or partial embedding of finite-sized flakes. By analyzing flakes of different sizes, we find that the size-dependence of the embedding can be rationalized based on the energy cost of embedding vs bending the graphene flake. The embedding itself is driven by the formation of covalent bonds between the under-coordinated edge C atoms and the liquid Cu surface, which is accompanied by a significant charge transfer. In contrast, the central flake atoms are located around or slightly above 3 Å from the liquid Cu surface and exhibit weak van der Waals-bonding and much lower charge transfer. The structural and electronic properties of the embedded state revealed in our work provide the atomic-scale information needed to develop effective models to explain the special growth observed in experiments where various interesting phenomena such as flake self-assembly and rotational alignment, high growth speeds, and low defect densities in the final graphene product have been observed.

Mobile Small Polarons Qualitatively Explain Conductivity in Lithium Titanium Oxide Battery Electrodes.

Lithium titanium oxide Li4Ti5O12 is an intriguing anode material promising particularly long-life batteries, due to its remarkable phase stability during (dis)charging of the cell. However, its usage is limited by its low intrinsic electronic conductivity. Introducing oxygen vacancies can be one method for overcoming this drawback, possibly by altering the charge carrier transport mechanism. We use Hubbard corrected density functional theory to show that polaronic states in combination with a possible hopping mechanism can play a crucial role in the experimentally observed increase in electronic conductivity. To gauge polaronic charge mobility, we compute the relative stabilities of different localization patterns and estimate polaron hopping barrier heights.

Multi-ion Conduction in Li3OCl Glass Electrolytes.

Antiperovskite glasses such as Li3OCl and doped analogues have been proposed as excellent electrolytes for all-solid-state Li ion batteries (ASSB). Incorporating these electrolytes in ASSBs results in puzzling properties. This Letter describes a theoretical Li3OCl glass created by conventional melt-quench procedures. The ion conductivities are calculated using molecular dynamics based on a polarizable force field that is fitted to an extensive set of density functional theory-based energies, forces, and stresses for a wide range of nonequilibrium structures encompassing crystal, glass, and melt. We find high Li+ ion conductivity in good agreement with experiments. However, we also find that the Cl- ion is mobile as well so that the Li3OCl glass is not a single-ion conductor, with a transference number t + ≈ 0.84. This has important implications for its use as an electrolyte for all-solid-state batteries because the Cl could react irreversibly with the electrodes and/or produce glass decomposition during discharge-charge.

Ab Initio Simulation of pH-Sensitive Biomarkers in Magnetic Resonance Imaging.

An ab initio simulation scheme is introduced as a theoretical prescreening approach to facilitate and enhance the research for pH-sensitive biomarkers. The proton 1H and carbon 13C nuclear magnetic resonance (NMR) chemical shifts of the recently published marker for extracellular pH, [1,5-13C2]zymonic acid (ZA), and the as yet unpublished ( Z)-4-methyl-2-oxopent-3-enedioic acid (OMPD) were calculated with ab initio methods as a function of the pH. The influence of the aqueous solvent was taken into account either by an implicit solvent model or by explicit water molecules, where the latter improved the accuracy of the calculated chemical shifts considerably. The theoretically predicted chemical shifts allowed for a reliable NMR peak assignment. The p Ka value of the first deprotonation of ZA and OMPD was simulated successfully whereas the parametrization of the implicit solvent model does not allow for an accurate description of the second p Ka. The theoretical models reproduce the pH-induced chemical shift changes and the first p Ka with sufficient accuracy to establish the ab initio prescreening approach as a valuable support to guide the experimental search for pH-sensitive biomarkers.

The influence of conjugated alkynyl(aryl) surface groups on the optical properties of silicon nanocrystals: photoluminescence through in-gap states.

Developing new methods, other than size and shape, for controlling the optoelectronic properties of semiconductor nanocrystals is a highly desired target. Here we demonstrate that the photoluminescence (PL) of silicon nanocrystals (SiNCs) can be tuned in the range 685-800 nm solely via surface functionalization with alkynyl(aryl) (phenylacetylene, 2-ethynylnaphthalene, 2-ethynyl-5-hexylthiophene) surface groups. Scanning tunneling microscopy/spectroscopy on single nanocrystals revealed the formation of new in-gap states adjacent to the conduction band edge of the functionalized SiNCs. PL red-shifts were attributed to emission through these in-gap states, which reduce the effective band gap for the electron-hole recombination process. The observed in-gap states can be associated with new interface states formed via (-Si-C≡C-) bonds in combination with conjugated molecules as indicated by ab initio calculations. In contrast to alkynyl(aryl)s, the formation of in-gap states and shifts in PL maximum of the SiNCs were not observed with aryl (phenyl, naphthalene, 2-hexylthiophene) and alkynyl (1-dodecyne) surface groups. These outcomes show that surface functionalization with alkynyl(aryl) molecules is a valuable tool to control the electronic structure and optical properties of SiNCs via tuneable interface states, which may enhance the performance of SiNCs in semiconductor devices.

Addressing global uncertainty and sensitivity in first-principles based microkinetic models by an adaptive sparse grid approach.

In the last decade, first-principles-based microkinetic modeling has been developed into an important tool for a mechanistic understanding of heterogeneous catalysis. A commonly known, but hitherto barely analyzed issue in this kind of modeling is the presence of sizable errors from the use of approximate Density Functional Theory (DFT). We here address the propagation of these errors to the catalytic turnover frequency (TOF) by global sensitivity and uncertainty analysis. Both analyses require the numerical quadrature of high-dimensional integrals. To achieve this efficiently, we utilize and extend an adaptive sparse grid approach and exploit the confinement of the strongly non-linear behavior of the TOF to local regions of the parameter space. We demonstrate the methodology on a model of the oxygen evolution reaction at the Co3O4 (110)-A surface, using a maximum entropy error model that imposes nothing but reasonable bounds on the errors. For this setting, the DFT errors lead to an absolute uncertainty of several orders of magnitude in the TOF. We nevertheless find that it is still possible to draw conclusions from such uncertain models about the atomistic aspects controlling the reactivity. A comparison with derivative-based local sensitivity analysis instead reveals that this more established approach provides incomplete information. Since the adaptive sparse grids allow for the evaluation of the integrals with only a modest number of function evaluations, this approach opens the way for a global sensitivity analysis of more complex models, for instance, models based on kinetic Monte Carlo simulations.

Benchmarks and Dielectric Constants for Reparametrized OPLS and Polarizable Force Field Models of Chlorinated Hydrocarbons.

The knowledge of dielectric response properties of the environment is of paramount importance in many theoretical embedding methods and studies of solutes and of catalytic sites and processes in condensed phases. In particular, the realistic embedding of active sites into solid/liquid and liquid/liquid interfaces is a crucial point in the context of modeling energy conversion (e.g., electrochemical, photochemical, power-to-X) processes. Recently, the finding that the dielectric permeability of liquids near solid/liquid interfaces is far from being constant but deviates strongly from the bulk value within several nanometers from the interface has raised the interest in a more fundamental understanding of the response properties near interfaces. As these questions are hard to study experimentally, reliable theoretical models are required. Here we describe a careful first-principles based reparametrization of nonpolarizable molecular mechanics force fields for a class of technological relevant organic chlorinated hydrocarbon solvents which are immiscible with water. For the solvent 1,2-dichloroethane (1,2-DCE) we also present a new polarizable force field based on the Drude oscillator model. Its parametrization needs particular attention to avoid unphysical couplings between the internal torsional degree of freedom and the Drude oscillators, which could severely skew the response properties. The performance of this new set of force fields is critically assessed based on a comprehensive molecular dynamics study.