Within density functional theory (DFT), adding a Hubbard U correction can mitigate some of the deficiencies of local and semi-local exchange-correlation functionals, while maintaining computational efficiency. However, the accuracy of DFT+U largely depends on the chosen Hubbard U values. We propose an approach to determining the optimal U parameters for a given material by machine learning. The Bayesian optimization (BO) algorithm is used with an objective function formulated to reproduce the band structures produced by more accurate hybrid functionals. This approach is demonstrated for transition metal oxides, europium chalcogenides, and narrow-gap semiconductors. The band structures obtained using the BO U values are in agreement with hybrid functional results. Additionally, comparison to the linear response (LR) approach to determining U demonstrates that the BO method is superior.

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Singlet fission (SF), the conversion of one singlet exciton into two triplet excitons, could significantly enhance solar cell efficiency. Molecular crystals that undergo SF are scarce. Computational exploration may accelerate the discovery of SF materials. However, many-body perturbation theory (MBPT) calculations of the excitonic properties of molecular crystals are impractical for large-scale materials screening. We use the sure-independence-screening-and-sparsifying-operator (SISSO) machine-learning algorithm to generate computationally efficient models that can predict the MBPT thermodynamic driving force for SF for a dataset of 101 polycyclic aromatic hydrocarbons (PAH101). SISSO generates models by iteratively combining physical primary features. The best models are selected by linear regression with cross-validation. The SISSO models successfully predict the SF driving force with errors below 0.2 eV. Based on the cost, accuracy, and classification performance of SISSO models, we propose a hierarchical materials screening workflow. Three potential SF candidates are found in the PAH101 set.

At an interface between two materials physical properties and functionalities may be achieved, which would not exist in either material alone. Epitaxial inorganic interfaces are at the heart of semiconductor, spintronic, and quantum devices. First principles simulations based on density functional theory (DFT) can help elucidate the electronic and magnetic properties of interfaces and relate them to the structure and composition at the atomistic scale. Furthermore, DFT simulations can predict the structure and properties of candidate interfaces and guide experimental efforts in promising directions. However, DFT simulations of interfaces can be technically elaborate and computationally expensive. To help researchers embarking on such simulations, this review covers best practices for first principles simulations of epitaxial inorganic interfaces, including DFT methods, interface model construction, interface structure prediction, and analysis and visualization tools.

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Zn2SnO4-reduced graphene oxide photocatalysts were synthesized by using SnCl4 5H2O, Zn(NO3)2 · 6H2O and graphene oxide via hydrothermal process. The structure, morphology, specific surface area and photo response of the as-prepared nanocomposites were characterized by X-ray diffraction, Transmission electron microscopy, UV-vis diffuse reflectance spectra, Brunauer-emmett-teller surface area measurement and Photoluminescence emission spectra. Experimental results showed that the Zn2SnO4 nanoparticles, with 20-30 nm a size range, were uniformly dispersed on the surfaces of reduced graphene oxide. Moreover, the as-prepared Zn2SnO4-reduced graphene oxide photocatalysts exhibited enhanced photocatalytic activities for degradation of Rhodamine B compared to those of pure Zn2SnO4. When the amount of reduced graphene oxide was 4 wt%, it showed the highest photocatalytic efficiency of 99.7% for 240 min, and the photocatalytic efficiency was still 98.5% after it was recycled 4 times. It also possessed the band gap of 2.48 eV and specific surface area of 58.1 m2 g-1.

Zinc ferrite-reduced graphene oxide composites, which could effectively remove the methylene blue from aqueous solution, were prepared via a facile solvothermal process. These as-prepared samples were characterized by X-ray diffraction, transmission electron microscopy, Fourier transform infrared spectroscopy, vibration sample magnetometer and UV-vis diffuse reflectance spectroscopy. Experimental results showed that solvents played an important role in the electron structure of the final samples. Moreover, they influenced the photocatalytic performance as well. Among all the samples prepared in different solvents, those composites prepared in N-N-dimethylformamide showed the greatest performance. They could effectively remove more than 90% of the methylene blue from the solution in about 180 min. The efficient removal of target dye turned out to be the result of the combination of physical adsorption and photocatalytic degradation under visible light irritation. These catalysts showed remarkable stability, which could be effectively reused for three times. In addition, all these samples showed a certain magnetic response, which was beneficial to recycle.

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