We utilize real-time time-dependent density functional theory and Ehrenfest dynamics scheme to investigate excited-state nonadiabatic dynamics of ligand dissociation of cobalt tricarbonyl nitrosyl, ...Co(CO)
NO, which is a precursor used for cobalt growth in advanced technologies, where the precursor's reaction is enhanced by electronic excitation. Based on the first-principles calculations, we demonstrate two dissociation pathways of the NO ligand on the precursor. Detailed electronic structures are further analyzed to provide an insight into dynamics following the electronic excitations. This study sheds light on computational demonstration and underlying mechanism of the electronic-excitation-induced dissociation, especially in molecules with complex chemical bonds such as the Co(CO)
NO.
Twisted bilayer graphene (TBLG) has emerged as an important platform for studying correlated phenomena, including unconventional superconductivity, in two-dimensional systems. The complexity of the ...atomic-scale structures in TBLG has made even the study of single-particle physics at low energies around the Fermi level, quite challenging. Our goal here is to provide a convenient and physically motivated picture of single-particle physics in TBLG using reduced models with the smallest possible number of localized orbitals. The reduced models exactly reproduce the low-energy bands of ab initio tight-binding models, including the effects of atomic relaxations. Furthermore, we obtain for the first time the corresponding Wannier orbitals that incorporate all symmetries of TBLG, which are also calculated as a function of angle, a requisite first step towards incorporating electron interaction effects. We construct eight-band and five-band models for the low-energy states for twist angles between 1.3^{∘} and 0.6^{∘}. The models are created using a multistep Wannier projection technique starting with appropriate ab initio k·p continuum Hamiltonians. Our procedure can also readily capture the perturbative effects of substrates and external displacement fields while offering a significant reduction in complexity for studying electron-electron correlation phenomena in realistic situations.
Adsorption energies on surfaces are excellent descriptors of their chemical properties, including their catalytic performance. High-throughput adsorption energy predictions can therefore help ...accelerate first-principles catalyst design. To this end, we present over 5000 DFT calculations of H adsorption energies on dilute Ag alloys and describe a general machine learning approach to rapidly predict H adsorption energies for new Ag alloy structures. We find that random forests provide accurate predictions and that the best features are combinations of traditional chemical and structural descriptors. Further analysis of our model errors and the underlying forest kernel reveals unexpected finite-size electronic structure effects: embedded dopant atoms can display counterintuitive behavior such as nonmonotonic trends as a function of composition and high sensitivity to dopants far from the adsorbing H atom. We explain these behaviors with simple tight-binding Hamiltonians and d-orbital densities of states. We also use variations among forest leaves to predict the uncertainty of predictions, which allows us to mitigate the effects of larger errors.
Dilute alloy materials hold great promise for enhancing selectivity in catalytic reactions. A major challenge with such catalytic materials is developing supported alloy nanoparticles (NPs) with a ...catalytically optimal composition that are stable with respect to sintering. Here, we demonstrate that NP Pd/Au catalysts with minor concentrations of Pd prepared by a colloid-templating approach are active for oxygen dissociation and catalytic oxidation, as exemplified by CO oxidation. Using electron microscopic imaging, infrared spectroscopy, and X-ray photoelectron spectroscopy, we demonstrate that Pd is stabilized within the Au nanoparticle in its metallic state with minor amounts of Pd at the surface. The Pd–Au nanoparticles are embedded in the colloidally templated silica and form a thermally stable catalyst at high temperature. Surface analysis by diffuse reflectance infrared spectroscopy indicates that the Pd atomic distribution at the surface varies with the bulk concentration, forming isolated Pd atoms at 2% Pd and a mixture of Pd monomers and small Pd clusters at 9% Pd. X-ray absorption fine structure analysis indicates that Pd is well distributed in the bulk of the nanoparticle. In-depth understanding of this new class of catalytic materials opens the way to a wide array of possibilities for crucial enhancement of activity and selectivity in catalysis.
Abstract
Availability of affordable and widely applicable interatomic potentials is the key needed to unlock the riches of modern materials modeling. Artificial neural network-based approaches for ...generating potentials are promising; however, neural network training requires large amounts of data, sampled adequately from an often unknown potential energy surface. Here we propose a self-consistent approach that is based on crystal structure prediction formalism and is guided by unsupervised data analysis, to construct an accurate, inexpensive, and transferable artificial neural network potential. Using this approach, we construct an interatomic potential for carbon and demonstrate its ability to reproduce first principles results on elastic and vibrational properties for diamond, graphite, and graphene, as well as energy ordering and structural properties of a wide range of crystalline and amorphous phases.
Using the recently proposed Susceptible–Asymptomatic–Infected–Vaccinated–Removed (SAIVR) model, we study the impact of key factors affecting COVID-19 vaccine rollout effectiveness and the ...susceptibility to resurgent epidemics. The SAIVR model expands the widely used Susceptible–Infectious–Removed (SIR) model for describing epidemics by adding compartments to include the asymptomatic infected (A) and the vaccinated (V) populations. We solve the model numerically to make predictions on the susceptibility to resurgent COVID-19 epidemics depending on initial vaccination coverage, importation loads, continuing vaccination, and more contagious SARS-CoV-2 variants, under persistent immunity and immunity waning conditions. The parameters of the model represent reported epidemiological characteristics of the SARS-CoV-2 virus such as the disease spread in countries with high levels of vaccination coverage. Our findings help explain how the combined effects of different vaccination coverage levels and waning immunity lead to distinct patterns of resurgent COVID-19 epidemics (either surges or endemic), which are observed in countries that implemented different COVID-19 health policies and achieved different vaccinated population plateaus after the vaccine rollouts in the first half of 2021.
We present a computational tool, eReaxFF, for simulating explicit electrons within the framework of the standard ReaxFF reactive force field method. We treat electrons explicitly in a pseudoclassical ...manner that enables simulation several orders of magnitude faster than quantum chemistry (QC) methods, while retaining the ReaxFF transferability. We delineate here the fundamental concepts of the eReaxFF method and the integration of the Atom-condensed Kohn–Sham DFT approximated to second order (ACKS2) charge calculation scheme into the eReaxFF. We trained our force field to capture electron affinities (EA) of various species. As a proof-of-principle, we performed a set of molecular dynamics (MD) simulations with an explicit electron model for representative hydrocarbon radicals. We establish a good qualitative agreement of EAs of various species with experimental data, and MD simulations with eReaxFF agree well with the corresponding Ehrenfest dynamics simulations. The standard ReaxFF parameters available in the literature are transferrable to the eReaxFF method. The computationally economic eReaxFF method will be a useful tool for studying large-scale chemical and physical systems with explicit electrons as an alternative to computationally demanding QC methods.
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