Cu-Ni Oxidation Mechanism Unveiled: A Machine Learning-Accelerated First-Principles and TEM Study.

The development of accurate methods for determining how alloy surfaces spontaneously restructure under reactive and corrosive environments is a key, long-standing, grand challenge in materials science. Using machine learning-accelerated density functional theory and rare-event methods, in conjunction with environmental transmission electron microscopy (ETEM), we examine the interplay between surface reconstructions and preferential segregation tendencies of CuNi(100) surfaces under oxidation conditions. Our modeling approach predicts that oxygen-induced Ni segregation in CuNi alloys favors Cu(100)-O c(2 × 2) reconstruction and destabilizes the Cu(100)-O (2√2 × √2)45° missing row reconstruction (MRR).

Overcoming Inaccuracies in Machine Learning Interatomic Potential Implementation for Ionic Vacancy Simulations.

Machine learning interatomic potentials, particularly ones based on deep neural networks, have taken significant strides in accelerating first-principles simulations, expanding the length and time scales of the simulations with accuracies akin to first-principles simulations. Notwithstanding their success in accurately describing the physical properties of pristine ionic systems with multiple oxidation states, herein we show that an implementation of deep neural network potentials (DNPs) yield vacancy formation energies in MgO with a significant ∼3 eV error. In contrast, we show that moment tensor potentials can accurately describe all properties of the oxide, including vacancy formation energies.

Cryptate binding energies towards high throughput chelator design: metadynamics ensembles with cluster-continuum solvation.

A tiered forcefield/semiempirical/-GGA pipeline together with a thermodynamic scheme designed with error cancellation in mind was developed to calculate binding energies of [2.2.2] cryptate complexes of mono- and divalent cations.

Theoretical Prediction of Thermal Expansion Anisotropy for YSiO Environmental Barrier Coatings Using a Deep Neural Network Potential and Comparison to Experiment.

Environmental barrier coatings (EBCs) are an enabling technology for silicon carbide (SiC)-based ceramic matrix composites (CMCs) in extreme environments such as gas turbine engines. However, the development of new coating systems is hindered by the large design space and difficulty in predicting the properties for these materials. Density Functional Theory (DFT) has successfully been used to model and predict some thermodynamic and thermo-mechanical properties of high-temperature ceramics for EBCs, although these calculations are challenging due to their high computational costs.

Exploring the formation of gold/silver nanoalloys with gas-phase synthesis and machine-learning assisted simulations.

While nanoalloys are of paramount scientific and practical interest, the main processes leading to their formation are still poorly understood. Key structural features in the alloy systems, including the crystal phase, chemical ordering, and morphology, are challenging to control at the nanoscale, making it difficult to extend their use to industrial applications. In this contribution, we focus on the gold/silver system that has two of the most prevalent noble metals and combine experiments with simulations to uncover the formation mechanisms at the atomic level.

Atomic Dynamics of Multi-Interfacial Migration and Transformations.

Redox-induced interconversions of metal oxidation states typically result in multiple phase boundaries that separate chemically and structurally distinct oxides and suboxides. Directly probing such multi-interfacial reactions is challenging because of the difficulty in simultaneously resolving the multiple reaction fronts at the atomic scale. Using the example of CuO reduction in H gas, a reaction pathway of CuO → monoclinic m-Cu O → Cu O is demonstrated and identifies interfacial reaction fronts at the atomic scale, where the Cu O/m-Cu O interface shows a diffuse-type interfacial transformation; while the lateral flow of interfacial ledges appears to control the m-Cu O /CuO transformation.

Machine-Learning Accelerated First-Principles Accurate Modeling of the Solid-Liquid Phase Transition in MgO under Mantle Conditions.

While accurate measurements of MgO under extreme high-pressure conditions are needed to understand and model planetary behavior, these studies are challenging from both experimental and computational modeling perspectives. Herein, we accelerate density functional theory (DFT) accurate calculations using deep neural network potentials (DNPs) trained over multiple phases and study the melting behavior of MgO via the two-phase coexistence (TPC) approach at 0-300 GPa and ≤9600 K. The resulting DNP-TPC melting curve is in excellent agreement with existing experimental studies.

The Impact of the 2021 Cyclone Shaheen on the Mental Health of Affected Omanis.

Objectives: Nine strong cyclones have been recorded in Oman in the last 50 years, the last being tropical cyclone Shaheen in October 2021, in the northern Oman area. The aim of our study was to determine the relationship between property loss and the mental health of residents after cyclone Shaheen.

Methods: We conducted a cross-sectional study among Omani citizens living in areas affected by cyclone Shaheen three to six months post-cyclone.

Simple Approach for Reconciling Cyclic Voltammetry with Hydrogen Adsorption Energy for Hydrogen Evolution Exchange Current.

