The physical properties of two-dimensional (2D) lead halide based hybrid perovskites are quite exciting and challenging. Further, the role of organic cations in 2D perovskites is still in a debate. We investigated layered (CH3(CH2)3NH3)2(CH3NH3)Pb2I7 2D Ruddlesden-Popper (2DRP) phase (M1) and 2D derivative of CH3NH3PbI3 (M2) using density functional theory. The spin orbit coupling mediates the significantly large Rashba splitting energy of 328.5 meV for M2, which is higher than earlier 2D hybrid perovskites. At the picosecond time scale, the dynamical Rashba effect was observed due to organic and inorganic cation dynamics. Two step absorption suggests an indirect optical gap of 2.38 and 2.15 eV for M1 and M2, respectively and solar performance depicts excellent power conversion efficiency of 14.92% and 19.75% for M1 and M2, respectively. For the first time, we explored the thermoelectric properties of 2D hybrid perovskites and perceived high power factor for p-type doping in M2. Our findings suggest that these novel 2D perovskites have the potential to be used in solar and heat energy harvesting.Exploring materials with high hydrogen evolution reaction (HER) performance is of importance for the development of clean hydrogen energy, and the defects on the surfaces of catalysts are essential. In this work, we evaluate the HER performance among group IVA monochalcogenides MXs (M = Ge/Sn, X = S/Se) with M/X point defects on the edges. Compared with basal planes and bare edges, the GeS edge with Ge vacancy (ΔGH* = 0.016 eV), GeSe edge with Se vacancy (ΔGH* = 0.073 eV), and SnSe edge with Sn vacancy (ΔGH* = -0.037 eV) hold the best HER performances, which are comparable to or even better than the value for Pt (-0.07 eV). Furthermore, the relationships between ΔGH* and p-band centers of considered models are summarized. The stability of proposed electrocatalysts are analyzed by vacancy-formation energy and strain engineering. In summary, the HER performance of MXs is greatly improved by introduction of point defects at the edges, which is promising for their use as electrocatalysts for the conversion and storage of energy in the future.We propose a numerically simple and straightforward, yet accurate and efficient neural networks-based fitting strategy to construct coupled potential energy surfaces (PESs) in a quasi-diabatic representation. The fundamental invariants are incorporated to account for the complete nuclear permutation inversion symmetry. Instead of derivative couplings or interstate couplings, a so-called modified derivative coupling term is fitted by neural networks, resulting in accurate description of near degeneracy points, such as the conical intersections. The adiabatic energies, energy gradients, and derivative couplings are well reproduced, and the vanishing of derivative couplings as well as the isotropic topography of adiabatic and diabatic energies in asymptotic regions are automatically satisfied. All of these features of the coupled global PESs are requisite for accurate dynamics simulations. Our approach is expected to be very useful in developing highly accurate coupled PESs in a quasi-diabatic representation in an efficient machine learning-based way.Together with experimental data, theoretically predicted dipole moments represent a valuable tool for different branches in the chemical and physical sciences. https://www.selleckchem.com/products/pnd-1186-vs-4718.html With the diversity of levels of theory and basis sets available, a reliable combination must be carefully chosen in order to achieve accurate predictions. In a recent publication (J. Chem. Theory Comput. 2018, 14 (4), 1969-1981), Hait and Head-Gordon took a first step in this regard by providing recommendations on the best density functionals suitable for these purposes. However, no extensive study has been performed to provide recommendations on the basis set choice. Here, we shed some light into this matter by evaluating the performance of 38 general-purpose basis sets of single- up to triple-ζ-quality, when coupled with nine different levels of theory, in the computation of dipole moments. The calculations were performed on a data set with 114 small molecules containing second- and third-row elements. We based our analysis in regularized root-mean-square errors (regularized RMSE), in which the difference between the calculated μcalc and benchmark μbmk dipole moment values is derived as (μcalc[D] - μbmk[D])/(max(μbmk[D],1[D])). This procedure ensures relative errors for ionic species and absolute errors for species with small dipole moment values. Our results indicate that the best compromise between accuracy and computational efficiency is achieved by performing the computations with an augmented double-ζ-quality basis set (i.e., aug-pc-1, aug-pcseg-1, aug-cc-pVDZ) together with a hybrid functional (e.g., ωB97X-V, SOGGA11-X). Augmented triple-ζ basis sets could enhance the accuracy of the computations, but the computational cost of introducing such a basis set is substantial compared with the small improvement provided. These findings also highlight the crucial role that augmentation of the basis set with diffuse functions on both hydrogen and non-hydrogen atoms plays in the computation of dipole moments.Cell-free protein synthesis (CFPS) platforms have undergone numerous workflow improvements to enable diverse applications in research, biomanufacturing, and education. The Escherichia coli cell extract-based platform has been broadly adopted due to its affordability and versatility. The upstream processing of cells to generate crude cell lysate remains time-intensive and technically nuanced, representing one of the largest sources of cost associated with the biotechnology. To overcome these limitations, we have improved the processes by developing a long-lasting autoinduction media formulation for CFPS that obviates human intervention between inoculation and harvest. The cell-free autoinduction (CFAI) media supports the production of robust cell extracts from high cell density cultures nearing the stationary phase of growth. As a result, the total mass of cells and the resulting extract volume obtained increases by 400% while maintaining robust reaction yields of reporter protein, sfGFP (>1 mg/mL). Notably, the CFAI workflow allows users to go from cells on a streak plate to completing CFPS reactions within 24 h.