Inverse Monte Carlo Method Research Articles

Development of a bottom-up coarse-grained model for interactions of lipids with TiO nanoparticles.

Understanding interactions of inorganic nanoparticles with biomolecules is important in many biotechnology, nanomedicine, and toxicological research, however, the size of typical nanoparticles makes their direct modeling by atomistic simulations unfeasible. Here, we present a bottom-up coarse-graining approach for modeling titanium dioxide (TiO ) nanomaterials in contact with phospholipids that uses the inverse Monte Carlo method to optimize the effective interactions from the structural data obtained in small-scale all-atom simulations of TiO surfaces with lipids in aqueous solution. The resulting coarse-grained models are able to accurately reproduce the structural details of lipid adsorption on different titania surfaces without the use of an explicit solvent, enabling significant computational resource savings and favorable scaling. Our coarse-grained simulations show that small spherical TiO nanoparticles ( nm) can only be partially wrapped by a lipid bilayer with phosphoethanolamine headgroups, however, the lipid adsorption increases with the radius of the nanoparticle. The current approach can be used to study the effect of the size and shape of TiO nanoparticles on their interactions with cell membrane lipids, which can be a determining factor in membrane wrapping as well as the recently discovered phenomenon of nanoquarantining, which involves the formation of layered nanomaterial-lipid structures.

Absorption and reduced scattering coefficients in epidermis and dermis from a Swedish cohort study.

Knowledge of optical properties is important to accurately model light propagation in tissue, but in vivo reference data are sparse. The aim of our study was to present in vivo skin optical properties from a large Swedish cohort including 3809 subjects using a three-layered skin model and spatially resolved diffuse reflectance spectroscopy (Periflux PF6000 EPOS). Diffuse reflectance spectra (475 to 850nm) at 0.4 and 1.2mm source-detector separations were analyzed using an inverse Monte Carlo method. The model had one epidermis layer with variable thicknesses and melanin-related absorptions and two dermis layers with varying hemoglobin concentrations and equal oxygen saturations. The reduced scattering coefficient was equal across all layers. Median absorption coefficients () in the upper dermis ranged from 0.094 at 475nm to 0.0048 at 850nm and similarly in the lower dermis from 0.059 to 0.0035. The reduced scattering coefficient () ranged from 3.22 to 1.20, and the sampling depth (mm) ranged from 0.23 to 0.38 (0.4mm separation) and from 0.49 to 0.68 (1.2mm separation). There were differences in optical properties across sex, age groups, and BMI categories. Reference material for skin optical properties is presented.

Artificial neural network-based determination of denoised optical properties in double integrating spheres measurement

Accurate determination of the optical properties of biological tissues enables quantitative understanding of light propagation in these tissues for optical diagnosis and treatment applications. The absorption ([Formula: see text]) and scattering ([Formula: see text]) coefficients of biological tissues are inversely analyzed from their diffuse reflectance (R) and total transmittance (T), which are measured using a double integrating spheres (DIS) system. The inversion algorithms, for example, inverse adding doubling method and inverse Monte Carlo method, are sensitive to noise signals during the DIS measurements, resulting in reduced accuracy during determination. In this study, we propose an artificial neural network (ANN) to estimate [Formula: see text] and [Formula: see text] at a target wavelength from the R and T spectra measured via the DIS to reduce noise in the optical properties. Approximate models of the optical properties and Monte Carlo calculations that simulated the DIS measurements were used to generate spectral datasets comprising [Formula: see text], [Formula: see text], R and T. Measurement noise signals were added to R and T, and the ANN model was then trained using the noise-added datasets. Numerical results showed that the trained ANN model reduced the effects of noise in [Formula: see text] and [Formula: see text] estimation. Experimental verification indicated noise-reduced estimation from the R and T values measured by the DIS with a small number of scans on average, resulting in measurement time reduction. The results demonstrated the noise robustness of the proposed ANN-based method for optical properties determination and will contribute to shorter DIS measurement times, thus reducing changes in the optical properties due to desiccation of the samples.

