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Photoredox catalysis can be induced to activate organic substrates or to modulate the oxidation state of transition-metal catalysts via unique single-electron transfer processes, so as to achieve c...

We try to determine if machine learning (ML) methods, applied to the discovery of new materials on the basis of existing data sets, have the power to predict completely new classes of compounds (extrapolating) or perform well only when interpolating between known materials. We introduce the leave-one-group-out cross-validation, in which the ML model is trained to explicitly perform extrapolations of unseen chemical families. This approach can be used across materials science and chemistry problems to improve the added value of ML predictions, instead of using extrapolative ML models that were trained with a regular cross-validation. We consider as a case study the problem of the discovery of non-fullerene acceptors because novel classes of acceptors are naturally classified into distinct chemical families. We show that conventional ML methods are not useful in practice when attempting to predict the efficiency of a completely novel class of materials. The approach proposed in this work increases the accuracy of the predictions to enable at least the categorization of materials with a performance above and below the median value.

This paper considers the parameter estimation problem of a first-order threshold autoregressive conditional heteroscedasticity model by using the empirical likelihood method. We obtain the empirical likelihood ratio statistic based on the estimating equation of the least squares estimation and construct the confidence region for the model parameters. Simulation studies indicate that the empirical likelihood method outperforms the normal approximation-based method in terms of coverage probability.

Recent experimental work reported that visible-light photoredox catalysis coupled with primary sulfonamides and electron-deficient alkenes could efficiently construct C-C bonds at the α-position of primary amine derivatives under mild conditions. Here, a systematic study was conducted to explore the non-negligible excited-state single-electron-transfer (SET) processes and the catalytic cycle. Hydrogen atom transfer (HAT) catalysis containing different site-selective functionalization, involved as a critical process during the reaction, was computationally characterized. The superiorities of iridium-based photoredox catalysts in terms of photoabsorption properties, phosphorescence rates, and electron-transfer rates for SET processes were focused on. In addition, the function of quinuclidine in the entire photocatalytic reaction was also probed. These intrinsic properties and detailed insights into the mechanism are supposed to be helpful to the understanding of the C-C bond functionalization reaction and the future application of the iridium-based photoredox catalyst.

To gain a deep understanding on structure-property relationship of all-small-molecule nonfullerene organic solar cells, donor molecules SM1 and SM2 with and without cyano group substituents, respectively, were selected, for which SM1-based device has obvious higher power conversion efficiency (10.11%) than SM2-based device (5.32%) in the same condition. Here a systemic study combining quantum chemistry with molecular dynamics was conducted to explore what improve the performance of SM1-based organic solar cells from the theoretical perspective. The results indicate that the cyano substituents in SM1 not only could induce a redshift absorption spectrum through decreasing the energy gap but also lead to better intramolecular charge transfer, which is beneficial to charge transfer at donor/acceptor interface. More interestingly, the SM1/acceptor interface presents an additional charge transfer state (CT state) below the first optically Frenkel exciton state (FE state) except that its FE state has stronger absorption compared with SM2/acceptor interface, which plays an important role in improving its charge transfer rate (kinter-CT) through increasing electronic coupling of charge transfer (VDA-CT). Thus it is speculated that molecular design considering additional CT states below FE state is a promising strategy in organic solar cells like SM1-based device with good performance.

This paper concentrates on the parameter estimating strategy for the generalized random coefficient autoregressive (GRCA) model in the presence of the auxiliary information. We propose a weighted least squares estimate for the model parameters and empirical likelihood (EL) based weights are obtained through using these auxiliary information. The asymptotic distribution of our proposed estimator is normal distribution and the asymptotic variance is reduced compared to the least square (LS) estimator. Therefore, our method yields more efficient estimates. We also carry out some simulation experiments to assess the performance of the suggested estimator and illustrate the usefulness of this method through the analysis of a real time series data sets.

