Theoretical Prediction of Thermal Expansion Anisotropy for Y2Si2O7 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. In this work, we use machine learning to train a deep neural network potential (DNP) for Y2Si2O7, which is then applied to calculate the thermodynamic and thermo-mechanical properties at near-DFT accuracy much faster and using less computational resources than DFT. We use this DNP to predict the phonon-based thermodynamic properties of Y2Si2O7 with good agreement to DFT and experiments. We also utilize the DNP to calculate the anisotropic, lattice direction-dependent coefficients of thermal expansion (CTEs) for Y2Si2O7. Molecular dynamics trajectories using the DNP correctly demonstrate the accurate prediction of the anisotropy of the CTE in good agreement with the diffraction experiments. In the future, this DNP could be applied to accelerate additional property calculations for Y2Si2O7 compared to DFT or experiments.

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 H2 gas, a reaction pathway of CuO → monoclinic m-Cu4 O3 → Cu2 O is demonstrated and identifies interfacial reaction fronts at the atomic scale, where the Cu2 O/m-Cu4 O3 interface shows a diffuse-type interfacial transformation; while the lateral flow of interfacial ledges appears to control the m-Cu4 O3 /CuO transformation. Together with atomistic modeling, it is shown that such a multi-interface transformation results from the surface-reaction-induced formation of oxygen vacancies that diffuse into deeper atomic layers, thereby resulting in the formation of the lower oxides of Cu2 O and m-Cu4 O3 , and activate the interfacial transformations. These results demonstrate the lively dynamics at the reaction fronts of the multiple interfaces and have substantial implications for controlling the microstructure and interphase boundaries by coupling the interplay between the surface reaction dynamics and the resulting mass transport and phase evolution in the subsurface and bulk.

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. Nanoparticles were produced using a state-of-the-art inert-gas aggregation source and analyzed using transmission electron microscopy and energy-dispersive X-ray spectroscopy. Machine-learning-assisted molecular dynamics simulations were employed to model the crystallization process from liquid droplets to nanocrystals. Our study finds a preponderance of nanoparticles with five-fold symmetric morphology, including icosahedra and decahedra which is consistent with previous results on mono-metallic nanoparticles. However, we observed that gold atoms, rather than silver atoms, segregate at the surface of the obtained nanoparticles for all the considered alloy compositions. These segregation tendencies are in contrast to previous studies and have consequences on the crystallization dynamics and the subsequent crystal ordering. We finally showed that the underpinning of this surprising segregation dynamics is due to charge transfer and electrostatic interactions rather than surface energy considerations.

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. We show that the mitigation of finite-size effects that typically skew the predicted melting temperatures in DFT-TPC simulations in excess of several hundred kelvin requires models with ∼16 000 atoms and >100 ps molecular dynamics trajectories. In addition, the DNP can successfully describe MgO metallization well at increased pressures that are captured by DFT but missed by classical interatomic potentials.

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 one-step, one-charge transfer process. Owing to a universal and absolute 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. Furthermore, the model resolves controversies over analytical studies for HER kinetics.

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. We created and validated DNPs for AgxOy, CuxOy MgxOy, PtxOy, and ZnxOy, whereby each system was trained without any limitations on oxidation states. We illustrate how the database can be augmented to enhance the DNP transferability for a new polymorph, surface energies, and thermal expansion. In addition, we show that these potentials can correctly interpolate significant pressure and temperature ranges, exhibit stability over long molecular dynamics simulation time scales, and replicate nonharmonic thermal expansion, consistent with experimental results.

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. We apply the developed methodology to the strongly anharmonic α-CsPbBr_{3} that displays phonon instability within the harmonic approximation in the whole Brillouin zone. While the SC1 theory significantly underestimates the cubic-to-tetragonal phase transition temperature (T_{c}) by more than 50%, we show that our approach yields T_{c}=404-423 K, in excellent agreement with the experimental value of 403 K. We also demonstrate that an accurate determination of QP peaks is paramount for quantitative prediction and elucidation of the phonon linewidth.

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. In addition, the model reproduces with high fidelity the experimental exchange currents for elemental metal catalysts over 15 orders of magnitude and is consistent with the recently proposed analytical model based on a data-driven approach. Our findings based on fundamental electrochemistry principles are general and can be applied to other reactions including CO2 reduction, metal oxidation, and lithium (de)intercalation reactions.

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). From nanosecond (ns) to second timescales, our multiscale computational methodology can observe rare stochastic events not typically seen with standard MD, closing the gap between computational and experimental timescales for surface segregation. Rapid diffusion of a vacancy to the slab is resolved by all four methods in tens of nanoseconds. Stochastic re-entry of vacancies into the subsurface, however, is only seen on the microsecond timescale in the two KMC methods. Kinetic vacancy trapping on the surface and its effect on the segregation rate are discussed. The equilibrium composition profile of CuNi after segregation during annealing is estimated to occur on a timescale of seconds as determined by KMC, a result directly comparable to nanoscale experiments.

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. As a consequence, charges from deep levels can escape into bands, and light absorption can be extended to longer wavelengths, improving material performance. The phenomenon, demonstrated with iodine vacancy in CH3NH3PbI3 using a machine learning force field, can be expected for a variety of defects and dopants in many MHPs and other soft inorganic semiconductors. Since large-scale anharmonic motions can be precursors to chemical decomposition, a known problem with MHPs, we propose that materials that are stiffer than MHPs but softer than traditional inorganic semiconductors, such as Si and TiO2, may simultaneously exhibit excellent performance and stability.

