Journal of Chemical Physics最新文献

Despite the capability of bis-(thio)carbohydrazones to coordinate metals and the remarkable biological properties of the resulting complexes, no general information is known about their individual behavior in solution. This study is focused on two recently synthesized compounds, a bis-thiocarbohydrazone (bis-TCH) and a bis-carbohydrazone (bis-CH) isolated as sodium salts, that have shown chelating properties toward copper(II) and zinc(II) metal ions along with promising cytotoxic activity. In this work, an integrated theoretical-computational, nuclear magnetic resonance (NMR), and vibrational characterization of both bis-TCH and bis-CH anions in a non-protic solvent (dimethylsulfoxide) is presented to better elucidate their properties. Their protonic NMR spectra underline the presence of cis-trans, EE isomers, characterized by a significant conformational freedom at room temperature. The presence of oxygen or sulfur heteroatoms can tune the molecular conformational dynamics driving a different interaction with the solvent, as highlighted by density functional theory calculations and atomistic molecular dynamics simulations. Our results demonstrate that a quantitative agreement with the NMR and Raman signals is achieved only when an explicit solvent description is included. The insights achieved by this study can contribute to a better understanding of the behavior of bis-carbohydrazones and bis-thiocarbohydrazones in solution, a crucial and mandatory step to improve the design of novel, more potent analogs.

The integration of scanning tunneling microscopy (STM) and electron spin resonance spectroscopy with voltage pulses is an emerging technique to probe the local spin dynamics of surface-adsorbed molecules. However, in experiments, the detection of real-time spin dynamics is severely hampered by the limited temporal resolution of STM electronics, and the associated theoretical investigations are still in their early stages due to various challenges in numerical simulations. In this work, we employ the highly accurate hierarchical equations of motion method to characterize the spin states and track the real-time coherent flip-flop spin dynamics in a surface-adsorbed hydrogenated Ti dimer. Our simulations accurately reproduce the experimental observations and reveal the influences of substrate and pulse duration on the spin decoherence process of the dimer. These achievements provide valuable insights into the coherent spin dynamics of surface-adsorbed molecules and set the stage for the application of surface-adsorbed molecular spins to quantum sensing, quantum information processing, and quantum computing.

A detailed analysis of the N(1s) and C(1s) X-Ray Photoelectron Spectroscopy (XPS) is made, where the measured XPS is compared with theoretical Sudden Approximation (SA) intensities and theoretical XPS Binding Energies (BEs). There is remarkably good agreement between the theoretical predictions and the measured XPS; in particular, the different full width at half maximum values for the C(1s) and N(1s) BEs are explained in terms of unresolved C(1s) BEs for the different C atoms in pyridine. This work demonstrates that the combination of theory and XPS measurements can extract analysis of the XPS relevant to the molecular electronic structure. The theory used is based on fully relativistic self-consistent field solutions of the Dirac-Coulomb Hamiltonian, and the SA is used to determine relative XPS intensities.

Along with the surge in interest in quantum computing, interest in the unitary coupled cluster (UCC) Ansatz has reemerged. Although extensively studied within electronic structure theory, the UCC Ansatz remains relatively unexplored for the problem of molecular vibrations. In this contribution, working equations for the unitary vibrational coupled cluster (UVCC) Ansatz are derived, implemented, and benchmarked. Accuracy and convergence of state-specific excitation energies toward the full vibrational configuration interaction (FVCI) limit are observed to be comparable to vibrational coupled cluster theory. In addition, the overlap of a truncated UVCC state with the FVCI state is shown to exhibit some interesting properties from the perspective of fault-tolerant quantum computing.

In this work, we present MOLPIPx, a versatile library designed to seamlessly integrate permutationally invariant polynomials with modern machine learning frameworks, enabling the efficient development of linear models, neural networks, and Gaussian process models. These methodologies are widely employed for parameterizing potential energy surfaces across diverse molecular systems. MOLPIPx leverages two powerful automatic differentiation engines-JAX and EnzymeAD-Rust-to facilitate the efficient computation of energy gradients and higher-order derivatives, which are essential for tasks such as force field development and dynamic simulations. MOLPIPx is available at https://github.com/ChemAI-Lab/molpipx.

Molecular simulations of biological and physical phenomena generally involve sampling complicated, rough energy landscapes characterized by multiple local minima. In this work, we introduce a new family of methods for advanced sampling that draw inspiration from functional representations used in machine learning and approximation theory. As shown here, such representations are particularly well suited for learning free energies using artificial neural networks. As a system evolves through phase space, the proposed methods gradually build a model for the free energy as a function of one or more collective variables, from both the frequency of visits to distinct states and generalized force estimates corresponding to such states. Implementation of the methods is relatively simple and, more importantly, for the representative examples considered in this work, they provide computational efficiency gains of up to several orders of magnitude over other widely used simulation techniques.

