Integrating firefly algorithm with density functional theory for global optimization of Al42− clusters

Bonobo Optimizer: A New Tool Toward the Global Optimization of Small Atomic Clusters

In the realm of structural and bonding investigations within chemical systems, elucidating global minimum energy configurations stands as a paramount goal. As the systems increase in size and complexity, this pursuit becomes progressively challenging. Herein, we introduce Bonobo optimizer (BO), a metaheuristic algorithm inspired by the social and reproductive behaviors of bonobos, to the domain of chemical problem solving. Focusing on small carbon clusters, this study systematically evaluates BO's performance, showcasing its robustness and efficiency. Parametric studies highlight the algorithm's adaptability, consistently converging to global minimum structures. Rigorous statistical validation supports the results, and a comparative analysis against established global optimization algorithms underlines BO's superior efficiency. This exploration extends the applicability of BO to the optimization of atomic clusters, providing a promising avenue for future advancements in computational chemistry.

Global Optimization: A Soft Computing Perspective

Tackling the problem of global optimization is one of the most important domains that physicists and chemists are working on. The use of soft computing (SC) techniques has made this easier by reducing nonlinearity and instability and making it technologically rich. This Perspective aims at explaining the basic mathematical models of the most efficient and commonly used SC techniques in computational chemistry for finding the global minimum (GM) energy structures of chemical systems. In this Perspective, we discuss the global optimizations of several chemical systems that our group has worked on using CNN, PSO, FA, ABC, BO, and some hybrid techniques, two of which are interfaced to achieve better-quality results.

Growth mechanism, electronic properties and spectra of aluminum clusters

Density functional theory (DFT) and particle swarm optimization (PSO) have been applied to study the growth behavior, electronic properties and spectra of neutral, anionic and cationic aluminum clusters with 3-20 atoms. Many isomers have been obtained through a comprehensive structural search. The results indicate that the ground state structures of neutral and anionic aluminum clusters follow an identical periodic growth law. When the number of atoms is 6-11 and 13-18, Al atoms in these clusters grow around an octahedral cluster nucleus and an icosahedral cluster nucleus, respectively. For Al_n⁺ (n ≤ 14 and n ≠ 7) clusters, the most stable structure is different from that of Al_n or Al_n^-clusters. When n > 14, the ground state structure of Al_n⁺ clusters is similar to that of Al_n or Al_n^-clusters. The electronic properties of aluminum clusters have been analyzed by the averaged binding energy, second-order difference of energy, energy gap and dissociation energy. It is found that the Al₇⁺ and Al₁₃^- clusters have very high stability and a large energy gap and can be regarded as two superatoms. The aluminum cluster with 18 or 40 valence electrons are the least likely to lose an electron. The dissociation behavior of Al_n⁺ clusters caused by collision is reasonably explained by means of the dissociation energy. The optical absorption spectra of neutral aluminum clusters have been simulated by using the time-dependent density functional theory. The ground states of anionic aluminum clusters have been determined by comparing theoretical photoelectron spectra (PES) with experimental findings. Infrared and Raman spectra of cationic aluminum clusters have been forecasted and can assist in identifying the most stable structure in future experiments.

Atomic Clusters: Structure, Reactivity, Bonding, and Dynamics

Atomic clusters lie somewhere in between isolated atoms and extended solids with distinctly different reactivity patterns. They are known to be useful as catalysts facilitating several reactions of industrial importance. Various machine learning based techniques have been adopted in generating their global minimum energy structures. Bond-stretch isomerism, aromatic stabilization, Rener-Teller effect, improved superhalogen/superalkali properties, and electride characteristics are some of the hallmarks of these clusters. Different all-metal and nonmetal clusters exhibit a variety of aromatic characteristics. Some of these clusters are dynamically stable as exemplified through their fluxional behavior. Several of these cluster cavitands are found to be agents for effective confinement. The confined media cause drastic changes in bonding, reactivity, and other properties, for example, bonding between two noble gas atoms, and remarkable acceleration in the rate of a chemical reaction under confinement. They have potential to be good hydrogen storage materials and also to activate small molecules for various purposes. Many atomic clusters show exceptional opto-electronic, magnetic, and nonlinear optical properties. In this Review article, we intend to highlight all these aspects.

Efficient Search for Energetically Favorable Molecular Conformations against Metastable States via Gray-Box Optimization

In order to accurately understand and estimate molecular properties, finding energetically favorable molecular conformations is the most fundamental task for atomistic computational research on molecules and materials. Geometry optimization based on quantum chemical calculations has enabled the conformation prediction of arbitrary molecules, including de novo ones. However, it is computationally expensive to perform geometry optimizations for enormous conformers. In this study, we introduce the gray-box optimization (GBO) framework, which enables optimal control over the entire geometry optimization process, among multiple conformers. Algorithms designed for GBO roughly estimate energetically preferable conformers during their geometry optimization iterations. They then preferentially compute promising conformers. To evaluate the performance of the GBO framework, we applied it to a test set consisting of seven dipeptides and mycophenolic acid to determine their stable conformations at the density functional theory level. We thus preferentially obtained energetically favorable conformations. Furthermore, the computational costs required to find the most stable conformation were significantly reduced (approximately 1% on average, compared to the naive approach for the dipeptides).