Stability, Speed, and Constraints for Structural Coarse-Graining in VOTCA.

Structural coarse-graining involves the inverse problem of deriving pair potentials that reproduce target radial distribution functions. Despite its clear mathematical formulation, there are open questions about the existing methods concerning speed, stability, and physical representability of the resulting potentials. In this work, we make progress on several aspects of iterative methods used to solve the inverse problem. Based on integral equation theory, we derive fast Gauss-Newton schemes applicable to very general systems, including molecules with bonds and mixtures. Our methods are similar to inverse Monte Carlo in terms of convergence speed and have a similar cost per iteration as iterative Boltzmann inversion. We investigate stability problems in our schemes and in the inverse Monte Carlo method and propose modifications to fix them. Furthermore, we establish how the pair potential can be constrained at each iteration to reproduce the pressure, Kirkwood-Buff integral, or the enthalpy of vaporization. We demonstrate the potential of our approach in deriving coarse-grained force fields for nine different solvents and their mixtures. All methods described are implemented in the free and open VOTCA software framework for systematic coarse-graining.

Method for Designing and Characterizing Phototherapy Shields

Optimal shield properties and design are of vital importance for preventing adverse effects of light-based clinical procedures. The goal of this study was to select the most appropriate materials for a two-layer phototherapy shield. Four biocompatible fabrics, to be utilized as the layer contacting patients’ skin, and two reflective materials, to be utilized as the layer facing the light source, were investigated. The optical properties of the four biocompatible fabrics and transmittance of the two reflective materials were determined in the 400–500 nm range. Absorption coefficient, scattering coefficient, and anisotropy factors of biocompatible fabrics were determined using integrating sphere spectrophotometry and an inverse Monte Carlo method. Fabric and reflective materials that exhibited highest attenuation of the blue light were selected to assemble a two-layer composite prototype. Prototypes were exposed to blue light emitted from a clinical source to ensure negligible temperature increase under clinically relevant exposure conditions. A protype blue-light phototherapy shield was made from two test materials, both of which provide sufficient light attenuation to provide patient protection. The testing method employed in this study may prove valuable for designing protective gear for a range of clinical procedures .

Short-range order in Fe-21.9 at.% Al

Abstract Elastic diffuse neutron scattering of an Fe-21.9 at.% Al single crystal was measured at 878, 966 and 1073 K. The separated short-range order scattering revealed the building elements of the B2 structure as the characteristic local atomic arrangement for all states investigated. Effective pair interaction parameters, obtained by the inverse Monte-Carlo method, were found to depend on temperature, thus prohibiting a determination of possible ground state structures from the high-temperature measurements. An additional X-ray study of single-crystalline Fe-18.2 at.% Al aged at 598 K, gave no indication for B32 as ground state structure.

Optimization of ray tracing algorithm for Laser Radar Cross Section calculation based on material Bidirectional Reflection Distribution Function

This paper studied the Laser Radar Cross Section (LRCS) of the target scale model by using the Bidirectional Reflection Distribution Function (BRDF) of materials and the Monte Carlo method for reverse ray tracing. The results of the research are shown through data from the satellite scale model. On the basis of the classification of the roughness of different materials and in conjunction with probability density distribution, we adopted a non-uniform sampling method to improve the split-ray collection method in the spatial distribution of scattering energy in the reverse ray tracing algorithm. By conducting the typical Monte Carlo simulation of a simple dihedron and comparing the theoretical calculation results, we tested and verified the correctness of the proposed method. We analyzed the difference between the primary and secondary scattering results of the target LRCS with different conditions. The calculation results show that for rough materials, if only primary scattering is considered in a plurality of detection directions, a large calculation error will occur, so the influence of the secondary scattering must be considered; and the contribution of the diffuse reflection component to the secondary scattering is more significant. Finally, we compared the secondary scattering of different rough materials to describe the circumstances of secondary scattering in which diffuse reflection conditions need to be considered. In addition, the inverse Monte Carlo method used in this paper to calculate the multiple scattering of the LRCS of the target can effectively improve the calculation speed of the acquisition processing. This improves the reliability of the calculation results while taking into account the estimated speed.

