Solid-liquid phase transition inside van der Waals nanobubbles: an atomistic perspective.

The liquid-solid phase transition during the confinement of a van der Waals bubble is studied using molecular dynamics simulations. In particular, argon is considered inside a graphene bubble, where the outer membrane is a sheet of graphene, and the substrate is atomically flat graphite. A methodology to avoid metastable states of argon is developed and implemented to derive a melting curve of trapped argon. It is found that in the confinement, the melting curve of argon shifts toward higher temperatures, and the temperature shift is about 10-30 K. The ratio of the height to the radius of the GNB (H/R) decreases with increasing temperature. It also most likely undergoes an abrupt change through the liquid-crystal phase transition. The semi-liquid state of argon was detected in the transition region. At this state, the argon structure stays layered, but the atoms travel distances of several lattice constants.

The ab initio potential energy curves of atom pairs and transport properties of high-temperature vapors of Cu and Si and their mixtures with He, Ar, and Xe gases.

The potential energy curves (PECs) for the homonuclear He-He, Ar-Ar, Cu-Cu, and Si-Si dimers, as well as heteronuclear Cu-He, Cu-Ar, Cu-Xe, Si-He, Si-Ar, and Si-Xe dimers, are obtained in quantum Monte Carlo (QMC) calculations. It is shown that the QMC method provides the PECs with an accuracy comparable with that of the state-of-the-art coupled cluster singles and doubles with perturbative triples corrections [CCSD(T)] calculations. The QMC data are approximated by the Morse long range (MLR) and (12-6) Lennard-Jones (LJ) potentials. The MLR and LJ potentials are used to calculate the deflection angles in binary collisions of corresponding atom pairs and transport coefficients of Cu and Si vapors and their mixtures with He, Ar, and Xe gases in the range of temperature from 100 K to 10 000 K. It is shown that the use of the LJ potentials introduces significant errors in the transport coefficients of high-temperature vapors and gas mixtures. The mixtures with heavy noble gases demonstrate anomalous behavior when the viscosity and thermal conductivity can be larger than that of the corresponding pure substances. In the mixtures with helium, the thermal diffusion factor is found to be unusually large. The calculated viscosity and diffusivity are used to determine parameters of the variable hard sphere and variable soft sphere molecular models as well as parameters of the power-law approximations for the transport coefficients. The results obtained in the present work include all information required for kinetic or continuum simulations of dilute Cu and Si vapors and their mixtures with He, Ar, and Xe gases.

Why Na+ has higher propensity than K+ to condense DNA in a crowded environment.

Experimentally, in the presence of the crowding agent polyethylene glycol (PEG), sodium ions compact double-stranded DNA more readily than potassium ions. Here, we have used molecular dynamics simulations and the "ion binding shells model" of DNA condensation to provide an explanation for the observed variations in condensation of short DNA duplexes in solutions containing different monovalent cations and PEG; several predictions are made. According to the model we use, externally bound ions contribute the most to the ion-induced aggregation of DNA duplexes. The simulations reveal that for two adjacent DNA duplexes, the number of externally bound Na+ ions is larger than the number of K+ ions over a wide range of chloride concentrations in the presence of PEG, providing a qualitative explanation for the higher propensity of sodium ions to compact DNA under crowded conditions. The qualitative picture is confirmed by an estimate of the corresponding free energy of DNA aggregation that is at least 0.2kBT per base pair more favorable in solution with NaCl than with KCl at the same ion concentration. The estimated attraction free energy of DNA duplexes in the presence of Na+ depends noticeably on the DNA sequence; we predict that AT-rich DNA duplexes are more readily condensed than GC-rich ones in the presence of Na+. Counter-intuitively, the addition of a small amount of a crowding agent with high affinity for the specific condensing ion may lead to the weakening of the ion-mediated DNA-DNA attraction, shifting the equilibrium away from the DNA condensed phase.

Universal shape of graphene nanobubbles on metallic substrate.

