Search

Two-dimensional (2D) materials provide an ideal platform to explore novel superconducting behavior including Ising superconductivity, topological superconductivity and Majorana bound states in different 2D stoichiometric Ta-, Nb-, and Fe-based crystals. However, tuning the element content in 2D compounds for regulating their superconductivity has not been realized. In this work, we report the synthesis of high quality Fe1+yTe with tunable Fe content by chemical vapor deposition (CVD). The quality and composition of Fe1+yTe are characterized by Raman spectroscopy, X-ray photoelectron spectroscopy (XPS) and scanning transmission electron microscopy (STEM). The superconducting behavior of Fe1+yTe crystals with varying Fe contents is observed. The superconducting transition of selected Fe1.13Te sample is sharp ({\Delta}Tc = 1 K), while Fe1.43Te with a high-Fe content shows a relative broad superconducting transition ({\Delta}Tc = 2.6 K) at zero magnetic field. Significantly, the conspicuous vortex flow and a transition from a 3D vortex liquid state to a 2D vortex liquid state is observed in Fe1.43Te sample. Our work highlights the tunability of the superconducting properties of Fe1+yTe and sheds light on the vortex dynamics in Fe-based superconductors, which facilitates us to understand the intrinsic mechanisms of high-temperature superconductivity., Comment: 30 pages, 15 figures, accepted by Advanced Functional Materials

Phosphorescent metal complexes have been under intense investigations as emissive dopants for energy efficient organic light emitting diodes (OLEDs). Among them, cyclometalated Pt(II) complexes are widespread triplet emitters with color-tunable emissions. To render their practical applications as OLED emitters, it is in great need to develop Pt(II) complexes with high radiative decay rate constant ($k_r$) and photoluminescence (PL) quantum yield. Thus, an efficient and accurate prediction tool is highly desirable. Here, we develop a general protocol for accurate predictions of emission wavelength, radiative decay rate constant, and PL quantum yield for phosphorescent Pt(II) emitters based on the combination of first-principles quantum mechanical method, machine learning (ML) and experimental calibration. A new dataset concerning phosphorescent Pt(II) emitters is constructed, with more than two hundred samples collected from the literature. Features containing pertinent electronic properties of the complexes are chosen. Our results demonstrate that ensemble learning models combined with stacking-based approaches exhibit the best performance, where the values of squared correlation coefficients ($R^2$), mean absolute error (MAE), and root mean square error (RMSE) are 0.96, 7.21 nm and 13.00 nm for emission wavelength prediction, and 0.81, 0.11 and 0.15 for PL quantum yield prediction. For radiative decay rate constant ($k_r$), the obtained value of $R^2$ is 0.67 while MAE and RMSE are 0.21 and 0.25 (both in log scale), respectively. The accuracy of the protocol is further confirmed using 24 recently reported Pt(II) complexes, which demonstrates its reliability for a broad palette of Pt(II) emitters.We expect this protocol will become a valuable tool, accelerating the rational design of novel OLED materials with desired properties.

This paper has been withdrawn by the authors due to request from a JPCB editor., Comment: 4 pages, 3 figures

A Neural-Networks-based approach is proposed to construct a new type of exchange-correlation functional for density functional theory. It is applied to improve B3LYP functional by taking into account of high-order contributions to the exchange-correlation functional. The improved B3LYP functional is based on a neural network whose structure and synaptic weights are determined from 116 known experimental atomization energies, ionization potentials, proton affinities or total atomic energies which were used by Becke in his pioneer work on the hybrid functionals [J. Chem. Phys. ${\bf 98}$, 5648 (1993)]. It leads to better agreement between the first-principles calculation results and these 116 experimental data. The new B3LYP functional is further tested by applying it to calculate the ionization potentials of 24 molecules of the G2 test set. The 6-311+G(3{\it df},2{\it p}) basis set is employed in the calculation, and the resulting root-mean-square error is reduced to 2.2 kcal$\cdot$mol$^{-1}$ in comparison to 3.6 kcal$\cdot$mol$^{-1}$ of conventional B3LYP/6-311+G(3{\it df},2{\it p}) calculation., Comment: 10 pages, 1figure

Despite of their success, the results of first-principles quantum mechanical calculations contain inherent numerical errors caused by various approximations. We propose here a neural-network algorithm to greatly reduce these inherent errors. As a demonstration, this combined quantum mechanical calculation and neural-network correction approach is applied to the evaluation of standard heat of formation $\DelH$ and standard Gibbs energy of formation $\DelG$ for 180 organic molecules at 298 K. A dramatic reduction of numerical errors is clearly shown with systematic deviations being eliminated. For examples, the root--mean--square deviation of the calculated $\DelH$ ($\DelG$) for the 180 molecules is reduced from 21.4 (22.3) kcal$\cdotp$mol$^{-1}$ to 3.1 (3.3) kcal$\cdotp$mol$^{-1}$ for B3LYP/6-311+G({\it d,p}) and from 12.0 (12.9) kcal$\cdotp$mol$^{-1}$ to 3.3 (3.4) kcal$\cdotp$mol$^{-1}$ for B3LYP/6-311+G(3{\it df},2{\it p}) before and after the neural-network correction., Comment: 5 pages, 3 figures, REVTeX4 style, submitted to Phys.Rev.Letters