Effective reconstruction of bioluminescence tomography based on GPU-accelerated inverse Monte Carlo method

Diffusion equations (DEs) or simplified spherical harmonic equations are commonly used forward models in bioluminescence tomography (BLT), which are usually numerically calculated by the finite element method to construct the system matrix for reconstruction. However, the numerical solver is not accurate enough. The Monte Carlo (MC) method is regarded as the golden standard for modeling light propagation in biological tissue. In this paper, we proposed a GPU-accelerated inverse MC method for BLT reconstruction. The main feature is that the system matrix for BLT reconstruction is calculated by the MC method instead of the model-based numerical approximation. We evaluated the performance of the proposed method with both phantom-based simulation and animal-based in vivo experiment. The results show that, compared with the DE-based method, the proposed GPU-accelerated inverse MC method is more accurate and effective in BLT reconstruction.

Coarse-graining of polyisoprene melts using inverse Monte Carlo and local density potentials.

Bottom-up coarse-graining of polymers is commonly performed by matching structural order parameters such as distribution of bond lengths, bending and dihedral angles, and pair distribution functions. In this study, we introduce the distribution of nearest-neighbors as an additional order parameter in the concept of local density potentials. We describe how the inverse-Monte Carlo method provides a framework for forcefield development that is capable of overcoming challenges associated with the parameterization of interaction terms in polymer systems. The technique is applied on polyisoprene melts as a prototype system. We demonstrate that while different forcefields can be developed that perform equally in terms of matching target distributions, the inclusion of nearest-neighbors provides a straightforward route to match both thermodynamic and conformational properties. We find that several temperature state points can also be addressed, provided that the forcefield is refined accordingly. Finally, we examine both the single-particle and the collective dynamics of the coarse-grain models, demonstrating that all forcefields present a similar acceleration relative to the atomistic systems.

Utilizing nuclear data in delayed gamma-ray spectroscopy inverse Monte Carlo analysis

Safeguards verification of uranium and plutonium in high-radioactivity nuclear material is currently performed using destructive analysis techniques. However, the preparation method is a burden on both the safeguards inspectors and facility operators. While nondestructive assay (NDA) techniques would improve the efficiency and time, there are no passive NDA techniques available to directly verify the U and Pu content. As an alternative, the JAEA and JRC are collaboratively developing the Delayed Gamma-ray Spectroscopy (DGS) active-interrogation NDA technique to evaluate the fissile composition from the unique fission product yield distributions. To analyze the data we are developing an Inverse Monte Carlo (IMC) method that simulates the interrogation and evaluates the individual contributions from the mixed nuclear material to the composite spectrum. While the current nuclear data affects the ability to evaluate the composition, the IMC analysis method can be used to determine the systematic uncertainty contributions and has the potential to improve the nuclear data. We will present the current status of the DGS collaborative work as it relates to the development of the DGS IMC analysis.

