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Neural network potentials achieve CASPT2 accuracy for reactive chemistry and molecular simulations. Using transfer learning, these potentials require minimal CASPT2 data on small systems to accurately predict bond dissociation in larger systems.
A quantum mechanical force field (QMFF) for water is described. Unlike traditional approaches that use quantum mechanical results and experimental data to parameterize empirical potential energy functions, the present QMFF uses a quantum mechanical framework to represent intramolecular and intermolecular interactions in an entire condensed-phase system. In particular, the internal energy terms used in molecular mechanics are replaced by a quantum mechanical formalism that naturally includes electronic polarization due to intermolecular interactions and its effects on the force constants of the intramolecular force field. As a quantum mechanical force field, both intermolecular interactions and the Hamiltonian describing the individual molecular fragments can be parameterized to strive for accuracy and computational efficiency. In this work, we introduce a polarizable molecular orbital model Hamiltonian for water and for oxygen- and hydrogen-containing compounds, whereas the electrostatic potential responsible for intermolecular interactions in the liquid and in solution is modeled by a three-point charge representation that realistically reproduces the total molecular dipole moment and the local hybridization contributions. The present QMFF for water, which is called the XP3P (explicit polarization with three-point-charge potential) model, is suitable for modeling both gas-phase clusters and liquid water. The paper demonstrates the performance of the XP3P model for water and proton clusters and the properties of the pure liquid from about 900 × 10{sup 6} self-consistent-field calculations on a periodic system consisting of 267 water molecules. The unusual dipole derivative behavior of water, which is incorrectly modeled in molecular mechanics, is naturally reproduced as a result of an electronic structural treatment of chemical bonding by XP3P. We anticipate that the XP3P model will be useful for studying proton transport in solution and solid phases as well as across biological ion channels through membranes.
Generation of flash X-rays (FXRs) at less than 500 keV is described with emphasis on experimental investigation. The pulser is a Tesla transformer-Water transmission line based pulsed power generator operating in double resonance mode to power a rod-pinch diode. The configuration of aspect ratio reported here falls much below the normally reported ratios for the rod-pinch diode operation. Experimental investigation at such low pulsed voltage has revealed “flowering” of the anode tip and “pitting” of the perspex window. A possible explanation in terms of Lorentz body force is discussed rather than the pinch mechanism generally suggested in literature. The experimental investigation for the FXR generation is corroborated by measuring the radiation dose using CaSO{sub 4} (Dy) thermo luminescent dosimeters.
Nanowires of magnetic materials such as Iron, nickel, cobalt, and alloys of them are one of the most widely investigated structures because of their possible applications in high density magnetic recording media, sensor elements, and building blocks in biological transport systems. In this work, Iron nanowires have been prepared by electrodeposition technique using Anodized Aluminium Oxide (AAO) templates. The electrolyte used consisted of FeSO{sub 4.}6H{sub 2}O buffered with H{sub 3}BO{sub 3} and acidized by dilute H{sub 2}SO{sub 4}. FESEM analysis shows that the asdeposited nanowires are parallel to one another and have high aspect ratio with a reasonably high pore-filing factor. To fabricate the working electrode, a thin film of copper (∼ 220 nm thick) was coated on back side of AAO template by e-beam evaporation system to create electrical contact with the external circuit. The TEM results show that electrodeposited nanowires have diameter around 100 nm and are polycrystalline in structure. Magnetic properties show the existence of anisotropy for in and out of plane configuration. These nanowires have potential applications in magnetic data storage, catalysis and magnetic sensor applications.
BaH (and its isotopomers) is an attractive molecular candidate for laser cooling to ultracold temperatures and a potential precursor for the production of ultracold gases of hydrogen and deuterium. The theoretical challenge is to simulate the laser cooling cycle as reliably as possible and this paper addresses the generation of a highly accurate ab initio {sup 2}Σ{sup +} potential for such studies. The performance of various basis sets within the multi-reference configuration-interaction (MRCI) approximation with the Davidson correction is tested and taken to the Complete Basis Set (CBS) limit. It is shown that the calculated molecular constants using a 46 electron effective core-potential and even-tempered augmented polarized core-valence basis sets (aug-pCVnZ-PP, n = 4 and 5) but only including three active electrons in the MRCI calculation are in excellent agreement with the available experimental values. The predicted dissociation energy D{sub e} for the X{sup 2}Σ{sup +} state (extrapolated to the CBS limit) is 16 895.12 cm{sup −1} (2.094 eV), which agrees within 0.1% of a revised experimental value of <16 910.6 cm{sup −1}, while the calculated r{sub e} is within 0.03 pm of the experimental result.