Delta-T shot noise is activated in temperature-biased electronic junctions, down to the atomic scale. It is characterized by a quadratic dependence on the temperature difference and a nonlinear relationship with the transmission coefficients of partially opened conduction channels. In this work, we demonstrate that delta-T noise, measured across an ensemble of atomic-scale junctions, can be utilized to estimate the temperature bias in these systems. Our approach employs a supervised machine learning algorithm to train a neural network, with input features being the scaled electrical conductance, the delta-T noise, and the mean temperature. Due to limited experimental data, we generate synthetic datasets, designed to mimic experiments. The neural network, trained on these synthetic data, was subsequently applied to predict temperature biases from experimental datasets. Using performance metrics, we demonstrate that the mean bias-the deviation of predicted temperature differences from their true value-is less than 1 K for junctions with conductance up to 4G0. Our study highlights that, while a single delta-T noise measurement is insufficient for accurately estimating the applied temperature bias due to noise contributions from other sources, averaging over an ensemble of junctions enables predictions within experimental uncertainties. This suggests that machine learning approaches can be utilized for estimation of temperature biases and similarly other stimuli in electronic junctions.

We report the detection of atomic oxygen and quantitative measurements of its electronic Raman coherence decays in flames and low-temperature plasmas using time-resolved hybrid femtosecond/picosecond coherent anti-Stokes Raman scattering (CARS). Atomic oxygen was detected using the Raman transitions between the spin-orbit coupled triplet ground states. Atomic oxygen was generated in an H2/O2/Ar diffusion flame and an O2/Ar pulsed plasma discharge. Single exponential decays were observed for the O(3P2)-O(3P1) Raman transition at 158.3 cm-1 and the O(3P2)-O(3P0) Raman transition at 227 cm-1. From the decay measurements, the atomic O Raman linewidths were obtained from 25 to 150 Torr in non-equilibrium plasma and at 760 Torr in a flame. Enhanced signal-to-noise ratios (SNRs) of atomic oxygen and atomic to molecular oxygen signal contrasts were obtained by taking advantage of electronic triplet coherence beating. Enhancement of up to seven times in the atomic O SNR was observed. We also found that the dephasing rates of O2(v = 0-3, N = 37) were similar, which provides evidence for the assumption that vibrational excitation does not influence the dephasing of diatomic molecular rotational CARS transitions.

We present an implementation of the relativistic ionization-potential (IP) equation-of-motion coupled-cluster (EOMCC) with up to 3-hole-2-particle (3h2p) excitations that makes use of the molecular mean-field exact two-component framework and the full Dirac-Coulomb-Breit Hamiltonian. The closed-shell nature of the reference state in an X2C-IP-EOMCC calculation allows for accurate predictions of spin-orbit splittings in open-shell molecules without breaking degeneracies, as would occur in an excitation-energy EOMCC calculation carried out directly on an unrestricted open-shell reference. We apply X2C-IP-EOMCC to the ground and first excited states of the HCCX+ (X = Cl, Br, I) cations, where it is demonstrated that a large basis set (i.e., quadruple-zeta quality) and 3h2p correlation effects are necessary for accurate absolute energetics. The maximum error in calculated adiabatic IPs is on the order of 0.1 eV, whereas spin-orbit splittings themselves are accurate to ≈0.01 eV, as compared to experimentally obtained values.

We introduce a technique for extracting microstructural geometry from NMR line shape analysis in porous materials at angstrom-scale resolution with the use of weak magnetic field gradients. Diverging from the generally held view of FID signals undergoing simple exponential decay, we show that a detailed analysis of the line shape can unravel structural geometry on much smaller scales than previously thought. While the original q-space PFG NMR relies on strong magnetic field gradients in order to achieve high spatial resolution, our current approach reaches comparable or higher resolution using much weaker gradients. As a model system, we simulated gas diffusion for xenon confined within carbon nanotubes over a range of temperatures and nanotube diameters in order to unveil manifestations of confinement in the diffusion behavior. We report a multiscale scheme that couples the above-mentioned MD simulations with the generalized Langevin equation to estimate the transport properties of interest for this problem, such as diffusivity coefficients and NMR line shapes, using the Green-Kubo correlation function to correctly evaluate time-dependent diffusion. Our results highlight how NMR methodologies can be adapted as effective means toward structural investigation at very small scales when dealing with complicated geometries. This method is expected to find applications in materials science, catalysis, biomedicine, and other areas.