We devised a suite of universal statistical interaction descriptors (SIDs) and trained accurate machine learning models to predict thermoelectric properties, thereby facilitating the search for materials exhibiting ultralow thermal conductivity and high power factors. The cutting-edge SID-based model demonstrated the highest accuracy in predicting lattice thermal conductivity, yielding an average absolute error of 176 W m⁻¹ K⁻¹. The highly effective models forecast that rubidium and cesium hypervalent triiodides XI3 will display very low thermal conductivity values and noteworthy power factors. Using first-principles calculations coupled with the self-consistent phonon theory and the Boltzmann transport equation, we calculated the anharmonic lattice thermal conductivities of CsI3 and RbI3 in the c-axis direction at 300 K as 0.10 W m⁻¹ K⁻¹ and 0.13 W m⁻¹ K⁻¹, respectively. Further research demonstrates that the ultralow thermal conductivity exhibited by XI3 is a consequence of the interplay between the vibrations of alkali and halogen atoms. At an optimal hole doping level at 700 Kelvin, CsI3 shows a ZT value of 410, while RbI3 exhibits a ZT value of 152. This highlights the potential of hypervalent triiodides as superior thermoelectric materials.
By employing a microwave pulse sequence, the coherent transfer of electron spin polarization to nuclei can lead to an enhancement in the sensitivity of solid-state nuclear magnetic resonance (NMR). The optimization of DNP pulse sequences for bulk nuclei remains an active area of research, just as a profound understanding of the characteristics of an effective DNP sequence remains a subject of investigation. Herein, we define a novel sequence, the Two-Pulse Phase Modulation (TPPM) DNP, for this context. Electron-proton polarization transfer, using periodic DNP pulse sequences, is theoretically described and numerically simulated, demonstrating excellent agreement. The heightened sensitivity of TPPM DNP at 12 Tesla surpassed that of XiX (X-inverse-X) and TOP (Time-Optimized Pulsed) DNP sequences, however, this improvement came at the expense of employing relatively higher nutation frequencies. In opposition to other techniques, the XiX sequence demonstrates outstanding performance at nutation frequencies of only 7 MHz. Carcinoma hepatocelular Experimental investigation, complemented by theoretical analysis, unequivocally reveals that the quick electron-proton polarization transfer, arising from a preserved dipolar coupling term in the effective Hamiltonian, is directly related to a rapid build-up time of bulk dynamic nuclear polarization. The performances of XiX and TOP DNP exhibit varying sensitivities to the concentration of the polarizing agent, as evidenced by further experimental results. The data obtained from these experiments establish vital reference points for the advancement of enhanced DNP sequences.
The public release of a massively parallel, GPU-accelerated software, the first of its kind to unify coarse-grained particle simulations with field-theoretic simulations, is announced in this paper. MATILDA.FT (Mesoscale, Accelerated, Theoretically Informed, Langevin, Dissipative particle dynamics, and Field Theory), built from the ground up with CUDA-enabled GPUs and Thrust library support, was specifically designed to take advantage of massive parallelism for efficient simulations of mesoscopic systems. Various systems, ranging from polymer solutions and nanoparticle-polymer interfaces to coarse-grained peptide models and liquid crystals, have been modeled by its use. The source code for MATILDA.FT, built with CUDA/C++ using an object-oriented method, is exceptionally clear and simple to extend. A comprehensive overview of the presently available features and the logic of parallel algorithms and approaches is given here. The theoretical groundwork and illustrative systems simulations using MATILDA.FT as the simulation engine are presented here. The GitHub repository MATILDA.FT provides access to the source code, the documentation, additional tools, and example files.
To mitigate finite-size effects stemming from snapshot-dependent electronic density response functions and related properties in LR-TDDFT simulations of disordered extended systems, averaging across various ion configuration snapshots is crucial. A uniform framework for calculating the macroscopic Kohn-Sham (KS) density response function is established, connecting the average values of charge density perturbation snapshots to the averaged variations in the KS potential. The direct perturbation method, as described in [Moldabekov et al., J. Chem.], enables the formulation of LR-TDDFT in disordered systems, specifically by employing the adiabatic (static) approximation for the exchange-correlation (XC) kernel. Exploring the abstract nature of computation, the field of computational theory excels. The sentence corresponding to [19, 1286] from 2023 demands ten distinct and different structural arrangements. By implementing the presented approach, one can determine both the macroscopic dynamic density response function and the dielectric function, given a static exchange-correlation kernel that can be generated using any accessible exchange-correlation functional. Warm dense hydrogen serves as a case study for demonstrating the developed workflow's application. Various extended disordered systems, including warm dense matter, liquid metals, and dense plasmas, are amenable to the presented approach.