The Caldeira-Leggett (CL) model, which describes a system bi-linearly coupled to a harmonic bath, has enjoyed popularity in condensed phase spectroscopy owing to its utmost simplicity. However, the applicability of the model to cases with anharmonic system potentials, as it is required for the description of realistic systems in solution, is questionable due to the presence of the invertibility problem [F. Gottwald et al., J. Phys. Chem. Lett. 6, 2722 (2015)] unless the system itself resembles the CL model form. This might well be the case at surfaces or in the solid regime, which we here confirm for a particular example of an iodine molecule in the atomic argon environment under high pressure. For this purpose we extend the recently proposed Fourier method for parameterizing linear generalized Langevin dynamics [F. Gottwald et al., J. Chem. Phys. 142, 244110 (2015)] to the non-linear case based on the CL model and perform an extensive error analysis. In order to judge on the applicability of this model in advance, we give practical empirical criteria and discuss the effect of the potential renormalization term. The obtained results provide evidence that the CL model can be used for describing a potentially broad class of systems.
Graphene oxide (GO) is a promising novel nanomaterial with a wide range of potential biomedical applications due to its many intriguing properties. However, very little research has been conducted to study its possible adverse effects on protein-protein interactions (and thus subsequent toxicity to human). Here, the potential cytotoxicity of GO is investigated at molecular level using large-scale, all-atom molecular dynamics simulations to explore the interaction mechanism between a protein dimer and a GO nanosheet oxidized at different levels. Our theoretical results reveal that GO nanosheet could intercalate between the two monomers of HIV-1 integrase dimer, disrupting the protein-protein interactions and eventually lead to dimer disassociation as graphene does [B. Luan et al., ACS Nano 9(1), 663 (2015)], albeit its insertion process is slower when compared with graphene due to the additional steric and attractive interactions. This study helps to better understand the toxicity of GO to cell functions which could shed light on how to improve its biocompatibility and biosafety for its wide potential biomedical applications.
Semiclassical Transition State Theory (SCTST), a method for calculating rate constants of chemical reactions, offers gains in computational efficiency relative to more accurate quantum scattering methods. In full-dimensional (FD) SCTST, reaction probabilities are calculated from third and fourth potential derivatives along all vibrational degrees of freedom. However, the computational cost of FD SCTST scales unfavorably with system size, which prohibits its application to larger systems. In this study, the accuracy and efficiency of 1-D SCTST, in which only third and fourth derivatives along the reaction mode are used, are investigated in comparison to those of FD SCTST. Potential derivatives are obtained from numerical ab initio Hessian matrix calculations at the MP2/cc-pVTZ level of theory, and Richardson extrapolation is applied to improve the accuracy of these derivatives. Reaction barriers are calculated at the CCSD(T)/cc-pVTZ level. Results from FD SCTST agree with results from previous theoretical and experimental studies when Richardson extrapolation is applied. Results from our implementation of 1-D SCTST, which uses only 4 single-point MP2/cc-pVTZ energy calculations in addition to those for conventional TST, agree with FD results to within a factor of 5 at 250 K. This degree of agreement and the efficiency of the 1-D method suggest its potential as a means of approximating rate constants for systems too large for existing quantum scattering methods.
Very recently, boron nitride atomic chains were successively prepared and observed in experiments [O. Cretu et al., ACS Nano 8, 11950 (2015)]. Herein, using a first-principles technique, we study the magnetism and spin-dependent electronic transport properties of three types of BN atomic chains whose magnetic moment is 1 μ{sub B} for B{sub n}N{sub n−1}, 2 μ{sub B} for B{sub n}N{sub n}, and 3 μ{sub B} for B{sub n}N{sub n+1} type atomic chains, respectively. The spin-dependent electronic transport results demonstrate that the short B{sub n}N{sub n+1} chain presents an obvious spin-filtering effect with high spin polarization ratio (>90%) under low bias voltages. Yet, this spin-filtering effect does not occur for long B{sub n}N{sub n+1} chains under high bias voltages and other types of BN atomic chains (B{sub n}N{sub n−1} and B{sub n}N{sub n}). The proposed short B{sub n}N{sub n+1} chain is predicted to be an effective low-bias spin filters. Moreover, the length-conductance relationships of these BN atomic chains were also studied.
