Predicting superconducting transition temperature through advanced machine learning and innovative feature engineering.

Superconductivity is a remarkable phenomenon in condensed matter physics, which comprises a fascinating array of properties expected to revolutionize energy-related technologies and pertinent fundamental research. However, the field faces the challenge of achieving superconductivity at room temperature. In recent years, Artificial Intelligence (AI) approaches have emerged as a promising tool for predicting such properties as transition temperature (Tc) to enable the rapid screening of large databases to discover new superconducting materials. This study employs the SuperCon dataset as the largest superconducting materials dataset. Then, we perform various data pre-processing steps to derive the clean DataG dataset, containing 13,022 compounds. In another stage of the study, we apply the novel CatBoost algorithm to predict the transition temperatures of novel superconducting materials. In addition, we developed a package called Jabir, which generates 322 atomic descriptors. We also designed an innovative hybrid method called the Soraya package to select the most critical features from the feature space. These yield R2 and RMSE values (0.952 and 6.45 K, respectively) superior to those previously reported in the literature. Finally, as a novel contribution to the field, a web application was designed for predicting and determining the Tc values of superconducting materials.

Novel first-principles insights into graphene fluorination.

Fluorination of graphene sheets with xenon difluoride leads to the formation of the widest bandgap Gr derivative, namely, fluorographene. Accurate experimental observations distinguish two stages of mechanism in the fluorination procedure: the half-fluorination stage, wherein one side of the Gr sheet is rapidly fluorinated, and the full-fluorination stage, involving much slower fluorination of the opposite side of the sheet [R. J. Kashtiban et al., Nat. Commun. 5, 5902 (2014)]. Here, we perform comprehensive density functional calculations to illustrate accurate microscopic insights into the much slower rate of the full-fluorination stage compared with the half-fluorination one. The calculated minimum energy paths for the half- and full-fluorination processes demonstrate much enhanced fluorine adsorption after the half-fluorination stage, which sounds inconsistent with the experimental picture. This ambiguity is explained in terms of significant chemical activation of the graphene sheet after half-fluorination, which remarkably facilitates the formation of chemical contaminants in the system and, thus, substantially slows down the full-fluorination procedure. After considering the binding energy and durability of the relevant chemical species, including hydrogen, oxygen, and nitrogen molecules and xenon atom, it is argued that oxygen-fluorine ligands are the most likely chemical contaminants opposing the complete fluorination of a graphene sheet. Then, we propose an oxygen desorption mechanism to carefully explain the much enhanced rate of the full-fluorination procedure at elevated temperatures. The potential photocatalytic application of the pristine and defected samples in water splitting and carbon dioxide reduction reactions is also discussed.

Ab-initio search for efficient red thermally activated delayed fluorescence molecules for organic light emitting diodes.

In this work, we present a computational study on 105 selected organic molecules in order to find suitable candidates for using as thermally activated delayed fluorescent (TADF) emitters in organic light emitting diodes (OLEDs), in the emission range of red light. Based on time-dependent density functional theory (TD-DFT) computations, three promising candidates were found, predicted to have low singlet-triplet splittings, lower than 0.06 eV, and TADF rates of 0.124, 0.154 and 0.231 1/µs. Then, using an experimental-theory calibration approach, the emission wavelength of the molecules were estimated to be 570, 476, and 623 nm, respectively. For the molecule whose emission wavelength (623 nm) is predicted to be in our desired range, we measured the photoluminescence (PL) spectrum and find out that its emission peak is within the predicted accuracy of the employed method. Moreover, we benchmarked the performance of density functional based tight-binding (DFTB) method for future screening works and find out that, this method is an efficient pre-screening tool, useful in searching for molecules with desired emission wavelengths.

New candidates for the global minimum of medium-sized silicon clusters: A hybrid DFTB/DFT genetic algorithm applied to Si n , n = 8-80.

In this study, we perform a systematic search to find the possible lowest energy structure of silicon nanoclusters Sin (n = 8-80) by means of an evolutionary algorithm. The fitness function for this search is the total energy of density functional tight binding (DFTB). To be on firm ground, we take several low energy structures of DFTB and perform further geometrical optimization by density functional theory (DFT). Then we choose structures with the lowest DFT total energy and compare them with the reported lowest energy structures in the literature. In our search, we found several lowest energy structures that were previously unreported. We further observe a geometrical transition at n = 27 from elongated to globular structures. In addition, the optical gap of the lowest energy structures is investigated by time-dependent DFTB (TD-DFTB) and time-dependent DFT (TD-DFT). The results show the same trend in TD-DFTB and TD-DFT for the optical gap. We also find a sudden drop in the optical gap at n = 27, precisely where the geometrical transition occurs.

Stable isomers and electronic, vibrational, and optical properties of WS2 nano-clusters: A first-principles study.

In this paper, we employ an evolutionary algorithm along with the full-potential density functional theory (DFT) computations to perform a comprehensive search for the stable structures of stoichiometric (WS2)n nano-clusters (n = 1 - 9), within three different exchange-correlation functionals. Our results suggest that n = 5 and 8 are possible candidates for the low temperature magic sizes of WS2 nano-clusters while at temperatures above 500 Kelvin, n = 7 exhibits a comparable relative stability with n = 8. The electronic properties and energy gap of the lowest energy isomers were computed within several schemes, including semilocal Perdew-Burke-Ernzerhof and Becke-Lee-Yang-Parr functionals, hybrid B3LYP functional, many body based DFT+GW approach, ΔSCF method, and time dependent DFT calculations. Vibrational spectra of the lowest lying isomers, computed by the force constant method, are used to address IR spectra and thermal free energy of the clusters. Time dependent density functional calculation in a real time domain is applied to determine the full absorption spectra and optical gap of the lowest energy isomers of the WS2 nano-clusters.