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Finding the optimum material with improved properties for a given application is challenging because data acquisition in materials science and chemistry is time consuming and expensive. Therefore, dealing with small datasets is a reality in chemistry, whether the data are obtained from synthesis or computational experiments. In this work, we propose a new artificial intelligence method based on active learning (AL) to guide new experiments with as little data as possible, for optimum experimental design. The AL method is applied to AB[O.sub.3] perovskites, where a descriptor based on atomic properties was developed. Several regressor algorithms were employed: artificial neural network, Gaussian process, and support vector regressor. The developed AL method was applied in the experimental design of two important materials: non-stoichiometric perovskites ([Ba.sub.(1-x)][A.sub.x][Ti.sub.(1-y)][B.sub.y[O.sub.3]) due to substituting ionic sites with different concentrations and elements (A = Ca, Sr, Cd; B =Zr, Sn, Hf), aiming at the maximization of the energy storage density, and stoichiometric AB[O.sub.3] perovskites where different elements are changed in the A and B sites for the minimization of the formation energy. AL for experimental design is implemented in the machine learning agent for chemistry and design (MLChem4D) software, which has the potential to be applied in inorganic and organic synthesis (e.g., search for the optimum concentrations, catalysts, reactants, temperatures, and pH to improve the yield) and materials science (e.g., search the periodic table for the proper elements and their concentrations to improve the materials properties). The latter marks the first MLChem4D application for the design of perovskites. Key words: materials, perovskites, machine learning, active learning, design of experiment, transition metal oxides, 1. Introduction Gaining insight about the next experimental conditions to find new materials with improved properties (such as bandgap, mechanical properties, formation energy, convex hull energy, and energy storage density) [...]

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. Hartree–Fock, exchange. However, the spin-state energy ordering and the predictions of global minima structures are particularly sensitive to the choice of the hybrid functional and to the amount of exact exchange. This has been already qualitatively described for single conformations, reactions, and a limited number of conformations. Here, we have analyzed the mixing of exact exchange in exchange functionals for a set of several hundred isomers of the transition metal carbide, Mo4C2. The analysis of the calculated energies and charges using PBE0-type functional with varying amounts of exact exchange yields the following insights: (1) The sensitivity of spin-energy splitting is strongly correlated with the amount of exact exchange mixing. (2) Spin contamination is exacerbated when correlation is omitted from the exchange-correlation functional. (3) There is not one ideal value for the exact exchange mixing which can be used to parametrize or choose among the functionals. Calculated energies and electronic structures are influenced by exact exchange at a different magnitude within a given distribution; therefore, to extend the application range of hybrid functionals to the full periodic table the spin-energy splitting energies should be investigated. [ABSTRACT FROM AUTHOR]

Ni‐CeO2 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 Ce(4‐x)NixO(8‐x) (x = 1, 2, 3) nanoparticles, employing density functional theory calculations. Additionally, further investigation of the NPs by mass‐scaled parallel‐tempering Born‐Oppenheimer molecular dynamics resulted in the same putative global minimum structures found by AL, demonstrating the robustness of our AL search to learn from small datasets and assist in the global optimization of complex electronic structure systems. [ABSTRACT FROM AUTHOR]

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.x. RLMaterial interfaces with deMon2k, DFTB+, ORCA, and Quantum Espresso codes to compute the adsorbate@substrate energies. The RL method was applied for the structural determination of (i) the amino acid glycine and (ii) 2‐amino‐acetaldehyde, both interacting with a boron nitride (BN) monolayer, (iii) host‐guest interactions between phenylboronic acid and β‐cyclodextrin and (iv) ammonia on naphthalene. Density functional tight binding calculations were used to build the complex search surfaces with a reasonably low computational cost for systems (i)–(iii) and DFT for system (iv). Artificial neural network and gradient boosting regression techniques were employed to approximate the Q‐matrix or Q‐table for better decision making (policy) on next actions. Finally, we have developed a transfer‐learning protocol within the RL framework that allows learning from one chemical system and transferring the experience to another, as well as from different DFT or DFTB levels. [ABSTRACT FROM AUTHOR]

