Oxide-derived copper (OD-Cu) catalysts are promising candidates for the electrochemical CO2 reduction reaction (CO2RR) due to enhanced selectivity toward ethylene over methane evolution, which has been linked presence of subsurface oxygen (Osb). In this work, Osb is investigated with theoretical methods. Although unstable in slab models, it becomes stabilized within a "manually" reduced OD-Cu nanocube model was calculated by self-consistent charge density functional tight binding (SCC-DFTB)....

We have investigated the structure and electronic of single- double-walled imogolite nanotubes with Ge Si as group IV element. While it is known from experiment, in case single-walled tubes confirmed by theory, are monodisperse diameter. show that also showing a preferred chirality (zigzag), resulting hydrogen-bond network on tube surfaces, there an exceptionally stable form intertube interaction supports formation nanotubes. The strongest stabilization has been found for indexes nine units...

Pyrite is the most common sulfide in Earth. In presence of arsenopyrite its oxidation delayed, and instead, increases rate, releasing As(III) As(V) species medium. DFT/plane waves calculations were performed on pyrite/arsenopyrite interface models to understand stability, structure, electronic properties interface. This first step influence inlaid pyrite mechanism. The slightly stressed with minor changes bond lengths lattice parameters respect pure phases. work adhesion formation energy...

Structural, electronic, and mechanical properties of single-walled chrysotile nanotubes have been investigated using the self-consistent charge density-functional tight-binding method (SCC-DFTB). The naturally occurring (NTs) are composed brucite, Mg(OH)2, layer in outer side tridymite, SiO2, inner side. zigzag (17,0)–(45,0) armchair (9,9)–(29,29) nanotubes, which correspond to radii ranging from 16 47 Å, calculated. SCC-DFTB results good agreement with available experimental previously...

Perovskenes: a novel family of high-stability two-dimensional perovskite-type monolayer materials with predicted electronic, optical, and thermoelectric properties via first-principles calculations.

Structural elucidation of chemical compounds is challenging experimentally, and theoretical chemistry methods have added important insight into molecules, nanoparticles, alloys, materials geometries properties. However, finding the optimum structures a bottleneck due to huge search space, global algorithms been used successfully for this purpose. In work, we present quantum machine learning software/agent design discovery (QMLMaterial), intended automatic structural determination in silico...

Employing first-principles calculations based on density functional theory (DFT), we designed a novel two-dimensional (2D) elemental monolayer allotrope of carbon called hexatetra-carbon. In the hexatetra-carbon structure, each atom bonds with its four neighboring atoms in 2D double layer crystal which is formed by network hexagonal prisms. Based our calculations, it found that exhibits good structural stability as confirmed rather high calculated cohesive energy -6.86 eV/atom, and absence...

Active learning (AL) has been widely applied in chemistry and materials science. In this work, we propose a quantum active (QAL) method for automatic structural determination of doped nanoparticles, where machine (QML) models regression are used iteratively to indicate new structures be calculated by Density Functional Theory (DFT) or Based Tight Binding (DFTB) data acquisition is retrain the QML models. The QAL implemented Quantum Machine Learning Software/ Agent Material Design Discovery...

Imogolite is a single-walled aluminosilicate nanotube (NT) found in nature that can be easily synthesized, as well its analogue aluminogermanate NT. Based on geometrical assumptions and pKa values, species such H3PO4, H3PO3, H3AsO3, H3AsO4 could also candidates to form imogolite-like structures. In the present work, we provide insights about stability, electronic, structural mechanical properties of possible imogolite like NTs by means self-consistent charge density-functional tight-binding...

Genetic algorithms (GAs) are stochastic global search methods inspired by biological evolution. They have been used extensively in chemistry and materials science coupled with theoretical methods, ranging from force-fields to high-throughput first-principles methods. The methodology allows an accurate automated structural determination for molecules, atomic clusters, nanoparticles, solid surfaces, fundamental understanding chemical processes catalysis environmental sciences, instance. In...

The SCC-DFTB repulsion parameters based on the material science set (matsci) were redesigned to describe structure and dynamic properties of bulk liquid water. iterative Boltzman inversion (IBI) approach was applied by simultaneously correcting O–H O–O energy contribution develop new water–matsci water–matsci–UFF parameters. provide radial distribution functions in excellent agreement with available state-of-the-art experimental data. parametrization is compute binding energies a water...

Abstract Reinforcement learning (RL) methods have helped to define the state of art in field modern artificial intelligence, mostly after breakthrough involving AlphaGo and discovery novel algorithms. In this work, we present a RL method, based on Q‐learning, for structural determination adsorbate@substrate models silico, where minimization energy landscape resulting from adsorbate interactions with substrate is made by actions states (translations rotations) chosen an agent's policy. The...

The aim of this work was to study the interaction between local anesthetic benzocaine and p-sulfonic acid calix[n]arenes using NMR theoretical calculations assess effects complexation on cytotoxicity benzocaine. architectures complexes were proposed according (1) H data (Job plot, binding constants, ROESY) indicating details insertion in cavity calix[n]arenes. inclusion compounds optimized PM3 semiempirical method, electronic plus nuclear repulsion energy contributions performed at DFT level...

The electronic, structural and mechanical properties of the modified imogolites have been investigated using self consistent charge-density functional-tight binding method with "a posteriori" treatment dispersion interaction (SCC-DFTB-D). zigzag (12,0) imogolite has used as initial structure for calculations. functionalization interior nanotubes by organosilanes heat leading to dehydroxylation silanols were investigated. reaction trimethylmethoxysilanes is favored arrangement different...

Finding the optimum structures of non-stoichiometric or berthollide materials, such as (1D, 2D, 3D) materials nanoparticles (0D), is challenging due to huge chemical/structural search space. Computational methods coupled with global optimization algorithms have been used successfully for this purpose. In work, we developed an artificial intelligence method based on active learning (AL) Bayesian automatic structural elucidation vacancies in solids and nanoparticles. AL uses machine regression...

Since the form of exact functional in density theory is unknown, we must rely on approximations (DFAs). In past, very promising results have been reported by combining semi-local DFAs with exact, i.e. Hartree–Fock, exchange. However, spin-state energy ordering and predictions global minima structures are particularly sensitive to choice hybrid amount This has already qualitatively described for single conformations, reactions, a limited number conformations. Here, analyzed mixing exchange...

Finding the optimum material with improved properties for a given application is challenging because data acquisition in materials science and chemistry time consuming expensive. Therefore, dealing small datasets reality chemistry, whether are obtained from synthesis or computational experiments. In this work, we propose new artificial intelligence method based on active learning (AL) to guide experiments as little possible, experimental design. The AL applied ABO 3 perovskites, where...

Abstract Ni‐CeO 2 nanoparticles (NPs) are promising nanocatalysts for water splitting and gas shift reactions due to the ability of ceria temporarily donate oxygen catalytic reaction accept after is completed. Therefore, elucidating how different properties Ni‐Ceria NPs relate activity selectivity reaction, crucial importance development novel catalysts. In this work active learning (AL) method based on machine regression its uncertainty used global optimization Ce (4‐x) Ni x O (8‐x) (x = 1,...