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 in situ environmental transmission electron microscopy experiments with dynamical gas control and advanced data processing. We show that the Cu(100)-O (2√2 × âˆš2)R45° missing row reconstruction strongly favors upper terraces over lower terraces, while Cu(110)-O (2 × 1) "added row" reconstructions indicate slight preferences for upper or lower terraces, depending on oxygen concentration. The observed formation site preference and its variation with surface orientation and oxygen concentration are mechanistically explained by Ehrlich-Schwöbel barrier differences for oxygen diffusion on stepped surfaces.

Controlling the nucleation and growth of ultrasmall metal nanoclusters with MoS2 grain boundaries.

The stabilization of supported nanoclusters is critical for different applications, including catalysis and plasmonics. Herein we investigate the impact of MoS2 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. Our findings show that GBs stabilize the NCs up to a cluster size of nearly ten atoms, and with larger clusters having a similar binding to the pristine system. Notably, Pt monomers are found to be attracted to GB cores achieving 60% more stabilization compared to the pristine surface. Furthermore, we show that the nucleation and growth of the metal seeds are facile with low kinetic barriers, which are of similar magnitude to the diffusion barriers of metals on the pristine surface. The findings highlight the need to engineer ultrasmall NCs to take advantage of enhanced stabilization imparted by the GB region, particularly to circumvent sintering behavior for high-temperature applications.

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. The ensemble-average *OH adsorption energy is shown to explain the experimentally reported OER activities of PdAuAgTi and further is utilized to provide precise maps of the catalytic activity in the total composition space. Notably, the ORR activity of PdAuAgTi is found to be optimum in a narrow region of the composition space with 8-12 at. % Ti, which agrees with the experimental finding for enhanced ORR activity at 11-13 at. % Ti. In addition, replacing Au and Ag with the more cost-effective elements Cu and Zn is also shown to yield optimum catalysts for ORR. The current study shows that first-principles methods in conjunction with machine learning approaches are an effective tool for discovering multinary alloy systems for catalytic applications.

Weak Anharmonicity Rationalizes the Temperature-Driven Acceleration of Nonradiative Dynamics in Cu2ZnSnS4 Photoabsorbers.

We report a time-domain ab initio investigation of the nonradiative electron-hole recombination in quaternary Cu2ZnSnS4 (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. The overall nuclear anharmonic effect is weak in CZTS, which can be ascribed to their stable bonding environment. Phonon-induced loss of electronic coherence accelerates with temperature, due to stronger elastic electron-phonon scattering. The enhanced inelastic electron-phonon scattering decreases charge carrier lifetimes at higher temperatures, deteriorating material performance in optoelectronic devices. The detailed atomistic investigation of the temperature-dependent charge carrier dynamics, with particular focus on anharmonic effects, guides the development of more efficient solar cells based on CZTS and related semiconductor photoabsorbers.

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 Co25Mo45Fe10Ni10Cu10 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 NHx (x = 1, 2, 3) species on CoMoFeNiCu alloy surfaces with varied alloy compositions and atomic arrangements. We show that the 25/45 Co/Mo ratio identified experimentally as the most active composition for ammonia decomposition increases the likelihood that the surface adsorbs nitrogen equivalently to that of ruthenium while at the same time interacting moderately strongly with intermediates. Our study underscores the importance of computational modeling and machine learning to identify and optimize HEA alloys across their near-infinite materials design space.

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 Cu2O nano-island growth on Cu is layer-by-layer along Cu2O(110) planes, regardless of substrate orientation, contradicting classical models that predict multi-layer growth parallel to substrate surfaces. Growth kinetics show cubic relationships with time, indicating individual oxide monolayers follow Frank-van der Merwe growth whereas oxide islands follow Stranski-Krastanov growth. Cu sources for island growth transition from step edges to bulk substrates during oxidation, contrasting with classical corrosion theories which assume subsurface sources predominate. Our results resolve alternative epitaxial island growth mechanisms, improving the understanding of oxidation dynamics critical for advanced manufacturing at the nanoscale.

Assessing the Effects of Temperature and Oxygen Vacancy on Band Gap Renormalization in LaCrO3-δ: 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 LaCrO3-δ perovskite employing Allen-Heine-Cardona theory with first-principles simulations and corroborate with experimental observation. Antiferromagnetic cubic LaCrO3 shows a direct ground-state band gap of 2.62 eV that is reduced by over 1 eV due to the presence of oxygen vacancies, which can form endothermically. We find excellent agreement in temperature-dependent band gap shift in LaCrO3 between theory and an in-house experiment, proving that the theory can adequately predict renormalization on the band gap in a magnetic system. Band gaps in cubic LaCrO3-δ are found to monotonically narrow by 1.13 eV in pristine and by around 0.62 eV in oxygen-vacant structures as temperature increases from 0 to 1500 K. The predicted band gap variations are rationalized using an analytical model. The experimental zero-temperature band gaps are extracted from the model fits that can provide useful insights on the simulated band gaps.

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

We develop an ab initio nonadiabatic molecular dynamics (NAMD) method based on GW plus real-time Bethe-Salpeter equation (GW + rtBSE-NAMD) for the spin-resolved exciton dynamics. From investigations on MoS2, 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. Moreover, there are bright-to-dark exciton transitions induced by e-ph scattering and SOI. Our study proves that e-h many-body effects are essential to understand the spin-valley exciton dynamics in transition metal dichalcogenides and the newly developed GW + rtBSE-NAMD method provides a powerful tool for exciton dynamics in extended systems with time, space, momentum, energy, and spin resolution.