Graphene nanobubbles (GNBs) are formed from matter trapped between a two-dimensional material and a substrate. Such structures exhibit a wide range of new fundamental phenomena and are promising for nanoelectronic applications. However, a central part of the synthesis methods leads to the formation of GNBs with undetermined matter composition. Moreover, none of the GNBs' synthesis methods allow one to control the type of trapped matter. In a recent paper [K. M. Zahra, PCCP, 22,7606 (2020)], the authors proposed a new approach that allows the production of GNBs on a copper substrate with pure nitrogen inside in a controlled manner. In this work, we continue this research by studying the geometry of the GNBs in detail and indirectly measuring the internal pressure, which depends on the van der Waals adhesion energy and elastic properties of the graphene membrane. In agreement with other studies, we observe that dome-shaped bubbles exhibit universal scaling law, i.e., constant height to radius ratio. However, the measured height to radius ratio differs significantly from the known results of experiments and computer simulations. This deviation is explained by applying the membrane theory and taking into account the high adhesion of the copper substrate and graphene sheet. The adhesion energy calculated based on experimental data is close to the measurements performed by other experimental techniques.

nablaDFT: Large-Scale Conformational Energy and Hamiltonian Prediction benchmark and dataset.

Electronic wave function calculation is a fundamental task of computational quantum chemistry. Knowledge of the wave function parameters allows one to compute physical and chemical properties of molecules and materials. Unfortunately, it is infeasible to compute the wave functions analytically even for simple molecules. Classical quantum chemistry approaches such as the Hartree-Fock method or density functional theory (DFT) allow to compute an approximation of the wave function but are very computationally expensive. One way to lower the computational complexity is to use machine learning models that can provide sufficiently good approximations at a much lower computational cost. In this work we: (1) introduce a new curated large-scale dataset of electron structures of drug-like molecules, (2) establish a novel benchmark for the estimation of molecular properties in the multi-molecule setting, and (3) evaluate a wide range of methods with this benchmark. We show that the accuracy of recently developed machine learning models deteriorates significantly when switching from the single-molecule to the multi-molecule setting. We also show that these models lack generalization over different chemistry classes. In addition, we provide experimental evidence that larger datasets lead to better ML models in the field of quantum chemistry.

Liquid-gas phase transition of Ar inside graphene nanobubbles on the graphite substrate.

Graphene nanobubbles (GNBs) are formed when a substance is trapped between a graphene sheet (a 2D crystal) and an atomically flat substrate. The physical state of the substance inside GNBs can vary from the gas phase to crystal clusters. In this paper, we present a theoretical description of the gas-liquid phase transition of argon inside GNBs. The energy minimization concept is used to calculate the equilibrium properties of the bubble at constant temperature for a given mass of captured substance. We consider the total energy as a sum of the elastic energy of the graphene sheet, the bulk energy of the inner substance and the energy of adhesion between this substance, the substrate and graphene. The developed model allows us to reveal a correlation between the size of the bubble and the physical state of the substance inside it. A special case of a GNB that consists of argon trapped between a graphene sheet and a graphite substrate is considered. We predict the 'forbidden range' of radii, within which no stable GNBs exist, that separates bubble sizes with liquid argon inside from bubble sizes with gaseous argon. The height-to-radius ratio of the bubble is found to be constant for radii greater than 200 nm, which is consistent with experimental observations. The proposed model can be extended to various types of trapped substances and 2D crystals.

Modeling of the phase transition inside graphene nanobubbles filled with ethane.

