Active-learning for global optimization of Ni-Ceria nanoparticles: The case of CeNiO (x = 1, 2, 3).

Ni-CeO nanoparticles (NPs) are promising nanocatalysts for water splitting and water gas shift reactions due to the ability of ceria to temporarily donate oxygen to the catalytic reaction and accept oxygen after the reaction is completed. Therefore, elucidating how different properties of the Ni-Ceria NPs relate to the activity and selectivity of the catalytic reaction, is of crucial importance for the development of novel catalysts. In this work the active learning (AL) method based on machine learning regression and its uncertainty is used for the global optimization of CeNiO (x = 1, 2, 3) nanoparticles, employing density functional theory calculations.

Reinforcement learning for in silico determination of adsorbate-substrate structures.

Reinforcement learning (RL) methods have helped to define the state of the art in the field of modern artificial intelligence, mostly after the breakthrough involving AlphaGo and the discovery of novel algorithms. In this work, we present a RL method, based on Q-learning, for the structural determination of adsorbate@substrate models in silico, where the minimization of the energy landscape resulting from adsorbate interactions with a substrate is made by actions on states (translations and rotations) chosen from an agent's policy. The proposed RL method is implemented in an early version of the reinforcement learning software for materials design and discovery (RLMaterial), developed in Python3.

Perovskenes: two-dimensional perovskite-type monolayer materials predicted by first-principles calculations.

Inspired by the successful transfer of freestanding ultrathin films of SrTiO and BiFeO onto various substrates without any thickness limitation, in this study, using density functional theory (DFT), we assessed the structural stability of a group of two-dimensional perovskite-type materials which we call perovskenes. Specifically, we analyzed the stability of 2D SrTiO, SrZrO, BaTiO, and BaZrO monolayers. Our simulations revealed that the 2D monolayers of SrTiO, BaTiO, and BaZrO are at least meta-stable, as confirmed by cohesive energy calculations, evaluation of elastic constants, and simulation of phonon dispersion modes.

How important is the amount of exact exchange for spin-state energy ordering in DFT? Case study of molybdenum carbide cluster, Mo4C2.

Since the form of the exact functional in density functional theory is unknown, we must rely on density functional approximations (DFAs). In the past, very promising results have been reported by combining semi-local DFAs with exact, i.e.

QMLMaterial─A Quantum Machine Learning Software for Material Design and Discovery.

Structural elucidation of chemical compounds is challenging experimentally, and theoretical chemistry methods have added important insight into molecules, nanoparticles, alloys, and materials geometries and properties. However, finding the optimum structures is a bottleneck due to the huge search space, and global search algorithms have been used successfully for this purpose. In this work, we present the quantum machine learning software/agent for materials design and discovery (QMLMaterial), intended for automatic structural determination for several chemical systems: atomic clusters, atomic clusters and the spin multiplicity together, doping in clusters or solids, vacancies in clusters or solids, adsorption of molecules or adsorbents on surfaces, and finally atomic clusters on solid surfaces/materials or encapsulated in porous materials.

Aphid control using Yunck essential oil in strawberry crop.

Strawberry is a food rich in bioactive compounds with great antioxidant potential. However, due to the high incidence of pests that affect crop cultivation, phytosanitary management still lacks control methods for agroecological cultivation. Thus, the present research aimed to evaluate the chemical composition and the potential of the essential oil of the leaves of in the control of in laboratory and semi-field conditions.

GAMaterial-A genetic-algorithm software for material design and discovery.

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 and automated structural determination for molecules, atomic clusters, nanoparticles, and solid surfaces, fundamental to understanding chemical processes in catalysis and environmental sciences, for instance.

Automatic structural elucidation of vacancies in materials by active learning.

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

Hexatetra-Carbon: A Novel Two-Dimensional Semiconductor Allotrope of Carbon.

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 carbon atom bonds with its four neighboring atoms in a 2D double layer crystal structure, which is formed by a network of carbon hexagonal prisms. Based on our calculations, it is found that hexatetra-carbon exhibits a good structural stability as confirmed by its rather high calculated cohesive energy -6.

A new active learning approach for adsorbate-substrate structural elucidation in silico.

Adsorbate interactions with substrates (e.g. surfaces and nanoparticles) are fundamental for several technologies, such as functional materials, supramolecular chemistry, and solvent interactions.

Bioactivity and molecular properties of Phenoxyacetic Acids Derived from Eugenol and Guaiacol compared to the herbicide 2,4-D.

Herbicides are agrochemicals applied in the control of weeds. With the frequent and repetitive use of these substances, serious problems have been reported. Compounds of natural origin and their derivatives are attractive options to obtain new compounds with herbicidal properties.

Reliability of semiempirical and DFTB methods for the global optimization of the structures of nanoclusters.

In this work, we explore the possibility of using computationally inexpensive electronic structure methods, such as semiempirical and DFTB calculations, for the search of the global minimum (GM) structure of chemical systems. The basic prerequisite that these inexpensive methods will need to fulfill is that their lowest energy structures can be used as starting point for a subsequent local optimization at a benchmark level that will yield its GM. If this is possible, one could bypass the global optimization at the expensive method, which is currently impossible except for very small molecules.

An adaptive design approach for defects distribution modeling in materials from first-principle calculations.

Designing and understanding the mechanism of non-stoichiometric materials with enhanced properties is challenging, both experimentally and even computationally, due to the large number of chemical spaces and their distributions through the material. In the current work, it is proposed a Machine Learning approach coupled with the Efficient Global Optimization (EGO) method-an Adaptive Design (AD)-to model local defects in materials from first-principle calculations. Our method takes into account the smallest sample set as possible, envisioning the material defect structure relationship with target properties for new insights.

Accurate SCC-DFTB Parametrization for Bulk Water.

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

Benzocaine complexation with p-sulfonic acid calix[n]arene: experimental ((1) H-NMR) and theoretical approaches.

The aim of this work was to study the interaction between the local anesthetic benzocaine and p-sulfonic acid calix[n]arenes using NMR and theoretical calculations and to assess the effects of complexation on cytotoxicity of benzocaine. The architectures of the complexes were proposed according to (1) H NMR data (Job plot, binding constants, and ROESY) indicating details on the insertion of benzocaine in the cavity of the calix[n]arenes. The proposed inclusion compounds were optimized using the PM3 semiempirical method, and the electronic plus nuclear repulsion energy contributions were performed at the DFT level using the PBE exchange/correlation functional and the 6-311G(d) basis set.

Imogolite-like nanotubes: structure, stability, electronic and mechanical properties of the phosphorous and arsenic derivatives.

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

Structural, mechanical and electronic properties of nano-fibriform silica and its organic functionalization by dimethyl silane: a SCC-DFTB approach.

Self-consistent-charge density-functional tight-binding (SCC-DFTB) approximated method was employed to investigate the structural, mechanical and electronic properties of the zigzag and armchair nano-fibriform silica (SNTs) and their outer surface organic modified derivatives (MSNTs) with internal radii in the range of 8 to 36 Å. The strain energy curves showed that the nanotubes structures are energetically more stable compared to the respective sheet structures. External hydroxyl dihedral angles in silica nanotubes have small influence, about 0.