Nanoporous materials, particularly those derived from 2D materials, are opening up novel possibilities for water purification and energy applications. Consequently, an examination of the molecular underpinnings of the superior performance of these systems, regarding nanofluidic and ionic transport, is warranted. We introduce a novel, unified methodology for performing Non-Equilibrium Molecular Dynamics (NEMD) simulations on nanoporous membranes, facilitating the application of pressure, chemical potential, and voltage drops, ultimately quantifying the resulting transport characteristics of confined liquids under these imposed stimuli. Employing the NEMD approach, we examine a newly developed type of synthetic Carbon NanoMembrane (CNM), exhibiting remarkable desalination capabilities with high water permeability and complete salt exclusion. CNM's high water permeance, as evidenced by empirical data, originates from substantial entrance effects, resulting from negligible frictional resistance inside the nanopore. Calculating the symmetric transport matrix is not the limit of our methodology, which further permits calculation of the complex cross-phenomena including electro-osmosis, diffusio-osmosis, and streaming currents. Our prediction involves a substantial diffusio-osmotic current traversing the CNM pore, driven by a concentration gradient, despite the non-existent surface charges. In conclusion, CNMs are exceptional candidates as alternative, scalable membranes for the purpose of osmotic energy harvesting.
We describe a machine-learning approach, both local and transferable, for predicting the real-space density response of molecules and periodic systems to homogeneous electric fields. The new method, SALTER (Symmetry-Adapted Learning of Three-dimensional Electron Responses), is an advancement of the symmetry-adapted Gaussian process regression approach, previously used for learning three-dimensional electron densities. The descriptors representing atomic environments within SALTER require only a small, but crucial, adjustment. We detail the method's performance on discrete water molecules, water in its bulk phase, and a naphthalene crystal structure. Density response predictions exhibit root mean square errors of no more than 10%, based on a training set containing just over a hundred structures. The derived polarizability tensors, and the subsequent Raman spectra generated from them, exhibit satisfactory agreement with quantum mechanical calculations. As a result, SALTER demonstrates impressive accuracy in predicting derived quantities, maintaining the entirety of the data from the complete electronic response. Therefore, this method is able to anticipate vector fields in a chemical environment, and acts as a pivotal indication for forthcoming enhancements.
The chirality-induced spin selectivity (CISS) effect's sensitivity to temperature enables the differentiation of various theoretical proposals regarding its mechanism. We provide a brief summary of crucial experimental results, followed by an examination of temperature's impact on various CISS models. We then delve into the recently suggested spinterface mechanism, examining the multifaceted effects of temperature variations within its parameters. We meticulously analyze the experimental results presented by Qian et al. in Nature 606, 902-908 (2022), demonstrating, in contrast to the authors' proposed interpretation, that the CISS effect exhibits a strong correlation with lower temperatures. To conclude, the spinterface model's aptitude for accurately reproducing these experimental observations is exhibited.
A variety of spectroscopic observable expressions and quantum transition rates are predicated upon the underlying principle of Fermi's golden rule. selected prebiotic library FGR's utility has been repeatedly confirmed through decades of experimentation. Despite this, important cases still exist where the calculation of a FGR rate is ambiguous or ill-defined. Instances of divergent rate terms arise from the sparse distribution of final states or fluctuating system Hamiltonians over time. From a rigorous perspective, the tenets of FGR are no longer sound in such instances. Although that is the case, it is possible to craft modified forms of FGR rate expressions that are usefully effective. The adjusted FGR rate expressions provide solutions to a frequent ambiguity encountered in FGR usage, offering more dependable methods for general rate modeling. Model calculations, simple in nature, illustrate the value and implications inherent in the new rate expressions.
The World Health Organization stresses a strategic and intersectoral approach for mental health services, acknowledging the positive impact of the arts and the value of cultural factors on the mental health recovery process. check details The research objective of this study encompassed evaluating the role of participatory arts experiences in museums for supporting mental health recovery.