We considered a database of tens of thousands of known organic semiconductors and identified those compounds with computed electronic properties (orbital energies, excited state energies, and oscillator strengths) that would make them suitable as nonfullerene electron acceptors in organic solar cells. The range of parameters for the desirable acceptors is determined from a set of experimentally characterized high-efficiency nonfullerene acceptors. This search leads to ∼30 lead compounds never considered before for organic photovoltaic applications. We then proceed to modify these compounds to bring their computed solubility in line with that of the best small-molecule nonfullerene acceptors. A further refinement of the search can be based on additional properties like the reorganization energy for chemical reduction. This simple strategy, which relies on a few easily computable parameters and can be expanded to a larger set of molecules, enables the identification of completely new chemical families to be explored experimentally.

Recent experimental work reported that visible-light photoredox catalysis coupled with primary sulfonamides and electron-deficient alkenes could efficiently construct C-C bonds at the α-position of primary amine derivatives under mild conditions. Here, a systematic study was conducted to explore the non-negligible excited-state single-electron-transfer (SET) processes and the catalytic cycle. Hydrogen atom transfer (HAT) catalysis containing different site-selective functionalization, involved as a critical process during the reaction, was computationally characterized. The superiorities of iridium-based photoredox catalysts in terms of photoabsorption properties, phosphorescence rates, and electron-transfer rates for SET processes were focused on. In addition, the function of quinuclidine in the entire photocatalytic reaction was also probed. These intrinsic properties and detailed insights into the mechanism are supposed to be helpful to the understanding of the C-C bond functionalization reaction and the future application of the iridium-based photoredox catalyst.

In this work, we analyzed a dataset formed by 566 donor/acceptor pairs, which are part of organic solar cells recently reported. We explored the effect of different descriptors in machine learning ...

Charge dissociation in the active layer is one of the key factors for the power conversion efficiency of bulk heterojunction organic solar cells (OSCs). Numerous charge-transfer mechanisms have been proposed based on one of few microscopic models. Here, we would explore possible charge-transfer mechanisms for 155 models of donor/acceptor (D/A) interfaces, built via materials DCV-1 and C60 as the donor and acceptor, respectively. After the calculations of the key parameters related to the charge dissociation and a statistical analysis for the correlation between these parameters were carried out, we can obtain a more robust description of the charge dissociation in practical OSCs. The complicated relationship among the key parameters not only illustrates the important correlation between the D/A stacking pattern and charge-transfer mechanism but also suggests that different charge-transfer mechanisms take place more likely depending on the specific arrangements of the donor and acceptor.

In this paper, we consider the variable selection problem of the generalized random coefficient autoregressive model (GRCA). Instead of parametric likelihood, we use non-parametric empirical likelihood in the information theoretic approach. We propose an empirical likelihood-based Akaike information criterion (AIC) and a Bayesian information criterion (BIC).

For a photochromic spiro-diarylethene derivative (Spiro-DAE), its photocyclization reaction and multicolour fluorescence modulation performances were investigated based on a series of density functionals calculations in this work. Some elementary properties such as geometric and electronic structures at ground and the first excited state (S1), absorption and emission spectra in respective open-ring and closed-ring structures were compared, which indicate the cyclization reaction may occur at S1. It is further verified by the lower activation barrier (3.4 kcal/mol) of the transition state at S1 potential energy surface. The photochromic reaction rates for both cyclization and cycloreversion processes were quantitatively estimated, which suggests the much smaller radiative and internal conversion rates contribute to the higher photocyclization rate and thus efficiency. Furthermore, we suspect that the dihydroacridine fragment may make important contribution to the cyclization reaction occurring at S1 considering its contribution to electronic structure.

In order to compare the main difference of two diimide derivatives on the modulation of electronic and optical properties of P3HT-based organic solar cell, the density functional theory and molecular dynamics simulations were implemented to achieve elementary data on geometrical structure, molecular orbital, open-circuit voltage, absorption spectra, energetic driving force, and interface parameter of P3HT/D1 and P3HT/D2 systems. According to the investigation, P3HT/D1 system not only exhibits higher open circuit voltage and enough energetic driving force than P3HT/D2 system, but also possesses low-lying LUMO +1 orbital which can favor the exciton separation efficiency. Moreover, on the basis of some typical interface models choose from MD simulation, the estimation of the interface rate manifests that the P3HT/D1 interface possesses the smaller charge recombination rates and larger charge separation rates than those of the P3HT/D2 interface. It is expect that this work can provide certain guidelines for the further develop the performance of organic solar cell. We hope this work can further study on non-fullerene acceptor materials as a certain guides.

