Fundamental Invariant Neural Network (FI-NN) Potential Energy Surface for the OH + CH3OH Reaction with Analytical Forces.

The hydrogen abstraction reaction of OH + CH3OH plays a great role in combustion and atmospheric and interstellar chemistry and has been extensively studied theoretically and experimentally. Theoretically, the numerical gradients with respect to the Cartesian coordinates of atoms in molecular simulations on our recent potential energy surface (PES) for the title reaction trained using the permutationally invariant polynomial neural network (PIP-NN) approach hinder the extensive calculation because of the unaffordable computation cost. To address this issue, we in this work report a new full-dimensional accurate analytical PES for the title reaction using the fundamental invariant neural network (FI-NN) approach based on 140,192 points of the quality UCCSD(T)-F12a/AVTZ. Besides, the spin-orbit (SO) corrections of OH in the entrance channel were determined at the level of complete active space self-consistent field with the AVTZ basis set. As a compromise between computational cost and efficiency, the Δ-machine learning approach was employed to construct the SO-corrected PES. Based on this new FI-NN PES with analytical forces, thermal rate coefficients and various dynamic properties, including the integral cross sections, the differential cross sections, and the product energy partitioning, were determined by running a total of 5.5 million trajectories. The use of analytical gradients of the FI-NN PES accelerated simulations and about 99% of computation cost was saved, compared to that for the PIP-NN PES with numerical gradients. Such a significant acceleration is achieved mainly by replacing PIPs with FIs.

OH-Formation following vibrationally induced reaction dynamics of H2COO.

The reaction dynamics of H2COO to form HCOOH and dioxirane as first steps for OH-elimination is quantitatively investigated. Using a machine learned potential energy surface (PES) at the CASPT2/aug-cc-pVTZ level of theory vibrational excitation along the CH-normal mode νCH with energies up to 40.0 kcal mol-1 (∼5νCH) leads almost exclusively to HCOOH which further decomposes into OH + HCO. Although the barrier to form dioxirane is only 21.4 kcal mol-1 the reaction probability to form dioxirane is two orders of magnitude lower if the CH-stretch mode is excited. Following the dioxirane-formation pathway is facile, however, if the COO-bend vibration is excited together with energies equivalent to ∼2νCH or ∼3νCOO. For OH-formation in the atmosphere the pathway through HCOOH is probably most relevant because the alternative pathways (through dioxirane or formic acid) involve several intermediates that can de-excite through collisions, relax via internal vibrational relaxation (IVR), or pass through loose and vulnerable transition states (formic acid). This work demonstrates how, by selectively exciting particular vibrational modes, it is possible to dial into desired reaction channels with a high degree of specificity.

PhysNet meets CHARMM: A framework for routine machine learning/molecular mechanics simulations.

Full-dimensional potential energy surfaces (PESs) based on machine learning (ML) techniques provide a means for accurate and efficient molecular simulations in the gas and condensed phase for various experimental observables ranging from spectroscopy to reaction dynamics. Here, the MLpot extension with PhysNet as the ML-based model for a PES is introduced into the newly developed pyCHARMM application programming interface. To illustrate the conception, validation, refining, and use of a typical workflow, para-chloro-phenol is considered as an example. The main focus is on how to approach a concrete problem from a practical perspective and applications to spectroscopic observables and the free energy for the -OH torsion in solution are discussed in detail. For the computed IR spectra in the fingerprint region, the computations for para-chloro-phenol in water are in good qualitative agreement with experiment carried out in CCl4. Moreover, relative intensities are largely consistent with experimental findings. The barrier for rotation of the -OH group increases from ∼3.5 kcal/mol in the gas phase to ∼4.1 kcal/mol from simulations in water due to favorable H-bonding interactions of the -OH group with surrounding water molecules.

The neural network based Δ-machine learning approach efficiently brings the DFT potential energy surface to the CCSD(T) quality: a case for the OH + CH3OH reaction.