Explicit description of atomic polarizability is critical for the accurate treatment of inter-molecular interactions by force fields (FFs) in molecular dynamics (MD) simulations aiming to investigate complex electrostatic environments such as metal-binding sites of metalloproteins. Several models exist to describe key monovalent and divalent cations interacting with proteins. Many of these models have been developed from ion–amino-acid interactions and/or aqueous-phase data on cation solvation. The transferability of these models to cation–protein interactions remains uncertain. Herein, we assess the accuracy of existing FFs by their abilities to reproduce hierarchies of thousands of Ca2+–dipeptide interaction energies based on density-functional theory calculations. We find that the Drude polarizable FF, prior to any parameterization, better approximates the QM interaction energies than any of the non-polarizable FFs. Nevertheless, it required improvement in order to address polarization catastrophes where, at short Ca2+–carboxylate distances, the Drude particle of oxygen overlaps with the divalent cation. To ameliorate this, we identified those conformational properties that produced the poorest prediction of interaction energies to reduce the parameter space for optimization. We then optimized the selected cation–peptide parameters using Boltzmann-weighted fitting and evaluated the resulting parameters in MD simulations of the N-lobe of calmodulin. We also parameterized and evaluated the CTPOL FF, which incorporates charge-transfer and polarization effects in additive FFs. This work shows how QM-driven parameter development, followed by testing in condensed-phase simulations, may yield FFs that can accurately capture the structure and dynamics of ion–protein interactions. [ABSTRACT FROM AUTHOR]

The catalytic transformation of the heavy aromatics in bitumen into lighter components is the key to the upgrading and refining of the oil sands. To understand the chemical bonding in molybdenum carbide nanoparticle (MCNP) catalysts and the chemisorption bonds between the MCNPs and unsaturated hydrocarbons, the topological analysis of the electron localization function was applied to various MCNPs and their complexes with unsaturated hydrocarbons. For some of the smaller complexes, comparisons are made with the atoms in molecules approach, including the calculation of delocalization indices. The results are interpreted in the Lewis bonding scheme. It was found that the Mo-C bonding can be highly ionic in cases like [Mo.sub.8][C.sub.12] and MoC but shows significant covalent character in [Mo.sub.2]C, [Mo.sub.3]C, and [Mo.sub.28][C.sub.14]. The chemisorption bonds between hydrocarbons and the MCNPs involve electron sharing of various types with strong covalent character. The strong three- or four-center interactions determine the adsorption configurations of the hydrocarbons on the MCNPs. Derivatives of benzene show some different bonding features, which depend strongly on the substituent or the heteroatom. Key words: molybdenum carbide nanoparticles, unsaturated hydrocarbons, ELF, AIM, chemisorption. La transformation catalytique des composes aromatiques lourds du bitume en composes plus legers est un element important de la valorisation et du raffinage des sables bitumineux. En vue de comprendre les liaisons chimiques dans les catalyseurs a base de nanoparticules de carbure de molybdene (NPCM) et les liaisons par chimisorption entre les catalyseurs a base de NPCM et les hydrocarbures insatures, nous avons applique a divers NPCM et a leurs complexes avec des hydrocarbures insatures l'analyse topologique au moyen de la fonction de localisation electronique. Nous avons compare certains des complexes de petite taille, au moyen de la theorie quantique des atomes dans les molecules et du calcul des indices de delocalisation. Nous avons interprete les resultats des liaisons selon le schema de Lewis. En outre, nous avons observe que les liaisons Mo-C peuvent avoir un caractere tres ionique, comme les complexes [Mo.sub.8][C.sub.12] et MoC mais presentent un caractere covalent considerable dans les complexes [Mo.sub.2]C, [Mo.sub.3]C et [Mo.sub.28][C.sub.14]. Les liaisons par chimisorption entre les hydrocarbures et les NPCM font intervenir le partage d'electrons de divers types dont le caractere covalent est marque. Les fortes interactions a trois ou quatre atomes centraux determinent les configurations d'adsorption des hydrocarbures sur les NPCM. Les liaisons des derives aromatiques presentent quelques caracteristiques differentes, lesquelles dependent largement du substituant ou de l'heteroatome. [Traduit par la Redaction] Mots-cles : nanoparticules de carbure de molybdene, hydrocarbures insatures, ELF, AIM, chimisorption., Introduction In the petrochemical industry, the catalytic transformation of the heavy aromatics in bitumen into lighter components is the key to the upgrading and refining of the oil sands. (1) [...]