DETERMINATION OF THE TISSUE ANISOTROPY FACTOR DURING THE PHOTOMETRY BY ELLIPSOIDAL REFLECTORS

Background. The features of scattering anisotropy factor determination in the research of thick biological tissue samples.Objective. The purpose of the paper is development and testing of new method of tissue scattering anisotropy factor determination. The method is based on the analysis of illuminance of different zones of photometric images, received during the photometry by ellipsoidal reflectors.Methods. For implementation of ideas and assumptions, the paper applies basics of photometry by ellipsoidal reflectors for real and model experiments, with the implementation of direct and inverse Monte Carlo method for light propagation in biological tissue. Additionally, principles of zone analysis of photometric images illuminance, received during the photometry by ellipsoidal reflectors, are applied.Results. Based on the results of real experiment, the research represents input data set for Monte Carlo simulation of light propagation in biomedical photometer with ellipsoidal reflectors. Considering the modeling, the selection of critical thickness of samples of chicken and porcine muscle tissues was reasoned for further comparison with the results of real experiment. Dependencies of illuminance of different zones of photometric images for the selected thicknesses in the significant range of anisotropy factor value change were received. Anisotropy factors were determined in the spatial scattering cross sections. There was performed the comparative evaluation of character of photometric image zone illumination dependency for samples of various muscle tissues of similar thickness, and different thicknesses of selected tissue. Based on the developed method the specificities of real experiment results reproduction during the determination of scattering anisotropy factor by the illuminance values of photometric images, received during the simulation are shown.Conclusions. Method of the mirror ellipsoid of revolution (which was used during the experiment with biological tissues samples in reflected and transmitted light) isn’t limited by the functionality of preliminary estimation of quantity of cross section for further spatial analysis and investigation of scattering indicatrix. Photometry by ellipsoidal reflectors can be applied as the separate method for determination of the magnitude of the scattering anisotropy factor based on the results of model and numerical experiments, and the developed procedure.

Transferability of Local Density-Assisted Implicit Solvation Models for Homogeneous Fluid Mixtures.

The application of bottom-up coarse grained (CG) models to study the equilibrium mixing behavior of liquids is rather challenging, since these models can be significantly influenced by the density or the concentration of the state chosen during parametrization. This dependency leads to low transferability in density/concentration space and has been one of the major limitations in bottom-up coarse graining. Recent approaches proposed to tackle this shortcoming range from the addition of thermodynamic constraints, to an extended ensemble parametrization, to the addition of supplementary terms to the system's Hamiltonian. To study fluid phase equilibria with bottom-up CG models, the application of local density (LD) potentials appears to be a promising approach, as shown in previous work by Sanyal and Shell [T. Sanyal, M. S. Shell, J. Phys. Chem. B, 2018, 122, 5678]. Here, we want to further explore this method and test its ability to model a system which contains structural inhomogeneities only on the molecular scale, namely, solutions of methanol and water. We find that a water-water LD potential improves the transferability of an implicit-methanol CG model toward high water concentration. Conversely, a methanol-methanol LD potential does not significantly improve the transferability of an implicit-water CG model toward high methanol concentration. These differences appear due to the presence of cooperative interactions in water at high concentrations that the LD potentials can capture. In addition, we compare two different approaches to derive our CG models, namely, relative entropy optimization and the Inverse Monte Carlo method, and formally demonstrate under which analytical and numerical assumptions these two methods yield equivalent results.

Implicit solvent systematic coarse-graining of dioleoylphosphatidylethanolamine lipids: From the inverted hexagonal to the bilayer structure.

Lamellar and hexagonal lipid structures are of particular importance in the biological processes such as membrane fusion and budding. Atomistic simulations of formation of these phases and transitions between them are computationally prohibitive, hence development of coarse-grained models is an important part of the methodological development in this area. Here we apply systematic bottom-up coarse-graining to model different phase structures formed by 1,2-dioleoylphosphatidylethanolamine (DOPE) lipid molecules. We started from atomistic simulations of DOPE lipids in water carried out at two different water/lipid molar ratio corresponding to the lamellar Lα and inverted hexagonal HII structures at low and high lipid concentrations respectively. The atomistic trajectories were mapped to coarse-grained trajectories, in which each lipid was represented by 14 coarse-grained sites. Then the inverse Monte Carlo method was used to compute the effective coarse-grained potentials which for the coarse-grain model reproduce the same structural properties as the atomistic simulations. The potentials derived from the low concentration atomistic simulation were only able to form a bilayer structure, while both Lα and HII lipid phases were formed in simulations with potentials obtained at high concentration. The typical atomistic configurations of lipids at high concentration combine fragments of both lamellar and non-lamellar structures, that is reflected in the extracted coarse-grained potentials which become transferable and can form a wide range of structures including the inverted hexagonal, bilayer, tubule, vesicle and micellar structures.

