Motion in a Random Force Field

We consider the motion of a particle in a random isotropic force field. Assuming that the force field arises from a Poisson field in $\mathbb{R}^d$, $d \geq 4$, and the initial velocity of the particle is sufficiently large, we describe the asymptotic behavior of the particle

Development and Validation of a ReaxFF Reactive Force Field for Cu Cation/Water Interactions and Copper Metal/Metal Oxide/Metal Hydroxide Condensed Phases

To enable large-scale reactive dynamic simulations of copper oxide/water and copper ion/water interactions we have extended the ReaxFF reactive force field framework to Cu/O/H interactions. To this end, we employed a multistage force field development strategy, where the initial training set (containing metal/metal oxide/metal hydroxide condensed phase data and [Cu(H_2O)_n]^(2+) cluster structures and energies) is augmented by single-point quantum mechanices (QM) energies from [Cu(H_2O)_n]^(2+) clusters abstracted from a ReaxFF molecular dynamics simulation. This provides a convenient strategy to both enrich the training set and to validate the final force field. To further validate the force field description we performed molecular dynamics simulations on Cu^(2+)/water systems. We found good agreement between our results and earlier experimental and QM-based molecular dynamics work for the average Cu/water coordination, Jahn−Teller distortion, and inversion in [Cu(H_2O)_6]^(2+) clusters and first- and second-shell O−Cu−O angular distributions, indicating that this force field gives a satisfactory description of the Cu-cation/water interactions. We believe that this force field provides a computationally convenient method for studying the solution and surface chemistry of metal cations and metal oxides and, as such, has applications for studying protein/metal cation complexes, pH-dependent crystal growth/dissolution, and surface catalysis

Force field feature extraction for ear biometrics

The overall objective in defining feature space is to reduce the dimensionality of the original pattern space, whilst maintaining discriminatory power for classification. To meet this objective in the context of ear biometrics a new force field transformation treats the image as an array of mutually attracting particles that act as the source of a Gaussian force field. Underlying the force field there is a scalar potential energy field, which in the case of an ear takes the form of a smooth surface that resembles a small mountain with a number of peaks joined by ridges. The peaks correspond to potential energy wells and to extend the analogy the ridges correspond to potential energy channels. Since the transform also turns out to be invertible, and since the surface is otherwise smooth, information theory suggests that much of the information is transferred to these features, thus confirming their efficacy. We previously described how field line feature extraction, using an algorithm similar to gradient descent, exploits the directional properties of the force field to automatically locate these channels and wells, which then form the basis of characteristic ear features. We now show how an analysis of the mechanism of this algorithmic approach leads to a closed analytical description based on the divergence of force direction, which reveals that channels and wells are really manifestations of the same phenomenon. We further show that this new operator, with its own distinct advantages, has a striking similarity to the Marr-Hildreth operator, but with the important difference that it is non-linear. As well as addressing faster implementation, invertibility, and brightness sensitivity, the technique is also validated by performing recognition on a database of ears selected from the XM2VTS face database, and by comparing the results with the more established technique of Principal Components Analysis. This confirms not only that ears do indeed appear to have potential as a biometric, but also that the new approach is well suited to their description, being robust especially in the presence of noise, and having the advantage that the ear does not need to be explicitly extracted from the background

Universal Vectorial and Ultrasensitive Nanomechanical Force Field Sensor

Miniaturization of force probes into nanomechanical oscillators enables ultrasensitive investigations of forces on dimensions smaller than their characteristic length scale. Meanwhile it also unravels the force field vectorial character and how its topology impacts the measurement. Here we expose an ultrasensitive method to image 2D vectorial force fields by optomechanically following the bidimensional Brownian motion of a singly clamped nanowire. This novel approach relies on angular and spectral tomography of its quasi frequency-degenerated transverse mechanical polarizations: immersing the nanoresonator in a vectorial force field does not only shift its eigenfrequencies but also rotate eigenmodes orientation as a nano-compass. This universal method is employed to map a tunable electrostatic force field whose spatial gradients can even take precedence over the intrinsic nanowire properties. Enabling vectorial force fields imaging with demonstrated sensitivities of attonewton variations over the nanoprobe Brownian trajectory will have strong impact on scientific exploration at the nanoscale

Amino-acid-dependent main-chain torsion-energy terms for protein systems

Many commonly used force fields for protein systems such as AMBER, CHARMM, GROMACS, OPLS, and ECEPP have amino-acid-independent force-field parameters of main-chain torsion-energy terms. Here, we propose a new type of amino-acid-dependent torsion-energy terms in the force fields. As an example, we applied this approach to AMBER ff03 force field and determined new amino-acid-dependent parameters for $\psi$ and $\psi'$ angles for each amino acid by using our optimization method, which is one of the knowledge-based approach. In order to test the validity of the new force-field parameters, we then performed folding simulations of $\alpha$-helical and $\beta$-hairpin peptides, using the optimized force field. The results showed that the new force-field parameters gave structures more consistent with the experimental implications than the original AMBER ff03 force field.Comment: 10 pages, (Revtex4.1), 3 tables, 6 figure

Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning

Classical intermolecular potentials typically require an extensive parametrization procedure for any new compound considered. To do away with prior parametrization, we propose a combination of physics-based potentials with machine learning (ML), coined IPML, which is transferable across small neutral organic and biologically-relevant molecules. ML models provide on-the-fly predictions for environment-dependent local atomic properties: electrostatic multipole coefficients (significant error reduction compared to previously reported), the population and decay rate of valence atomic densities, and polarizabilities across conformations and chemical compositions of H, C, N, and O atoms. These parameters enable accurate calculations of intermolecular contributions---electrostatics, charge penetration, repulsion, induction/polarization, and many-body dispersion. Unlike other potentials, this model is transferable in its ability to handle new molecules and conformations without explicit prior parametrization: All local atomic properties are predicted from ML, leaving only eight global parameters---optimized once and for all across compounds. We validate IPML on various gas-phase dimers at and away from equilibrium separation, where we obtain mean absolute errors between 0.4 and 0.7 kcal/mol for several chemically and conformationally diverse datasets representative of non-covalent interactions in biologically-relevant molecules. We further focus on hydrogen-bonded complexes---essential but challenging due to their directional nature---where datasets of DNA base pairs and amino acids yield an extremely encouraging 1.4 kcal/mol error. Finally, and as a first look, we consider IPML in denser systems: water clusters, supramolecular host-guest complexes, and the benzene crystal.Comment: 15 pages, 9 figure

DREIDING: A generic force field for molecular simulations

We report the parameters for a new generic force field, DREIDING, that we find useful for predicting structures and dynamics of organic, biological, and main-group inorganic molecules. The philosophy in DREIDING is to use general force constants and geometry parameters based on simple hybridization considerations rather than individual force constants and geometric parameters that depend on the particular combination of atoms involved in the bond, angle, or torsion terms. Thus all bond distances are derived from atomic radii, and there is only one force constant each for bonds, angles, and inversions and only six different values for torsional barriers. Parameters are defined for all possible combinations of atoms and new atoms can be added to the force field rather simply. This paper reports the parameters for the "nonmetallic" main-group elements (B, C, N, 0, F columns for the C, Si, Ge, and Sn rows) plus H and a few metals (Na, Ca, Zn, Fe). The accuracy of the DREIDING force field is tested by comparing with (i) 76 accurately determined crystal structures of organic compounds involving H, C, N, 0, F, P, S, CI, and Br, (ii) rotational barriers of a number of molecules, and (iii) relative conformational energies and barriers of a number of molecules. We find excellent results for these systems