Physics-Based Synthetic Data Model for Automated Segmentation in Catalysis Microscopy.

In catalysis research, the amount of microscopy data acquired when imaging dynamic processes is often too much for nonautomated quantitative analysis. Developing machine learned segmentation models is challenged by the requirement of high-quality annotated training data. We thus substitute expert-annotated data with a physics-based sequential synthetic data model.

Rationally designed laterally-condensed-catalysts deliver robust activity and selectivity for ethylene production in acetylene hydrogenation.

Future carbon management strategies require storage in elemental form, achievable through a sequence of CO hydrogenation reactions. Hydrogen is recycled from molecular intermediates by dehydrogenation, and side product acetylene selectively hydrogenated to ethylene. Existing Pd alloy catalysts for gas purification underperform in concentrated feeds, necessitating novel concepts.

The EFG Rosetta Stone: translating between DFT calculations and solid state NMR experiments.

We present a comprehensive study on the best practices for integrating first principles simulations in experimental quadrupolar solid-state nuclear magnetic resonance (SS-NMR), exploiting the synergies between theory and experiment for achieving the optimal interpretation of both. Most high performance materials (HPMs), such as battery electrodes, exhibit complex SS-NMR spectra due to dynamic effects or amorphous phases. NMR crystallography for such challenging materials requires reliable, accurate, efficient computational methods for calculating NMR observables from first principles for the transfer between theoretical material structure models and the interpretation of their experimental SS-NMR spectra.

Tracking Li atoms in real-time with ultra-fast NMR simulations.

We present for the first time a multiscale machine learning approach to jointly simulate atomic structure and dynamics with the corresponding solid state Nuclear Magnetic Resonance (ssNMR) observables. We study the use-case of spin-alignment echo (SAE) NMR for exploring Li-ion diffusion within the solid state electrolyte material LiPS (LPS) by calculating quadrupolar frequencies of Li. SAE NMR probes long-range dynamics down to microsecond-timescale hopping processes.

Materials Acceleration Platforms (MAPs): Accelerating Materials Research and Development to Meet Urgent Societal Challenges.

Climate Change and Materials Criticality challenges are driving urgent responses from global governments. These global responses drive policy to achieve sustainable, resilient, clean solutions with Advanced Materials (AdMats) for industrial supply chains and economic prosperity. The research landscape comprising industry, academe, and government identified a critical path to accelerate the Green Transition far beyond slow conventional research through Digital Technologies that harness Artificial Intelligence, Smart Automation and High Performance Computing through Materials Acceleration Platforms, MAPs.

Decoupling of Light and Dark Reactions in a 2D Niobium Tungstate for Light-Induced Charge Storage and On-Demand Hydrogen Evolution.

The direct coupling of light harvesting and charge storage in a single material opens new avenues to light storing devices. Here we demonstrate the decoupling of light and dark reactions in the two-dimensional layered niobium tungstate (TBA)(NbWO) for on-demand hydrogen evolution and solar battery energy storage. Light illumination drives Li/H photointercalation into the (TBA)(NbWO) photoanode, leading to small polaron formation assisted by structural distortions on the WO sublattice, along with a light-induced decrease in material resistance over 2 orders of magnitude compared to the dark.

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.

Electrolyte contact changes nano-LiTiO 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 LiTiO (LTO), we suggest a polaron based mechanism that influences Li ion uptake and mobility.

Tackling Structural Complexity in LiS-PS 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 calculations to industrially applicable LPS materials, we develop a near-universal machine-learning interatomic potential for the LPS material class.

Exploiting Nanoscale Complexion in LATP Solid-State Electrolyte via Interfacial Mg 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.

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 TiO core with coherent, monolayer-thin IrO or RuO films.

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

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.

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.

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.

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 (CORR) to useful chemicals. This expectation is nurtured by density functional theory (DFT) predictions of a break of key adsorption energy scaling relations that limit CORR at parent TMs. Here, we evaluate these prospects for hexagonal MoC in aqueous electrolytes in a multimethod experiment and theory approach.

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.

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.

Formation and stability of small polarons at the lithium-terminated LiTiO (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.

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.

Correlation between arterial blood gas and CT volumetry in patients with SARS-CoV-2 in the emergency department.

• The severity of SARS-CoV-2 patients is difficult to quickly assess in the ED. • The ABG test is a quick and easy tool that can help identify more severe patients. • CT cannot be used on all suspected SARS-CoV-2 infected patients admitted in ED.

Multi-ion Conduction in LiOCl Glass Electrolytes.

Antiperovskite glasses such as LiOCl 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 LiOCl glass created by conventional melt-quench procedures.

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 H and carbon C nuclear magnetic resonance (NMR) chemical shifts of the recently published marker for extracellular pH, [1,5-C]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 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.

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.