Determining the Adsorption Energetics of 2,3-Butanediol on RuO2(110): Coupling First-Principles Calculations With Global Optimizers

As climate change continues to pose a threat to the Earth due to the disrupted carbon cycles and fossil fuel resources remain finite, new sources of sustainable hydrocarbons must be explored. 2,3-butanediol is a potential source to produce butene because of its sustainability as a biomass-derived sugar. Butene is an attractive product because it can be used as a precursor to jet fuel, categorizing this work in the alcohol-to-jet pathway. While studies have explored the conversion of 2,3-butanediol to butene, little is understood about the fundamental reaction itself. We quantify the energetics for three pathways that were reported in the literature in the absence of a catalyst. One of these pathways forms a 1,3-butadiene intermediate, which is a highly exothermic process and thus is unlikely to occur since 2,3-butanediol likely gets thermodynamically trapped at this intermediate. We further determined the corresponding energetics of 2,3-butanediol adsorption on an ensemble of predetermined binding sites when it interacts with a defect-free stoichiometric RuO2(110) surface. Within this ensemble of adsorption sites, the most favorable site has 2,3-butanediol covering a Ru 5–coordinated cation. This approach is compared to that obtained using the global optimization algorithm as implemented in the Northwest Potential Energy Surface Search Engine. When using such a global optimization algorithm, we determined a more favorable ground-state structure that was missed during the manual adsorption site testing, with an adsorption energy of −2.61 eV as compared to −2.34 eV when using the ensemble-based approach. We hypothesize that the dehydration reaction requires a stronger chemical bond, which could necessitate the formation of oxygen vacancies. As such, this study has taken the first step toward the utilization of a global optimization algorithm for the rational design of Ru-based catalysts toward the formation of butene from sustainable resources.

Two modifications of extension of piyavskii’s global optimization algorithm to a function continuous on a compact interval and its convergence

В работах R.J. Vanderbei доказано, что непрерывная на выпуклом компактном множестве функция обладает свойством $\varepsilon $-липшицевости, обобщающим классическое понятие липшицевости. На основе этого свойства R.J. Vanderbei предложено одно обобщение метода Пиявского поиска глобального минимума непрерывной на отрезке функции. В данной работе предлагаются одна модификация этого метода для положительной $\varepsilon $-константы и одна модификация для положительной $\varepsilon $-константы и условия останова, не зависящего от выбора $\varepsilon $. Доказана сходимость предлагаемых алгоритмов, приведены результаты численных экспериментов на основе применения разработанной программы. Данные методы могут быть применены для оптимизации любых непрерывных на отрезке функций, например, при решении некоторых обратных задачах баллистики и в экономике в прямых задачах потребительского выбора маршаллианского типа с переменными ценами благ и с непрерывной функцией полезности. R.J. Vanderbei in his works proves that any continuous on a compact set function has the $\varepsilon $-Lipschitz property which extends conventional Lipschitz continuity. Based on this feature Vanderbei proposed one extension of Piyavskii’s global optimization algorithm to the continuous function case. In this paper we propose one modification of the Vanderbei’s algorithm for a positive $\varepsilon $-constant and another modification for a positive $\varepsilon $-constant and $\varepsilon $ value independent termination condition. We prove proposed methods convergence and perform several computational experiments with designed software for known test functions.

ESTIMATION OF HYDRODYNAMIC COEFFICIENTS OF A NONLINEAR MANOEUVRING MATHEMATICAL MODEL WITH FREE-RUNNING SHIP MODEL TESTS

Free-running model tests have been carried out based on a scaled chemical tanker ship model, having a guidance, control and navigation system developed and implemented in LabVIEW. In order to make the modelling more flexible and physically more realistic, a modified version of Abkowitz model was introduced. During the identification process, the model’s structure is fixed and its parameters have been obtained using system identification. A global optimization algorithm has been used to search the optimum values and minimize the loss functions. In order to reduce the effect of noise in the variables, different loss functions considering the empirical errors and generalization performance have been defined and implemented in the system identification program. The hydrodynamic coefficients have been identified based on the manoeuvring test data of free-running ship model. Validations of the system identification algorithm were also carried out and the comparisons with experiments demonstrated the effectiveness of the proposed system identification method.

BSO20: efficient brain storm optimization for real-parameter numerical optimization

Brain storm optimization (BSO) is an emerging global optimization algorithm. The primary idea is to divide the population into different clusters, and offspring are generated within a cluster or between two clusters. However, the problems of inefficient clustering strategy and insufficient exploration exist in BSO. In this paper, a novel and efficient BSO is proposed, called BSO20 (proposed in 2020). BSO20 pays attention to both the clustering strategy and the mutation strategy. First, we propose a hybrid clustering strategy, which combines two clustering strategies, i.e., nearest-better clustering and random grouping strategy. The size of the subpopulation clustered by two strategies is dynamically adjusted as the population evolves. Second, a modified mutation strategy is used in BSO20 to share information within a cluster or among multiple clusters to enhance the ability of exploration. BSO20 is tested on the problems of the 2017 IEEE Congress on Evolutionary Computation competition on real parameter numerical optimization. BSO20 is compared with several variants of BSO and two variants of particle swarm optimization, and the experimental results show that BSO20 is competitive.

MolFinder: an evolutionary algorithm for the global optimization of molecular properties and the extensive exploration of chemical space using SMILES

Here, we introduce a new molecule optimization method, MolFinder, based on an efficient global optimization algorithm, the conformational space annealing algorithm, and the SMILES representation. MolFinder finds diverse molecules with desired properties efficiently without any training and a large molecular database. Compared with recently proposed reinforcement-learning-based molecule optimization algorithms, MolFinder consistently outperforms in terms of both the optimization of a given target property and the generation of a set of diverse and novel molecules. The efficiency of MolFinder demonstrates that combinatorial optimization using the SMILES representation is a promising approach for molecule optimization, which has not been well investigated despite its simplicity. We believe that our results shed light on new possibilities for advances in molecule optimization methods.