The recently proposed permutationally invariant polynomial-neural network (PIP-NN) based Δ-machine learning (Δ-ML) approach (PIP-NN Δ-ML) is a flexible, general, and highly cost-efficient method to develop a full dimensional accurate potential energy surface (PES). Only a small portion of points, which can be actively selected from the low-level (often DFT) dataset, with high-level energies are needed to bring a low-level PES to a high-level of quality. The hydrogen abstraction reaction between the methanol and hydroxyl radical, OH + CH3OH, has been studied using theories and experiments for a long time due to its great importance in combustion, atmospheric and interstellar chemistry. However, it is not trivial to develop the full dimensional accurate PES for it. In this work, the PIP-NN Δ-ML method is successfully applied to the title reaction. The DFT PES was fitted by using 140 192 points. Only 5% of the DFT dataset was needed to be calculated at the level of UCCSD(T)-F12a/AVTZ, aiming to improve the DFT PES to the target high-level, UCCSD(T)-F12a/AVTZ. More than 92% of the original unaffordable calculation costs were saved. The kinetics, including rate coefficients and branching ratios, were then studied by performing quasi-classical trajectory calculations on this newly fitted PES for the title reaction.

Validating experiments for the reaction H2 + NH2- by dynamical calculations on an accurate full-dimensional potential energy surface.

Ion-molecule reactions play key roles in the field of ion related chemistry. As a prototypical multi-channel ion-molecule reaction, the reaction H2 + NH2- → NH3 + H- has been studied for decades. In this work, we develop a new globally accurate potential energy surface (PES) for the title system based on hundreds of thousands of sampled points over a wide dynamically relevant region that covers long-range interacting configuration space. The permutational invariant polynomial-neural network (PIP-NN) method is used for fitting and the resulting total root mean squared error (RMSE) is extremely small, 0.026 kcal mol-1. Extensive dynamical and kinetic calculations are carried out on this PIP-NN PES. Impressively, a unique phenomenon of significant reactivity suppression by exciting the rotational mode of H2 is reported, supported by both the quasi-classical trajectory (QCT) and quantum dynamics (QD) calculations. Further analysis uncovers that exciting the H2 rotational mode would prevent the formation of the reactant complex and thus suppress the reactivity. The calculated rate coefficients for H2/D2 + NH2- agree well with the experimental results, which show an inverse temperature dependence from 50 to 300 K, consistent with the capture nature of this barrierless reaction. The significant kinetic isotope effect observed by experiments is well reproduced by the QCT computations as well.

Simultaneous statistical modelling of excess zeros, over/underdispersion, and multimodality with applications in hotel industry.

We propose zero-inflated statistical models based on the generalized Hermite distribution for simultaneously modelling of excess zeros, over/underdispersion, and multimodality. These new models are parsimonious yet remarkably flexible allowing the covariates to be introduced directly through the mean, dispersion, and zero-inflated parameters. To accommodate the interval inequality constraint for the dispersion parameter, we present a new link function for the covariate-dependent dispersion regression model. We derive score tests for zero inflation in both covariate-free and covariate-dependent models. Both the score test and the likelihood-ratio test are conducted to examine the validity of zero inflation. The score test provides a useful tool when computing the likelihood-ratio statistic proves to be difficult. We analyse several hotel booking cancellation datasets extracted from two recently published real datasets from a resort hotel and a city hotel. These extracted cancellation datasets reveal complex features of excess zeros, over/underdispersion, and multimodality simultaneously making them difficult to analyse with existing approaches. The application of the proposed methods to the cancellation datasets illustrates the usefulness and flexibility of the models.

Correction: A critical comparison of neural network potentials for molecular reaction dynamics with exact permutation symmetry.

Correction for 'A critical comparison of neural network potentials for molecular reaction dynamics with exact permutation symmetry' by Jun Li et al., Phys. Chem. Chem. Phys., 2019, 21, 9672-9682, DOI: .

A critical comparison of neural network potentials for molecular reaction dynamics with exact permutation symmetry.