Graphene nanobubbles consist of a substance that is trapped between graphene sheets and atomically flat substrates. This substance is an example of confinement in which both the bulk and surface interactions and the tension of the graphene determine the mechanical and thermodynamic properties of the system. The van der Waals pressure build up due to the graphene-substrate attraction and surface influence facilitates the advanced condensation of trapped substances. Different phases of the trapped substance are assumed to be found inside the graphene nanobubbles depending on their radii. Smaller radii are attributed to the crystal and liquid phases, and larger radii correspond to the gas phase. In this study, graphene nanobubbles filled with ethane on a graphite substrate are investigated. The choice of trapped substance is inspired by typical experiments in which graphene nanobubbles are obtained with a mixture of hydrocarbons inside. We apply a multiscale model based on both molecular dynamics simulations and a continuum 1D model to obtain the shape of the bubble, stress distribution and phase state of the trapped substance. Calculations are performed for a set of temperatures below and above the critical temperature of ethane. A liquid-gas phase transition below the critical temperature leads to a 'forbidden range' of radii, in which no stable bubbles exist.

Ultrafast Polarization Switching in BaTiO3 Nanomaterials: Combined Density Functional Theory and Coupled Oscillator Study.

The challenge of achieving ultrafast switching of electric polarization in ferroelectric materials remains unsolved as there is no experimental evidence of such switching to date. In this study, we developed an enhanced model that describes switching within a two-dimensional space of generalized coordinates at THz pulses. Our findings indicate that stable switching in barium titanate cannot be achieved through a single linearly polarized pulse. When the intensity of the linearly polarized pulse reaches a certain threshold, the sample experiences depolarization but not stable switching. Our study also reveals that phonon friction plays a minor role in the switching dynamics and provides an estimate of the optimal parameters for the perturbing pulse with the lowest intensity that results in the depolarization of an initially polarized sample.

Process Parameter Selection for Production of Stainless Steel 316L Using Efficient Multi-Objective Bayesian Optimization Algorithm.

Additive manufacturing is a modern technique to produce parts with a complex geometry. However, the choice of the printing parameters is a time-consuming and costly process. In this study, the parameter optimization for the laser powder bed fusion process was investigated. Using state-of-the art multi-objective Bayesian optimization, the set of the most-promising process parameters (laser power, scanning speed, hatch distance, etc.), which would yield parts with the desired hardness and porosity, was established. The Gaussian process surrogate model was built on 57 empirical data points, and through efficient sampling in the design space, we were able to obtain three points in the Pareto front in just over six iterations. The produced parts had a hardness ranging from 224-235 HV and a porosity in the range of 0.2-0.37%. The trained model recommended using the following parameters for high-quality parts: 58 W, 257 mm/s, 45 µm, with a scan rotation angle of 131 degrees. The proposed methodology greatly reduces the number of experiments, thus saving time and resources. The candidate process parameters prescribed by the model were experimentally validated and tested.

Application of two-component neural network for exchange-correlation functional interpolation.

Density functional theory (DFT) is one of the primary approaches to solving the many-body Schrodinger equation. The essential part of the DFT theory is the exchange-correlation (XC) functional, which can not be obtained in analytical form. Accordingly, the accuracy improvement of the DFT is mainly based on the development of XC functional approximations. Commonly, they are built upon analytic solutions in low- and high-density limits and result from quantum Monte Carlo or post-Hartree-Fock numerical calculations. However, there is no universal functional form to incorporate these data into XC functional. Instead, various parameterizations use heuristic rules to build a specific XC functional. The neural network (NN) approach to interpolate the data from higher precision theories can give a unified path to parametrize an XC functional. Moreover, data from many existing quantum chemical databases could provide the XC functional with improved accuracy. We develop NN XC functional, which gives exchange potential and energy density without direct derivatives of exchange-correlation energy density. Proposed NN architecture consists of two parts NN-E and NN-V, which could be trained in separate ways, adding new flexibility to XC functional. We also show that the developed NN XC functional converges in the self-consistent cycle and gives reasonable energies when applied to atoms, molecules, and crystals.

Comment on "Pushing the frontiers of density functionals by solving the fractional electron problem".

Kirkpatrick et al. (Reports, 9 December 2021, p. 1385) trained a neural network-based DFT functional, DM21, on fractional-charge (FC) and fractional-spin (FS) systems, and they claim that it has outstanding accuracy for chemical systems exhibiting strong correlation. Here, we show that the ability of DM21 to generalize the behavior of such systems does not follow from the published results and requires revisiting.