Cyclic voltammetry (CV) is a standard technique to analyze the current-potential characteristics of the hydrogen evolution reaction (HER). Herein, we develop a computational quantum-scaled CV model for the HER building on the Butler-Volmer relation for a transfer process. Owing to a and rate constant verified by fitting to experimental cyclic voltammograms of elemental metals, we show that the model quantifies the exchange current─the main analytical descriptor for HER activity─solely using the hydrogen adsorption free energy obtained from density functional theory calculations.

Development and Validation of Versatile Deep Atomistic Potentials for Metal Oxides.

Machine learning interatomic potentials powered by neural networks have been shown to readily model a gradient of compositions in metallic systems. However, their application to date on ionic systems tends to focus on specific compositions and oxidation states owing to their more heterogeneous chemical nature. Herein we show that a deep neural network potential (DNP) can model various properties of metal oxides with different oxidation states without additional charge information.

First-Principles Phonon Quasiparticle Theory Applied to a Strongly Anharmonic Halide Perovskite.

Understanding and predicting lattice dynamics in strongly anharmonic crystals is one of the long-standing challenges in condensed matter physics. Here, we propose a first-principles method that gives accurate quasiparticle (QP) peaks of the phonon spectrum with strong anharmonic broadening. On top of the conventional first-order self-consistent phonon (SC1) dynamical matrix, the proposed method incorporates frequency renormalization effects by the bubble self-energy within the QP approximation.

Electron-Volt Fluctuation of Defect Levels in Metal Halide Perovskites on a 100 ps Time Scale.

Metal halide perovskites (MHPs) have gained considerable attention due to their excellent optoelectronic performance, which is often attributed to unusual defect properties. We demonstrate that midgap defect levels can exhibit very large and slow energy fluctuations associated with anharmonic acoustic motions. Therefore, care should be taken classifying MHP defects as deep or shallow, since shallow defects may become deep and vice versa.

Stabilizing magnetic skyrmions in constricted nanowires.

Magnetic skyrmions are topologically-protected chiral nano-scale spin structures that offer low power and high-density functionalities for spintronic devices. They behave as particles that can be moved, created and annihilated. These characteristics make them promising information-carrying bits, hence a precise control of the skyrmion motion is essential.

Reconciling the Volcano Trend with the Butler-Volmer Model for the Hydrogen Evolution Reaction.

The volcano trend has been widely utilized to forecast new optimum catalysts in computational chemistry while the Butler-Volmer relationship is the norm to explain current-potential characteristics from cyclic voltammetry in analytical chemistry. Herein, we develop an electrochemical model for hydrogen evolution reaction exchange currents that reconciles device-level chemistry, atomic-level volcano trend, and the Butler-Volmer relation. We show that the model is a function of the easy-to-compute hydrogen adsorption energy invariably obtained from first-principles atomic simulations.

Atomistic Mechanisms of Binary Alloy Surface Segregation from Nanoseconds to Seconds Using Accelerated Dynamics.

Although the equilibrium composition of many alloy surfaces is well understood, the rate of transient surface segregation during annealing is not known, despite its crucial effect on alloy corrosion and catalytic reactions occurring on overlapping timescales. In this work, CuNi bimetallic alloys representing (100) surface facets are annealed in vacuum using atomistic simulations to observe the effect of vacancy diffusion on surface separation. We employ multi-timescale methods to sample the early transient, intermediate, and equilibrium states of slab surfaces during the separation process, including standard MD as well as three methods to perform atomistic, long-time dynamics: parallel trajectory splicing (ParSplice), adaptive kinetic Monte Carlo (AKMC), and kinetic Monte Carlo (KMC).

Uneven Oxidation and Surface Reconstructions on Stepped Cu(100) and Cu(110).

How defects such as surface steps affect oxidation, especially initial oxide formation, is critical for nano-oxide applications in catalysis, electronics, and corrosion. We posit that surface reconstruction, a crucial intermediate oxidation step, can highlight initial oxide formation preferences and thus enable bridging the temporal and spatial scale gaps between atomistic simulations and experiments. We investigate the surface-step-induced uneven surface oxidation on Cu(100) and Cu(110), using atomic-scale environmental transmission electron microscopy experiments with dynamical gas control and advanced data processing.

Optimizing the Catalytic Activity of Pd-Based Multinary Alloys toward Oxygen Reduction Reaction.

The development of cost-effective catalysts for oxygen reduction reaction (ORR) has an enormous impact on fuel cells toward highly efficient low emission energy conversion. Recently, a Pt-free multinary PdAuAgTi alloy was discovered with excellent ORR activity and low overpotential close to that of Pt. To rationalize the experimental results, a model based on first-principles methods accelerated with deep learning is developed to rapidly compute and with high fidelity the *OH adsorption energy on the alloyed surface.

