Synthesis, Characterization and Computational Evaluation of Bicyclooctadienes Towards Molecular Solar Thermal Energy Storage

Molecular solar-thermal energy storage (MOST) systems are based on photoswitches that reversibly convert solar energy into chemical energy. In this context, bicyclooctadienes (BOD) undergo a photoinduced transformation to the corresponding...

Computational Discovery of TTF Molecules with Deep Generative Models

We present a computational workflow based on quantum chemical calculations and generative models based on deep neural networks for the discovery of novel materials. We apply the developed workflow to search for molecules suitable for the fusion of triplet-triplet excitations (triplet-triplet fusion, TTF) in blue OLED devices. By applying generative machine learning models, we have been able to pinpoint the most promising regions of the chemical space for further exploration. Another neural network based on graph convolutions was trained to predict excitation energies; with this network, we estimate the alignment of energy levels and filter molecules before running time-consuming quantum chemical calculations. We present a comprehensive computational evaluation of several generative models, choosing a modification of the Junction Tree VAE (JT-VAE) as the best one in this application. The proposed approach can be useful for computer-aided design of materials with energy level alignment favorable for efficient energy transfer, triplet harvesting, and exciton fusion processes, which are crucial for the development of the next generation OLED materials.

Similarity-based interference in sentence comprehension in aphasia: A computational evaluation of two models of cue-based retrieval

Sentence comprehension requires the listener to link incoming words with short-term memory representations in order to build linguistic dependencies. The cue-based retrieval theory of sentence processing predicts that the retrieval of these memory representations is affected by similarity-based interference. We present the first large-scale computational evaluation of interference effects in two models of sentence processing – the activation-based model, and a modification of the direct-access model – in individuals with aphasia (IWA) and control participants in German. The parameters of the models are linked to prominent theories of processing deficits in aphasia, and the models are tested against two linguistic constructions in German: Pronoun resolution and relative clauses. The data come from a visual-world eye-tracking experiment combined with a sentence-picture matching task. The results show that both control participants and IWA are susceptible to retrieval interference, and that a combination of theoretical explanations (intermittent deficiencies, slow syntax, and resource reduction) can explain IWA’s deficits in sentence processing. Model comparisons reveal that both models have a similar predictive performance in pronoun resolution, but the activation-based model outperforms the direct-access model in relative clauses.

Activation Mechanism of Nickel(0) N-Heterocyclic Carbene Catalysts Stabilized by Fumarate Ligands

Nickel(0) catalysts of N-heterocyclic carbenes (NHCs) that are stabilized by electronic deficient alkenes possess desirable properties of air tolerance and ease of handling while also retaining high catalytic activities. Since catalyst stability often comes at the expense of catalytic activity, we have undertaken a detailed study of the activation mechanism of a new IMes-nickel(0) catalyst stabilized by di-(o-tolyl) fumarate that converts the stable pre-catalyst form into a catalytically active species. Computational evaluation provided evidence against a simple ligand exchange as the activation mechanism, and a stoichiometric activation process that covalently modifies the stabilizing ligand was identified. A detailed computational picture for the activation process was developed, with predictive insights that explain the catalyst features that lead to both active and inactive precatalysts.

Refined cut selection for benders decomposition: applied to network capacity expansion problems

In this paper, we present a new perspective on cut generation in the context of Benders decomposition. The approach, which is based on the relation between the alternative polyhedron and the reverse polar set, helps us to improve established cut selection procedures for Benders cuts, like the one suggested by Fischetti et al. (Math Program Ser B 124(1–2):175–182, 2010). Our modified version of that criterion produces cuts which are always supporting and, unless in rare special cases, facet-defining. We discuss our approach in relation to the state of the art in cut generation for Benders decomposition. In particular, we refer to Pareto-optimality and facet-defining cuts and observe that each of these criteria can be matched to a particular subset of parametrizations for our cut generation framework. As a consequence, our framework covers the method to generate facet-defining cuts proposed by Conforti and Wolsey (Math Program Ser A 178:1–20, 2018) as a special case. We conclude the paper with a computational evaluation of the proposed cut selection method. For this, we use different instances of a capacity expansion problem for the european power system.