Similarities and Differences between Na+ and K+ Distributions around DNA Obtained with Three Popular Water Models.

We have compared distributions of sodium and potassium ions around double-stranded DNA, simulated using fixed charge SPC/E, TIP3P, and OPC water models and the Joung/Cheatham (J/C) ion parameter set, as well as the Li/Merz HFE 6-12 (L/M HFE) ion parameters for OPC water. In all the simulations, the ion distributions are in qualitative agreement with Manning's condensation theory and the Debye-Hückel theory, where expected. In agreement with experiment, binding affinity of monovalent ions to DNA does not depend on ion type in every solvent model. However, behavior of deeply bound ions, including ions bound to specific sites, depends strongly on the solvent model. In particular, the number of potassium ions in the minor groove of AT-tracts differs at least 3-fold between the solvent models tested. The number of sodium ions associated with the DNA agrees quantitatively with the experiment for the OPC water model, followed closely by TIP3P+J/C; the largest deviation from the experiment, ∼10%, is seen for SPC/E+J/C. On the other hand, SPC/E+J/C model is most consistent (67%) with the experimental potassium binding sites, followed by OPC+J/C (60%), TIP3P+J/C (53%), and OPC+L/M HFE (27%). The use of NBFIX correction with TIP3P+J/C improves its consistency with the experiment. In summary, the choice of the solvent model matters little for simulating the diffuse atmosphere of sodium and potassium ions around DNA, but ion distributions become increasingly sensitive to the solvent model near the helical axis. We offer an explanation for these trends. There is no single gold standard solvent model, although OPC water with J/C ions or TIP3P with J/C + NBFIX may offer an imperfect compromise for practical simulations of ionic atmospheres around DNA.

Neural network interpolation of exchange-correlation functional.

Density functional theory (DFT) is one of the most widely used tools to solve the many-body Schrodinger equation. The core uncertainty inside DFT theory is the exchange-correlation (XC) functional, the exact form of which is still unknown. Therefore, the essential part of DFT success is based on the progress in the development of XC approximations. Traditionally, they are built upon analytic solutions in low- and high-density limits and result from quantum Monte Carlo numerical calculations. However, there is no consistent and general scheme of XC interpolation and functional representation. Many different developed parametrizations mainly utilize a number of phenomenological rules to construct a specific XC functional. In contrast, the neural network (NN) approach can provide a general way to parametrize an XC functional without any a priori knowledge of its functional form. In this work, we develop NN XC functionals and prove their applicability to 3-dimensional physical systems. We show that both the local density approximation (LDA) and generalized gradient approximation (GGA) are well reproduced by the NN approach. It is demonstrated that the local environment can be easily considered by changing only the number of neurons in the first layer of the NN. The developed NN XC functionals show good results when applied to systems that are not presented in the training/test data. The generalizability of the formulated NN XC framework leads us to believe that it could give superior results in comparison with traditional XC schemes provided training data from high-level theories such as the quantum Monte Carlo and post-Hartree-Fock methods.

Atomistic study of the solid state inside graphene nanobubbles.

A two-dimensional (2D) material placed on an atomically flat substrate can lead to the formation of surface nanobubbles trapping different types of substances. In this paper graphene nanobubbles of the radius of 7-34 nm with argon atoms inside are studied using molecular dynamics (MD). All modeled graphene nanobubbles except for the smallest ones exhibit an universal shape, i.e., a constant ratio of a bubble height to its footprint radius, which is in an agreement with experimental studies and their interpretation using the elastic theory of membranes. MD simulations reveal that argon does exist in a solid close-packed phase, although the internal pressure in the nanobubble is not sufficiently high for the ordinary crystallization that would occur in a bulk system. The smallest graphene bubbles with a radius of 7 nm exhibit an unusual "pancake" shape. Previously, nanobubbles with a similar pancake shape were experimentally observed in completely different systems at the interface between water and a hydrophobic surface.