Modified optical coefficient measurement system for bulk tissue using an optical fiber insertion with varying field of view and depth at the fiber tip.

To measure the few millimeter-scale macroscopic optical properties of biological tissue, including the scattering coefficient, while avoiding the instability that originates from sample slicing preparation processes, we performed propagated light intensity measurements through an optical fiber that punctures the bulk tissue while varying the fiber tip depth and the field of view (FOV) at the tip; the results were analyzed using the inverse Monte Carlo method. We realized FOV changes at the fiber tip in the bulk tissue using a variable aperture that was located outside the bulk tissue through a short high-numerical aperture (high-NA) multi-mode fiber with a quasi-straight shape. Using a homogeneous optical model solution, we verified the principle and operation of the constructed experimental system. A 200-μm-core-diameter silica fiber with NA of 0.5 and length of 1m installed in a 21G needle was used. The detection fiber's shape was maintained over a radius of curvature of 30cm. The dependences of the detected light intensity on the FOV and the depth showed better than 1.4% accuracy versus calculated dependences based on the measured optical properties of the solution. Adaptation of the method for use with complex structured biological tissue, particularly in the presence of a thick fascia, was not completely resolved. However, we believe that our specific fiber puncture-based measurement method for use in bulk tissue based on variation of the FOV with inverse Monte Carlo method-based analysis will be useful in obtaining optical coefficients while avoiding sample preparation-related instabilities.

Magic v.3: An integrated software package for systematic structure-based coarse-graining

Molecular simulations of many phenomena related to biomolecular systems, soft matter and nanomaterials require consideration of length scales above 10 nm and time scales longer than 1μs, which necessitates the use of coarse-grained (low resolution) models, where each site of the model represents a group of atoms, and where the solvent is often omitted. Our software package MagiC is designed to perform systematic structure-based coarse-graining of molecular models, in which the effective pairwise potentials between coarse-grained sites of low-resolution molecular models are constructed to reproduce structural distribution functions obtained from modeling of systems in a high resolution (atomistic) description. The software takes as input atomistic trajectories generated by an external molecular dynamics package, and produce as an output interaction potentials for coarse-grained models which can be directly used in a coarse-grained simulations package. Here we present a major update (v.3) of the software with substantially improved functionality, compatibility with several major atomistic and coarse-grained simulations packages (GROMACS, LAMMPS, GALAMOST), analysis suite with graphical possibilities, diagnostics, documentation. We describe briefly the coarse-graining methodology, the structure of the software, describe users actions, and illustrate the whole process with two complex examples: cholesterol containing lipid bilayers and condensation of DNA caused by multivalent ions. Program summaryProgram Title: MagiCProgram Files doi:http://dx.doi.org/10.17632/9gnfxyshj8.1Licensing provisions: GPLv3Programming language: Fortran, PythonNature of problem: Systematic bottom-up multiscale modeling is a complex multi-stage process, in which results of simulations of a high-resolution (atomistic) model are used to construct a low resolution (coarse-grained) model, providing the same structural properties for the coarse-grained system as for the high-resolution system. Within the approach, structural properties of the high-resolution model are computed in terms of radial distribution functions and distributions of intramolecular degrees of freedom. Then the inverse problem is solved, in which the interaction potentials for the low-resolution model are determined which provide distribution functions coinciding with those obtained in the high-resolution simulations. The low-resolution model can be then used for simulations of the same system on larger length and time-scale.Solution method: The presented software package implements all stages of the systematic structure-based coarse-graining. It works as an integrated pipeline, giving the user ability to easily derive a coarse-grained model for a multicomponent complex molecular system and then use it for large-scale simulations. MagiC implements two approaches to solve the inverse problem: (i) the Inverse Monte Carlo method in which the inverse problem is solved using the Newton–Raphson method, with inversion of the Jacobian for the discretized relationship between interaction potentials and structural distribution functions; and (ii) the Iterative Boltzmann approach in which the inverse problem is solved using approximative relationships neglecting correlations between different degrees of freedom. The inverse solver includes also variational Inverse Monte Carlo approach when some of the coarse-grained potentials are fixed while others vary in order to fit the whole set of reference distribution functions.Additional comments: The MagiC main module can be also used for conventional Monte Carlo simulations of molecular systems described by tabulated pairwise potentials in the canonical ensemble.