Large conductance Ca(2+)- and voltage-activated potassium (BK) channels, composed of pore-forming α subunits and auxiliary β subunits, play important roles in diverse physiological activities. The β1 is predominately expressed in smooth muscle cells, where it greatly enhances the Ca(2+) sensitivity of BK channels for proper regulation of smooth muscle tone. However, the structural basis underlying dynamic interaction between BK mSlo1 α and β1 remains elusive. Using macroscopic ionic current recordings in various Ca(2+) and Mg(2+) concentrations, we identified two binding sites on the cytosolic N terminus of β1, namely the electrostatic enhancing site (mSlo1(K392,R393)-β1(E13,T14)), increasing the calcium sensitivity of BK channels, and the hydrophobic site (mSlo1(L906,L908)-β1(L5,V6,M7)), passing the physical force from the Ca(2+) bowl onto the enhancing site and S6 C-linker. Dynamic binding of these sites affects the interaction between the cytosolic domain and voltage-sensing domain, leading to the reduction of Mg(2+) sensitivity. A comprehensive structural model of the BK(mSlo1 α-β1) complex was reconstructed based on these functional studies, which provides structural and mechanistic insights for understanding BK gating.

In this paper, we consider the application of the empirical likelihood method to the generalized random coefficient autoregressive (GRCA) model. The empirical log-likelihood ratio statistics are proposed and the nonparametric versions of the Wilk’s theorem are obtained. Furthermore, when the order of the model is 1, we also derive a test statistic to test the stationary–ergodicity based on the conditional least-squares method. Numerical results from simulation studies suggest that the empirical likelihood method is more accurate than the normal approximation-based method of Hwang and Basawa (1998) [1] . Some simulation studies are also conducted to investigate the finite sample performances of the proposed test.

In this paper, we study the problem of testing for parameter changes in generalized random coefficient autoregressive model (GRCA). The testing method is based on the monitoring scheme proposed by Na et al. (Stat. Methods Appl. 20:171-199, 2011), and the test statistic relies on the conditional least-squares estimator of an unknown parameter. Furthermore, under mild conditions, we obtain the asymptotic property of the test statistic. Some simulation studies are also conducted to investigate the finite sample performances of the proposed test.

In this paper, we extend the standard autoregressive model to the case where the explanatory and response variables are random fuzzy variables. The fuzzy least-squares estimators (FLSE) of the model parameters are derived and their asymptotic properties are established. A simulation is conducted to evaluate our method, and it is found that the proposed method provides a better performance. AMS Subject Classification: 94D05; 62F12

Phillips and Magdalinos (2007) [1] gave the asymptotic theory for autoregressive time series with a root of the form ρn=1+c/kn, where kn is a deterministic sequence. In this paper, an extension to the more general case where the coefficients of an AR(1) model is a random variable and the error sequence is a sequence of martingale differences is discussed. A conditional least squares estimator of the autoregressive coefficient is derived and shown to be asymptotically normal. This extends the result of Phillips and Magdalinos (2007) [1] for stationary and near-stationary cases.

In this paper, we employ the empirical likelihood method to estimate the unknown parameters in Poisson autoregressive model in the presence of auxiliary information. It is shown that our approach proposed, compared to the maximum likelihood estimator, the least squares estimator, and the weighted least squares estimator, yields more efficient estimators. Some simulation studies are also conducted to investigate the finite sample performance of the proposed method.

In this paper, we apply the least-squares method to estimate the unknown parameters in first-order random coefficient integer-valued autoregressive ( $\operatorname{RCINAR}(1)$ ) processes. The least-squares estimator is derived and its limiting properties are discussed. Furthermore, we also derive a statistic to test the randomness of coefficients. Numerical results from simulation studies suggest that the proposed method is good for practical use.