Duggancorneliussen0955

Subsequently, such surface matrices are used to perform multi-state multi-mode nuclear dynamics for simulating PE spectra of benzene. Our theoretical findings clearly depict that the spectra for X̃2E1g and B̃2E2g-C̃2A2u states obtained from BBO treatment and TDDVR dynamics exhibit reasonably good agreement with the experimental results as well as with the findings of other theoretical approaches.Solid-state electrolyte materials with superior lithium ionic conductivities are vital to the next-generation Li-ion batteries. Molecular dynamics could provide atomic scale information to understand the diffusion process of Li-ion in these superionic conductor materials. Here, we implement the deep potential generator to set up an efficient protocol to automatically generate interatomic potentials for Li10GeP2S12-type solid-state electrolyte materials (Li10GeP2S12, Li10SiP2S12, and Li10SnP2S12). XMU-MP-1 concentration The reliability and accuracy of the fast interatomic potentials are validated. With the potentials, we extend the simulation of the diffusion process to a wide temperature range (300 K-1000 K) and systems with large size (∼1000 atoms). Important technical aspects such as the statistical error and size effect are carefully investigated, and benchmark tests including the effect of density functional, thermal expansion, and configurational disorder are performed. The computed data that consider these factors agree well with the experimental results, and we find that the three structures show different behaviors with respect to configurational disorder. Our work paves the way for further research on computation screening of solid-state electrolyte materials.Global coupled three-state two-channel potential energy and property/interaction (dipole and spin-orbit coupling) surfaces for the dissociation of NH3(Ã) into NH + H2 and NH2 + H are reported. The permutational invariant polynomial-neural network approach is used to simultaneously fit and diabatize the electronic Hamiltonian by fitting the energies, energy gradients, and derivative couplings of the two coupled lowest-lying singlet states as well as fitting the energy and energy gradients of the lowest-lying triplet state. The key issue in fitting property matrix elements in the diabatic basis is that the diabatic surfaces must be smooth, that is, the diabatization must remove spikes in the original adiabatic property surfaces attributable to the switch of electronic wavefunctions at the conical intersection seam. Here, we employ the fit potential energy matrix to transform properties in the adiabatic representation to a quasi-diabatic representation and remove the discontinuity near the conical intersection seam. The property matrix elements can then be fit with smooth neural network functions. The coupled potential energy surfaces along with the dipole and spin-orbit coupling surfaces will enable more accurate and complete treatment of optical transitions, as well as nonadiabatic internal conversion and intersystem crossing.We study the importance of self-interaction errors in density functional approximations for various water-ion clusters. We have employed the Fermi-Löwdin orbital self-interaction correction (FLOSIC) method in conjunction with the local spin-density approximation, Perdew-Burke-Ernzerhof (PBE) generalized gradient approximation (GGA), and strongly constrained and appropriately normed (SCAN) meta-GGA to describe binding energies of hydrogen-bonded water-ion clusters, i.e., water-hydronium, water-hydroxide, water-halide, and non-hydrogen-bonded water-alkali clusters. In the hydrogen-bonded water-ion clusters, the building blocks are linked by hydrogen atoms, although the links are much stronger and longer-ranged than the normal hydrogen bonds between water molecules because the monopole on the ion interacts with both permanent and induced dipoles on the water molecules. We find that self-interaction errors overbind the hydrogen-bonded water-ion clusters and that FLOSIC reduces the error and brings the binding energies into closer agreement with higher-level calculations. The non-hydrogen-bonded water-alkali clusters are not significantly affected by self-interaction errors. Self-interaction corrected PBE predicts the lowest mean unsigned error in binding energies (≤50 meV/H2O) for hydrogen-bonded water-ion clusters. Self-interaction errors are also largely dependent on the cluster size, and FLOSIC does not accurately capture the subtle variation in all clusters, indicating the need for further refinement.Dynamics of flexible molecules are often determined by an interplay between local chemical bond fluctuations and conformational changes driven by long-range electrostatics and van der Waals interactions. This interplay between interactions yields complex potential-energy surfaces (PESs) with multiple minima and transition paths between them. In this work, we assess the performance of the state-of-the-art Machine Learning (ML) models, namely, sGDML, SchNet, Gaussian Approximation Potentials/Smooth Overlap of Atomic Positions (GAPs/SOAPs), and Behler-Parrinello neural networks, for reproducing such PESs, while using limited amounts of reference data. As a benchmark, we use the cis to trans thermal relaxation in an azobenzene molecule, where at least three different transition mechanisms should be considered. Although GAP/SOAP, SchNet, and sGDML models can globally achieve a chemical accuracy of 1 kcal mol-1 with fewer than 1000 training points, predictions greatly depend on the ML method used and on the local region of the PES being sampled. Within a given ML method, large differences can be found between predictions of close-to-equilibrium and transition regions, as well as for different transition mechanisms. We identify key challenges that the ML models face mainly due to the intrinsic limitations of commonly used atom-based descriptors. All in all, our results suggest switching from learning the entire PES within a single model to using multiple local models with optimized descriptors, training sets, and