Evolutions of packing properties of perfect cylinders under densification and crystallization.

Cylindrical particles are ubiquitous in nature and industry, and a cylinder is a representative shape of rod-like particles. However, the disordered packing results of cylinders in previous studies are quite inconsistent with each other. In this work, we obtain the MRJ (maximally random jammed) packings and the MDRPs (maximally dense random packings) of perfect cylinders with the aspect ratio (height/diameter) 0.2 ≤ w ≤ 6.0 using the ASC (adaptive shrinking cell) algorithm and the IMC (inverse Monte Carlo) method, respectively. The optimal aspect ratio corresponding to the maximal packing density is w = 0.9 in the MRJ state, while the value is w = 1.2 in the MDRP state. Then we investigate the evolutions of packing properties of perfect cylinders under densification and crystallization. We compare the different final packing states generated via the two methods with different compression rates and order constraints. In the densification procedure, we generate jammed and random packings of cylinders with various compression rates via the ASC and IMC method, respectively. When decreasing the compression rate, we find that the packing density increases but the optimal w remains the same in both methods. In the crystallization procedure, the order constraint in the IMC method is gradually released which means the degree of order in the packings is allowed to increase, and we find that the optimal w shifts from 1.2 to 0.9 while the packing density increases as well. Meanwhile, the random packings evolve into the jammed packings in the crystallization procedure which reflects the competition mechanism between the randomness and jamming. These results also indicate that the optimal w is solely related to the degree of order in the cylinder packings but not determined by the protocol or packing density. Furthermore, a uniform shape elongation effect on the random-packing densities of various shaped particles is found via a new proposed definition of the scaled aspect ratio. Finally, a rough linear relationship between the mean and standard deviation of the reduced Voronoi cell volumes is obtained only for the random packings. Our findings should lead to a better understanding toward the jammed and random packings and are helpful in guiding the granular material design.

Blockspin renormalization-group study of color confinement due to violation of the non-Abelian Bianchi identity

Block-spin transformation of topological defects is applied to the violation of the non-Abelian Bianchi identity (VMABI) on lattice defined as Abelian monopoles. To get rid of lattice artifacts, we introduce various techniques smoothing the vacuum. The effective action can be determined by adopting the inverse Monte-Carlo method. The coupling constants $F(i)$ of the effective action depend on the coupling of the lattice action $\beta$ and the number of the blocking step $n$. But it is found that $F(i)$ satisfy a beautiful scaling, that is, they are a function of the product $b=na(\beta)$ alone for lattice coupling constants $3.0\le\beta\le3.9$ and the steps of blocking $1\le n\le 12$. The effective action showing the scaling behavior can be regarded as an almost perfect action corresponding to the continuum limit, since $a\to 0$ as $n\to\infty$ for fixed $b$. The almost perfect action showing the scaling is found to be independent of the smooth gauges adopted here. Then we compare the results with those obtained by the analytic blocking method of topological defects from the continuum. The infrared monopole action can be transformed into that of the string model. The physical string tension and the lowest glueball mass can be evaluated \textit{analytically} by the strong-coupling expansion of the string model. We get $\sqrt{\sigma}\simeq 1.3\sqrt{\sigma_{phys}}$ for $b\ge 1.0\ \ (\sigma_{phys}^{-1/2})$, whereas the scalar glueball mass is kept to be near $M(0^{++})\sim 3.7\sqrt{\sigma_{phys}}$. Also we can almost reproduce \textit{analytically} the scaling function of the squared monopole density determined numerically for large $b$ region $b>1.2\ (\sigma_{phys}^{-1/2})$.