Fundamental details concerning the interaction between H{sub 2} and CH{sub 3}–Si(111) have been elucidated by the combination of diffractive scattering experiments and electronic structure and scattering calculations. Rotationally inelastic diffraction (RID) of H{sub 2} and D{sub 2} from this model hydrocarbon-decorated semiconductor interface has been confirmed for the first time via both time-of-flight and diffraction measurements, with modest j = 0 → 2 RID intensities for H{sub 2} compared to the strong RID features observed for D{sub 2} over a large range of kinematic scattering conditions along two high-symmetry azimuthal directions. The Debye-Waller model was applied to the thermal attenuation of diffraction peaks, allowing for precise determination of the RID probabilities by accounting for incoherent motion of the CH{sub 3}–Si(111) surface atoms. The probabilities of rotationally inelastic diffraction of H{sub 2} and D{sub 2} have been quantitatively evaluated as a function of beam energy and scattering angle, and have been compared with complementary electronic structure and scattering calculations to provide insight into the interaction potential between H{sub 2} (D{sub 2}) and hence the surface charge density distribution. Specifically, a six-dimensional potential energy surface (PES), describing the electronic structure of the H{sub 2}(D{sub 2})/CH{sub 3}−Si(111) system, has been computed based on interpolation of density functional theory energies. Quantum and classical dynamics simulations have allowed for an assessment of the accuracy of the PES, and subsequently for identification of the features of the PES that serve as classical turning points. A close scrutiny of the PES reveals the highly anisotropic character of the interaction potential at these turning points. This combination of experiment and theory provides new and important details about the interaction of H{sub 2} with a hybrid organic-semiconductor interface, which can be used to further investigate energy flow in technologically relevant systems.
For two electronic states coupled by conical intersections, the line integral of the derivative coupling can be used to construct a complex-valued multiplicative phase factor that makes the real-valued adiabatic electronic wave function single-valued, provided that the curl of the derivative coupling is zero. Unfortunately for ab initio determined wave functions, the curl is never rigorously zero. However, when the wave functions are determined from a coupled two diabatic state Hamiltonian H d (fit to ab initio data), the resulting derivative couplings are by construction curl free, except at points of conical intersection. Here in this work we focus on a recently introduced diabatization scheme that produces the H d by fitting ab initio determined energies, energy gradients, and derivative couplings to the corresponding H d determined quantities in a least squares sense, producing a removable approximation to the ab initio determined derivative coupling. This approach and related numerical issues associated with the nonremovable ab initio derivative couplings are illustrated using a full 33-dimensional representation of phenol photodissociation. Finally, the use of this approach to provide a general framework for treating the molecular Aharonov Bohm effect is demonstrated.
Machine-learning potentials (MLPs) trained on data from quantum-mechanics based first-principles methods can approach the accuracy of the reference method at a fraction of the computational cost. To facilitate efficient MLP-based molecular dynamics and Monte Carlo simulations, an integration of the MLPs with sampling software is needed. Here, we develop two interfaces that link the atomic energy network (ænet) MLP package with the popular sampling packages TINKER and LAMMPS. The three packages, ænet, TINKER, and LAMMPS, are free and open-source software that enable, in combination, accurate simulations of large and complex systems with low computational cost that scales linearly with the number of atoms. Scaling tests show that the parallel efficiency of the ænet–TINKER interface is nearly optimal but is limited to shared-memory systems. The ænet–LAMMPS interface achieves excellent parallel efficiency on highly parallel distributed memory systems and benefits from the highly optimized neighbor list implemented in LAMMPS. We demonstrate the utility of the two MLP interfaces for two relevant example applications: the investigation of diffusion phenomena in liquid water and the equilibration of nanostructured amorphous battery materials.
Supported metallic nanoparticles play a central role in catalysis. However, predictive modeling is particularly challenging due to the structural and dynamic complexity of the nanoparticle and its interface with the support, given that the sizes of interest are often well beyond those accessible via traditional ab initio methods. With recent advances in machine learning, it is now feasible to perform MD simulations with potentials retaining near-density-functional theory (DFT) accuracy, which can elucidate the growth and relaxation of supported metal nanoparticles, as well as reactions on those catalysts, at temperatures and time scales approaching those relevant to experiments. Furthermore, the surfaces of the support materials can also be modeled realistically through simulated annealing to include effects such as defects and amorphous structures. We study the adsorption of fluorine atoms on ceria and silica supported palladium nanoparticles using machine learning potential trained by DFT data using the DeePMD framework. We show defects on ceria and Pd/ceria interfaces are crucial for the initial adsorption of fluorine, while the interplay between Pd and ceria and the reverse oxygen migration from ceria to Pd control spillover of fluorine from Pd to ceria at later stages. In contrast, silica supports do not induce fluorine spillover from Pd particles.