architectures for different parts of the complex PES.The ultrafast optical Kerr effect (OKE) is widely used to investigate the structural dynamics and interactions of liquids, solutions, and solids by observing their intrinsic nonlinear temporal responses through nearly collinear four-wave mixing. Non-degenerate mixing schemes allow for background free detection and can provide information on the interplay between a material's internal degrees of freedom. Here, we show a source of temporal dynamics in the OKE signal that is not reflective of the internal degrees of freedom but arises from a group index and momentum mismatch. It is observed in two-color experiments on condensed media with sizable spectral dispersion, a common property near an optical resonance. In particular, birefringence in crystalline solids is able to entirely change the character of the OKE signal via the off-diagonal tensor elements of the nonlinear susceptibility. We develop a detailed description of the phase-mismatched ultrafast OKE and show how to extract quantitative information on the spectrally resolved birefringence and group index from time-resolved experiments in one and two dimensions.Nanoscale mapping of the distinct electronic phases characterizing the metal-insulator transition displayed by most of the rare-earth nickelate compounds is fundamental for discovering the true nature of this transition and the possible couplings that are established at the interfaces of nickelate-based heterostructures. Here, we demonstrate that this can be accomplished by using scanning transmission electron microscopy in combination with electron energy-loss spectroscopy. By tracking how the O K and Ni L edge fine structures evolve across two different NdNiO3/SmNiO3 superlattices, displaying either one or two metal-insulator transitions depending on the individual layer thickness, we are able to determine the electronic state of each of the individual constituent materials. We further map the spatial configuration associated with their metallic/insulating regions, reaching unit cell spatial resolution. With this, we estimate the width of the metallic/insulating boundaries at the NdNiO3/SmNiO3 interfaces, which is measured to be on the order of four unit cells.The amount of quantum chemistry (QC) data is increasing year by year due to the continuous increase of computational power and development of new algorithms. However, in most cases, our atom-level knowledge of molecular systems has been obtained by manual data analyses based on selected descriptors. In this work, we introduce a data mining framework to accelerate the extraction of insights from QC datasets, which starts with a featurization process that converts atomic features into molecular properties (AtoMF). Then, it employs correlation coefficients (Pearson, Spearman, and Kendall) to investigate the AtoMF features relationship with a target property. We applied our framework to investigate three nanocluster systems, namely, Pt n TM55-n, Ce n Zr15-nO30, and (CH n + mH)/TM13. We found several interesting and consistent insights using Spearman and Kendall correlation coefficients, indicating that they are suitable for our approach; however, our results indicate that the Pearson coefficient is very sensitive to outliers and should not be used. Moreover, we highlight problems that can occur during this analysis and discuss how to handle them. Finally, we make available a new Python package that implements the proposed QC data mining framework, which can be used as is or modified to include new features.The bacterial mechanosensitive channel of large conductance (MscL) functions as a pressure-relief safety valve to prevent cells from lysing during sudden hypo-osmotic shock. The hydrophobic gate of MscL in the closed state forms a barrier to the permeation of ions and water molecules and can be switched to the open state for releasing solutions and ions. Currently, the gate-constituting residues and the functional role of these residues in the hydrophobic gate of MscL remain elusive and controversial. Here, we employ magic angle spinning solid-state nuclear magnetic resonance (ssNMR) techniques and functional assays to investigate the hydrophobic gate of MscL from Methanosarcina acetivorans (Ma-MscL) in lipid bilayers. We obtain chemical shift assignments of ∼70% residues of Ma-MscL and predict its 3D structure. Based on the structural characterization, we identify that the residues I21-T30 in the transmembrane helix 1 constitute the hydrophobic gate by detecting water distributions in the transmembrane pore using ssNMR H/D exchange and water-edited experiments. By using ssNMR structural characterization and functional assays, we reveal that the packing of aromatic rings of F23 in each subunit of Ma-MscL is critical to the hydrophobic gate, and hydrophilic substitutions of the other functionally important residues A22 and G26 modulate channel gating by attenuating hydrophobicity of constriction of F23.Pyrrolizidine alkaloids (PAs) are a type of natural phytotoxin that contaminate food and feed and become an environmental health risk to humans and livestock. PAs exert toxicity that requires metabolic activation by cytochrome P450 (CYP) 3A, and case reports showed that fetuses are quite susceptible to PAs toxicity. The aim of this study was to explore the characteristics of developmental toxicity and fetal hepatotoxicity induced by retrorsine (RTS, a typcial toxic PA) and the underlying mechanism. Pregnant Wistar rats were intragastrically administered with 20 mg/(kg·day) RTS from gestation day (GD) 9 to 20. Results showed that prenatal RTS exposure lowered fetal bodyweights, reduced hepatocyte numbers, and potentiated hepatic apoptosis in fetuses, particularly females. Simutaneously, RTS increased CYP3A expression and pregnane X receptor (PXR) activation in female fetal liver. We further confirmed that RTS was a PXR agonist in LO2 and HepG2 cell lines. Furthermore, agonism or antagonism of androgen receptor (AR) either induced or blocked RTS-mediated PXR activation, respectively.