Controlling the nucleation and growth of ultrasmall metal nanoclusters with MoS grain boundaries.

The stabilization of supported nanoclusters is critical for different applications, including catalysis and plasmonics. Herein we investigate the impact of MoS grain boundaries (GBs) on the nucleation and growth of Pt NCs. The optimum atomic structure of the metal clusters is obtained using an adaptive genetic algorithm that employs a hybrid approach based on atomistic force fields and density functional theory.

Weak Anharmonicity Rationalizes the Temperature-Driven Acceleration of Nonradiative Dynamics in CuZnSnS Photoabsorbers.

We report a time-domain ab initio investigation of the nonradiative electron-hole recombination in quaternary CuZnSnS (CZTS) at different temperatures using a combination of time-dependent density functional theory and nonadiabatic molecular dynamics. Our results demonstrate that higher temperatures increase both inelastic and elastic electron-phonon interactions. Elevated temperatures moderately increase the lattice anharmonicity and cause stronger fluctuations of electronic energy levels, enhancing the electron-phonon coupling.

Optimization of High-Entropy Alloy Catalyst for Ammonia Decomposition and Ammonia Synthesis.

The successful synthesis of high-entropy alloy (HEA) nanoparticles, a long-sought goal in materials science, opens a new frontier in materials science with applications across catalysis, structural alloys, and energetic materials. Recently, a CoMoFeNiCu HEA made of earth-abundant elements was shown to have a high catalytic activity for ammonia decomposition, which rivals that of state-of-the-art, but prohibitively expensive, ruthenium catalysts. Using a computational approach based on first-principles calculations in conjunction with data analytics and machine learning, we build a model to rapidly compute the adsorption energy of H, N, and NH ( = 1, 2, 3) species on CoMoFeNiCu alloy surfaces with varied alloy compositions and atomic arrangements.

Unusual layer-by-layer growth of epitaxial oxide islands during Cu oxidation.

Elucidating metal oxide growth mechanisms is essential for precisely designing and fabricating nanostructured oxides with broad applications in energy and electronics. However, current epitaxial oxide growth methods are based on macroscopic empirical knowledge, lacking fundamental guidance at the nanoscale. Using correlated in situ environmental transmission electron microscopy, statistically-validated quantitative analysis, and density functional theory calculations, we show epitaxial CuO nano-island growth on Cu is layer-by-layer along CuO(110) planes, regardless of substrate orientation, contradicting classical models that predict multi-layer growth parallel to substrate surfaces.

Assessing the Effects of Temperature and Oxygen Vacancy on Band Gap Renormalization in LaCrO: First-Principles and Experimental Corroboration.

Understanding the temperature dependence of functional properties in high-temperature gas sensors is vital for applications in combustion environments. Temperature effect on the electronic structure due to electron-phonon coupling is a key property of interest as this influences other responses of sensors. In this work, we assess the impact of temperature on band gap renormalization of pristine and oxygen-vacant LaCrO perovskite employing Allen-Heine-Cardona theory with first-principles simulations and corroborate with experimental observation.

Real-time -BSE investigations on spin-valley exciton dynamics in monolayer transition metal dichalcogenide.

We develop an ab initio nonadiabatic molecular dynamics (NAMD) method based on plus real-time Bethe-Salpeter equation ( + rtBSE-NAMD) for the spin-resolved exciton dynamics. From investigations on MoS, we provide a comprehensive picture of spin-valley exciton dynamics where the electron-phonon (e-ph) scattering, spin-orbit interaction (SOI), and electron-hole (e-h) interactions come into play collectively. In particular, we provide a direct evidence that e-h exchange interaction plays a dominant role in the fast valley depolarization within a few picoseconds, which is in excellent agreement with experiments.

Size-dependent polarizabilities and van der Waals dispersion coefficients of fullerenes from large-scale complex polarization propagator calculations.

While the anomalous non-additive size-dependencies of static dipole polarizabilities and van der Waals C dispersion coefficients of carbon fullerenes are well established, the widespread reported scalings for the latter (ranging from N to N) call for a comprehensive first-principles investigation. With a highly efficient implementation of the linear complex polarization propagator, we have performed Hartree-Fock and Kohn-Sham density functional theory calculations of the frequency-dependent polarizabilities for fullerenes consisting of up to 540 carbon atoms. Our results for the static polarizabilities and C coefficients show scalings of N and N, respectively, thereby deviating significantly from the previously reported values obtained with the use of semi-classical/empirical methods.

Tuning electrical and interfacial thermal properties of bilayer MoSvia electrochemical intercalation.

Layered two-dimensional (2D) materials such as MoShave attracted much attention for nano- and opto-electronics. Recently, intercalation (e.g.