Evaluation of a pointwise microcirculation assessment method using liquid and multilayered tissue simulating phantoms.

.A fiber-optic probe-based instrument, designed for assessment of parameters related to microcirculation, red blood cell tissue fraction (), oxygen saturation (), and speed resolved perfusion, has been evaluated using state-of-the-art tissue phantoms. The probe integrates diffuse reflectance spectroscopy (DRS) at two source–detector separations and laser Doppler flowmetry, using an inverse Monte Carlo method for identifying the parameters of a multilayered tissue model. Here, we characterize the accuracy of the DRS aspect of the instrument using (1) liquid blood phantoms containing yeast and (2) epidermis-dermis mimicking solid-layered phantoms fabricated from polydimethylsiloxane, titanium oxide, hemoglobin, and coffee. The root-mean-square (RMS) deviations for for the two liquid phantoms were 11% and 5.3%, respectively, and 11% for the solid phantoms with highest hemoglobin signatures. The RMS deviation for was 5.2% and 2.9%, respectively, for the liquid phantoms, and 2.9% for the solid phantoms. RMS deviation for the reduced scattering coefficient (), for the solid phantoms was 15% (475 to 850 nm). For the liquid phantoms, the RMS deviation in average vessel diameter () was . In conclusion, the skin microcirculation parameters and , as well as, and are estimated with reasonable accuracy.

Systematic hierarchical coarse-graining with the inverse Monte Carlo method.

We outline our coarse-graining strategy for linking micro- and mesoscales of soft matter and biological systems. The method is based on effective pairwise interaction potentials obtained in detailed ab initio or classical atomistic Molecular Dynamics (MD) simulations, which can be used in simulations at less accurate level after scaling up the size. The effective potentials are obtained by applying the inverse Monte Carlo (IMC) method [A. P. Lyubartsev and A. Laaksonen, Phys. Rev. E 52(4), 3730-3737 (1995)] on a chosen subset of degrees of freedom described in terms of radial distribution functions. An in-house software package MagiC is developed to obtain the effective potentials for arbitrary molecular systems. In this work we compute effective potentials to model DNA-protein interactions (bacterial LiaR regulator bound to a 26 base pairs DNA fragment) at physiological salt concentration at a coarse-grained (CG) level. Normally the IMC CG pair-potentials are used directly as look-up tables but here we have fitted them to five Gaussians and a repulsive wall. Results show stable association between DNA and the model protein as well as similar position fluctuation profile.

Effect of magnetism and atomic order on static atomic displacements in the Invar alloy Fe-27 at.% Pt

Fe-27 at.% Pt was aged at 1123 K and quenched to room temperature (RT) to set up a state of thermal equilibrium. The local atomic arrangement was studied by diffuse x-ray scattering above (at 427 K) and below (at RT) the Curie temperature as well as at RT under a saturating magnetic field. The separated short-range order scattering remained unchanged for all three states, with maxima at 100 positions. Effective pair interaction parameters determined by the inverse Monte Carlo method gave an order-disorder transition temperature of about 1088 K, close to direct experimental findings. The species-dependent static atomic displacements for the first two shells show large differences, with a strong increase in magnitude from the state at 427 K over RT to the state under saturating magnetic field. This outcome is in agreement with an increase in atomic volume of Fe with increasing local magnetic moment. Electronic-structure calculations closely reproduce the values for the static atomic displacements in the ferromagnetic state, and predict their dependence on the atomic configuration. They also reveal a strong dependence of the magnetic exchange interactions in Fe-Pt on the atomic configuration state and lattice parameter. In particular, the increase of the Curie temperature in a random state relative to that in the ordered one is demonstrated to be related to the corresponding change of the magnetic exchange interactions due to the different local atomic chemical environment. There exists a similar strong concentration dependence of the chemical interactions as in the case of magnetic exchange interactions. Theoretical effective interactions for Fe-27 at.% Pt alloy are in good agreement with experimental results, and they also reproduce well the ${\mathrm{L}1}_{2}$-A1 transition temperature.