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Effects of geometric shape and size of embedded boron nitride (BN) nanodot on the electronic and magnetic properties of the BN-doped graphene superlattices are systematically studied by spin-polarized first-principles calculations. The band gap of graphene superlattice is found to increase with the size of the BN nanodot, regardless of the shape of BN nanodot. Midgap states are found for graphene superlattices with triangular BN nanodots, and the number of such midgap states is determined by the imbalance between the number of carbon atoms occupying A- and B-sublattices, which is closely related to the geometric shape and size of the BN nanodot. When B and N atoms in the superlattices are exchanged, the valance bands and conduction bands are inverted with respect to the Fermi level due to electron-hole symmetry. Furthermore, partial occupation of the midgap states induces spin-polarization, and results in a magnetic ground state for the BN-doped graphene superlattices with triangular BN nanodots. Through electron or hole injection, the magnetic moment of the BN-doped graphene superlattice can be tuned, which may find applications in spintronic devices.

A cascaded model is proposed to establish the quantitative structure-activity relationship (QSAR) between the overall power conversion efficiency (PCE) and quantum chemical molecular descriptors of all-organic dye sensitizers. The cascaded model is a two-level network in which the outputs of the first level (JSC, VOC, and FF) are the inputs of the second level, and the ultimate end-point is the overall PCE of dye-sensitized solar cells (DSSCs). The model combines quantum chemical methods and machine learning methods, further including quantum chemical calculations, data division, feature selection, regression, and validation steps. To improve the efficiency of the model and reduce the redundancy and noise of the molecular descriptors, six feature selection methods (multiple linear regression, genetic algorithms, mean impact value, forward selection, backward elimination, and +n-m algorithm) are used with the support vector machine. The best established cascaded model predicts the PCE values of DSSCs with a MAE of 0.57 (%), which is about 10% of the mean value PCE (5.62%). The validation parameters according to the OECD principles are R(2) (0.75), Q(2) (0.77), and Qcv2 (0.76), which demonstrate the great goodness-of-fit, predictivity, and robustness of the model. Additionally, the applicability domain of the cascaded QSAR model is defined for further application. This study demonstrates that the established cascaded model is able to effectively predict the PCE for organic dye sensitizers with very low cost and relatively high accuracy, providing a useful tool for the design of dye sensitizers with high PCE.

Background Non-covalent interactions (NCIs) play critical roles in supramolecular chemistries; however, they are difficult to measure. Currently, reliable computational methods are being pursued to meet this challenge, but the accuracy of calculations based on low levels of theory is not satisfactory and calculations based on high levels of theory are often too costly. Accordingly, to reduce the cost and increase the accuracy of low-level theoretical calculations to describe NCIs, an efficient approach is proposed to correct NCI calculations based on the benchmark databases S22, S66 and X40 (Hobza in Acc Chem Rev 45: 663–672, 2012; Řezáč et al. in J Chem Theory Comput 8:4285, 2012).ResultsA novel type of NCI correction is presented for density functional theory (DFT) methods. In this approach, the general regression neural network machine learning method is used to perform the correction for DFT methods on the basis of DFT calculations. Various DFT methods, including M06-2X, B3LYP, B3LYP-D3, PBE, PBE-D3 and ωB97XD, with two small basis sets (i.e., 6-31G* and 6-31+G*) were investigated. Moreover, the conductor-like polarizable continuum model with two types of solvents (i.e., water and pentylamine, which mimics a protein environment with ε = 4.2) were considered in the DFT calculations. With the correction, the root mean square errors of all DFT calculations were improved by at least 70 %. Relative to CCSD(T)/CBS benchmark values (used as experimental NCI values because of its high accuracy), the mean absolute error of the best result was 0.33 kcal/mol, which is comparable to high-level ab initio methods or DFT methods with fairly large basis sets. Notably, this level of accuracy is achieved within a fraction of the time required by other methods. For all of the correction models based on various DFT approaches, the validation parameters according to OECD principles (i.e., the correlation coefficient R2, the predictive squared correlation coefficient q2 and from cross-validation) were >0.92, which suggests that the correction model has good stability, robustness and predictive power.ConclusionsThe correction can be added following DFT calculations. With the obtained molecular descriptors, the NCIs produced by DFT methods can be improved to achieve high-level accuracy. Moreover, only one parameter is introduced into the correction model, which makes it easily applicable. Overall, this work demonstrates that the correction model may be an alternative to the traditional means of correcting for NCIs.Graphical abstractA machine learning correction model efficiently improved the accuracy of non-covalent interactions(NCIs) calculated by DFT methods. The application of the correction model is easy and flexible, so it may be an alternative correction means for NCIs by first-principle calculations.Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-016-0133-7) contains supplementary material, which is available to authorized users.

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Summary The progress of bio‐hydrogen technology has led to the development of new energy technologies and is significant for the sustainable use of energy. After summarizing current research results, this study discusses that the key to increasing the hydrogen production rate is to improve the activity of hydrogen producing bacteria under the conditions of anaerobic fermentation. Using waste to prepare hydrogen producing bacteria is the developmental trend. The primary factors influencing bio‐hydrogen production from plant straw fermentation are also pointed out, indicating the method to improve the hydrogen production rate from plant straw. In addition, application of artificial intelligence technology to a bio‐hydrogen production reactor is helpful to achieve automatic control of continuous bio‐hydrogen production and improve the rate of hydrogen production.

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