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.

System-Specific Parameter Optimization for Nonpolarizable and Polarizable Force Fields.

The accuracy of classical force fields (FFs) has been shown to be limited for the simulation of cation-protein systems despite their importance in understanding the processes of life. Improvements can result from optimizing the parameters of classical FFs or by extending the FF formulation by terms describing charge transfer (CT) and polarization (POL) effects. In this work, we introduce our implementation of the CTPOL model in OpenMM, which extends the classical additive FF formula by adding CT and POL.

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.