The availability of accurate full-dimensional potential energy surfaces (PESs) is a mandatory condition for efficient computer simulations of molecular systems. Much effort has been devoted to developing reliable PESs with physically sound properties, such as the invariance of the energy with respect to the permutation of chemically identical atoms. In this work, we compare the performance of four neural network (NN)-based approaches with a rigorous permutation symmetry for fitting five typical reaction systems: OH + CO, H + H2S, H + NH3, H + CH4 and OH + CH4. The methods can be grouped into two categories, invariant polynomial based NNs and high-dimensional NN potentials (HD-NNPs). For the invariant polynomial based NNs, three types of polynomials, permutation invariant polynomials (PIPs), non-redundant PIPs (NRPIPs) and fundamental invariants (FIs), are used in the input layer of the NN. In HD-NNPs, the total energy is the sum of atomic contributions, each of which is given by an individual atomic NN with input vectors consisting of sets of atom-centered symmetry functions. Our results show that all methods exhibit a similar level of accuracy for the energies with respect to ab initio data obtained at the explicitly correlated coupled cluster level of theory (CCSD(T)-F12a). The HD-NNP method allows study of systems with larger numbers of atoms, making it more generally applicable than invariant polynomial based approaches, which in turn are computationally more efficient for smaller systems. To illustrate the applicability of the obtained potentials, quasi-classical trajectory calculations have been performed for the OH + CH4 → H2O + CH3 reaction to reveal its complicated mode specificity.

Is Insomnia a Risk Factor for Decreased Influenza Vaccine Response?

Healthy young adult college students (N = 133) with Insomnia (n = 65) or No Insomnia (n = 68) were compared on influenza serum antibody levels pre- and four weeks postvaccination. Volunteers underwent structured clinical interviews for sleep disorders to ensure insomnia diagnoses, as well as psychiatric interviews, physical examinations, and drug testing to ensure comorbid health problems were not potential confounds. There were significant time (both groups had increases in antibody levels pre- to postvaccination) and group (Insomnia group had lower HI antibody levels overall) main effects, but the time × group interaction was nonsignificant. Exploratory analyses did find significant PSQI x Time (p < .001) and Insomnia Status × Time (p = .002) interaction effects. Results indicate insomnia may be a risk factor for lowered immunity to the influenza virus.

Globally convergent algorithms for estimating generalized gamma distributions in fast signal and image processing.

Many applications in real-time signal, image, and video processing require automatic algorithms for rapid characterizations of signals and images through fast estimation of their underlying statistical distributions. We present fast and globally convergent algorithms for estimating the three-parameter generalized gamma distribution (G Gamma D). The proposed method is based on novel scale-independent shape estimation (SISE) equations. We show that the SISE equations have a unique global root in their semi-infinite domains and the probability that the sample SISE equations have a unique global root tends to one. The consistency of the global root, its scale, and index shape estimators is obtained. Furthermore, we establish that, with probability tending to one, Newton-Raphson (NR) algorithms for solving the sample SISE equations converge globally to the unique root from any initial value in its given domain. In contrast to existing methods, another remarkable novelty is that the sample SISE equations are completely independent of gamma and polygamma functions and involve only elementary mathematical operations, making the algorithms well suited for real-time both hardware and software implementations. The SISE estimators also allow the maximum likelihood (ML) ratio procedure to be carried out for testing the generalized Gaussian distribution (GGD) versus the G Gamma D. Finally, the fast global convergence and accuracy of our algorithms for finite samples are demonstrated by both simulation studies and real image analysis.

A trend analysis of organ transplantation among ethnic groups.

PURPOSE: This research provides public policy implications regarding organ resource allocation and increases public awareness of the current status of transplant use in various ethnic populations. PROCEDURES: Healthcare Cost and Utilization Project (HCUP), National Inpatient Sample (NIS) data were used to obtain a yearly estimate of the number of organ transplants by organ and by ethnic origin for 1988-1997. ICD-9-CM codes identified lung, heart, liver, and kidney organ-transplantation procedures. Each record in the sample was weighted by its respective discharge weight in order to extrapolate a national estimate. To assess whether there are significant differences among ethnic groups in organ transplantation rates over time, regression models were estimated for heart, liver, and kidney transplants. Transplantation rates were modeled as a function of time, ethnic origin, and interaction variables. FINDINGS: Examination of time trend graphs and regression analyses indicates that transplantation rates have not varied substantially across ethnic groups between 1988 and 1997. Rates for all groups, with the exception of Asians, exhibited similar time trends with little systematic variation. CONCLUSIONS: Further research is needed to determine whether variations exist due to organ availability versus prevalence of the disease.