A set of deep neural network models for rheumatoid arthritis (RA) classification using a highway network, a convolutional neural network and a residual network is proposed based on the data of diffuse optical tomography (DOT) utilising near-infrared light, which ensures early diagnosis of pathophysiological changes resulting from inflammation. A numerical model of the finger is used to generate images to overcome the inherent problem of insufficient clinical DOT images available. The proposed deep neural network models are applied to automatically classify simulated DOT images of inflamed and non-inflamed joints and transfer learning is also used to improve the performance of the classification. The results demonstrate that all three deep neural network methods improve the diagnostic accuracy as compared to the widely applied support vector machine (SVM), especially for high inter-subject variability databases. In cases of distinct modelled severity of disease, residual network achieved the highest accuracy (> 99 %), and both of highway and convolutional neural networks reached 99 %, respectively. However, as the severity of the modelled disease is reduced, this accuracy is reduced to 75.2 % for residual networks. The results indicate that transfer learning can improve the performance of deep neural network methods on RA classification from DOT data and highlight their potential as a computer aided tool in DOT diagnostic systems. (paper)
Since its inception over two decades ago, Kelvin probe force microscopy (KPFM) has become the standard technique for characterizing electrostatic, electrochemical and electronic properties at the nanoscale. In this work, we present a purely digital, software-based approach to KPFM utilizing big data acquisition and analysis methods. General Mode (G-Mode) KPFM, works by capturing the entire photodetector data stream, typically at the sampling rate limit, followed by subsequent de-noising, analysis and compression of the cantilever response. We demonstrate that the G-Mode approach allows simultaneous multi-harmonic detection, combined with on-the-fly transfer function correction required for quantitative CPD mapping. The KPFM approach outlined in this work significantly simplifies the technique by avoiding cumbersome instrumentation optimization steps (i.e. lock in parameters, feedback gains etc.), while also retaining the flexibility to be implemented on any atomic force microscopy platform. We demonstrate the added advantages of G-Mode KPFM by allowing simultaneous mapping of CPD and capacitance gradient (C') channels as well as increased flexibility in data exploration across frequency, time, space, and noise domains. As a result, G-Mode KPFM is particularly suitable for characterizing voltage sensitive materials or for operation in conductive electrolytes, and will be useful for probing electrodynamics in photovoltaics, liquids and ionic conductors.
Gamma-rays from cosmological sources contain information about gamma-ray interactions. Standard model and non-standard model photon interactions along the path between the source and the observer can lead to changes in the energy or state of the photons, which in turn alters the observed energy spectrum of the source. In general, these interactions are a function of photon energy as well as source distance. Here we show how existing high energy gamma-ray observations of blazars can be used to constrain the coupling of axion-like-particles (ALPs) to the photon. The same ALP-photon coupling that has been invoked to explain the observations of TeV blazars beyond their pair production horizon is shown to have a potential effect on the data set of Fermi blazars.
Peak demand on electricity grids is a growing problem that increases costs and risks to supply security. Residential sector loads often contribute significantly to seasonal and daily peak demand. Demand response refers to consumer actions that change the utility load profile in a way that reduces costs or improves grid security by applying price signals and automated load shedding technologies. The methodologies that are used to achieve demand response can hardly be applicable in developing countries. Peak pricing of electricity, for instance, can hardly be implemented in many developing countries as high prices would disproportionately affect the many low-income households who do not have the capacity to take action to avoid paying high peak prices. This study aims to develop demand response methodology that can be applied in developing countries to achieve residential peak demand reduction. We use a consumer preference survey to develop a methodology suitable for developing countries. The method of diversified demand is used with energy audit and monitored data to estimate the potential peak load reduction and its cost-effectiveness for Ghana. Results show that peak reduction of 15-210 MW is expected by 2040 with a positive return on investment of 2-22% for all designed scenarios.
From 1948 to 1954, the Atomic Bomb Casualty Commission conducted a study of pregnancy outcomes among births to atomic bomb survivors (Hiroshima and Nagasaki, Japan) who had received radiation doses ranging from 0 Gy to near-lethal levels. Past reports (1956, 1981, and 1990) on the cohort did not identify significant associations of radiation exposure with untoward pregnancy outcomes, such as major congenital malformations, stillbirths, or neonatal deaths, individually or in aggregate. We reexamined the risk of major congenital malformations and perinatal deaths in the children of atomic bomb survivors (n = 71,603) using fully reconstructed data to minimize the potential for bias, using refined estimates of the gonadal dose from Dosimetry System 2002 and refined analytical methods for characterizing dose-response relationships. The analyses showed that parental exposure to radiation was associated with increased risk of major congenital malformations and perinatal death, but the estimates were imprecise for direct radiation effects, and most were not statistically significant. Nonetheless, the uniformly positive estimates for untoward pregnancy outcomes among children of both maternal and paternal survivors are useful for risk assessment purposes, although extending them to populations other than the atomic bomb survivors comes with uncertainty as to generalizability.