SEARCH · Engineering Papers

Results for “MAPPING”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 433 records · Page 24

All sources NASA NTRS DOE OSTI

DOE OSTI2015

Imaging spectroscopy algorithms for mapping canopy foliar chemical and morphological traits and their uncertainties

A major goal of remote sensing is the development of generalizable algorithms to repeatedly and accurately map ecosystem properties across space and time. Imaging spectroscopy has great potential to map vegetation traits that cannot be retrieved from broadband spectral data, but rarely have such methods been tested across broad regions. Here we illustrate a general approach for estimating key foliar chemical and morphological traits through space and time using NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-Classic). We apply partial least squares regression (PLSR) to data from 237 field plots within 51 images acquired between 2008 and 2011. Using a series of 500 randomized 50/50 subsets of the original data, we generated spatially explicit maps of seven traits (leaf mass per area (M area ), percentage nitrogen, carbon, fiber, lignin, and cellulose, and isotopic nitrogen concentration, δ 15 N) as well as pixel-wise uncertainties in their estimates based on error propagation in the analytical methods. Both Marea and %N PLSR models had a R 2 > 0.85. Root mean square errors (RMSEs) for both variables were less than 9% of the range of data. Fiber and lignin were predicted with R 2 > 0.65 and carbon and cellulose with R 2 > 0.45. Although R 2 of %C and cellulose were lower than Marea and %N, the measured variability of these constituents (especially %C) was also lower, and their RMSE values were beneath 12% of the range in overall variability. Model performance for δ 15 N was the lowest (R 2 = 0.48, RMSE = 0.95‰), but within 15% of the observed range. The resulting maps of chemical and morphological traits, together with their overall uncertainties, represent a first-of-its-kind approach for examining the spatiotemporal patterns of forest functioning and nutrient cycling across a broad range of temperate and sub-boreal ecosystems. These results offer an alternative to categorical maps of functional or physiognomic types by providing non-discrete maps (i.e., on a continuum) of traits that define those functional types. A key contribution of this work is the ability to assign retrieval uncertainties by pixel, a requirement to enable assimilation of these data products into ecosystem modeling frameworks to constrain carbon and nutrient cycling projections.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Genome_shuffling_enables_quantitative_trait_locus_mapping_in_Bacillus_subtilis

Genetic mapping is a powerful tool for eukaryotic genetics that has only been applied to bacteria in limited circumstances. Quantitative trait locus (QTL) mapping generally relies on sexual recombination to break linkages between genes, yet bacteria rarely undergo sufficient homologous recombination to generate suitable mapping populations. In this work, we used iterative biparental genome shuffling by protoplast fusion inBacillus subtilisto generate a population of bacteria with substantial random recombination throughout their genomes. Individual shuffled progeny were arrayed in well plates, resequenced, and characterized for a range of complex phenotypes including spore germination and swarming motility. Genetic mapping of the resulting phenotypes identified high-confidence QTLs of moderate size (∼10 kb), and these associations were validated through targeted genetic swaps. ThisB. subtilisQTL population can easily be used to map additional phenotypes, and the general approach for QTL mapping is applicable in a wide range of bacteria.

Bacillus subtilis↗

DOE OSTI2019

Arctic Vegetation Mapping Using Unsupervised Training Datasets and Convolutional Neural Networks

Land cover datasets are essential for modeling and analysis of Arctic ecosystem structure and function and for understanding land–atmosphere interactions at high spatial resolutions. However, most Arctic land cover products are generated at a coarse resolution, often limited due to cloud cover, polar darkness, and poor availability of high-resolution imagery. A multi-sensor remote sensing-based deep learning approach was developed for generating high-resolution (5 m) vegetation maps for the western Alaskan Arctic on the Seward Peninsula, Alaska. The fusion of hyperspectral, multispectral, and terrain datasets was performed using unsupervised and supervised classification techniques over a ~343 km 2 area, and a high-resolution (5 m) vegetation classification map was generated. An unsupervised technique was developed to classify high-dimensional remote sensing datasets into cohesive clusters. We employed a quantitative method to add supervision to the unlabeled clusters, producing a fully labeled vegetation map. We then developed convolutional neural networks (CNNs) using the multi-sensor fusion datasets to map vegetation distributions using the original classes and the classes produced by the unsupervised classification method. To validate the resulting CNN maps, vegetation observations were collected at 30 field plots during the summer of 2016, and the resulting vegetation products developed were evaluated against them for accuracy. Our analysis indicates the CNN models based on the labels produced by the unsupervised classification method provided the most accurate mapping of vegetation types, increasing the validation score (i.e., precision) from 0.53 to 0.83 when evaluated against field vegetation observations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Introducing a New Spitzer Master BLISS Map to Remove the Instrument Systematic Phase-curve-parameter Degeneracy, as Demonstrated by a Reanalysis of the 4.5 μm WASP-43b Phase Curve

While Spitzer Infrared Array Camera (IRAC) systematics are generally well understood, each data set can provide its own challenges that continue to teach us about the underlying functional form of these systematics. Multiple groups have analyzed the phase curves of WASP-43b with varying detrending techniques, each obtaining different results. In this work, we take another look at WASP-43b while exploring the degenerate relation between Bilinearly Interpolated Subpixel Sensitivity (BLISS) mapping, point response function (PRF)–FWHM detrending, and phase curve parameters. We find that there is a strong correlation between the detrending parameters in the two models, and best-fit phase curve amplitudes vary strongly when the data are temporally binned. To remove this degeneracy, we present a new Gaussian centroided intrapixel sensitivity map (hereafter fixed sensitivity map), generated using 3,712,830 exposures spanning 5 yr, for a variety of aperture sizes at 4.5 μm. We find evidence for time variability in the sensitivity at 3.6 μm and do not generate a visit-independent map for this channel. With the fixed 4.5 μm intrapixel sensitivity map, the best fits for WASP-43b no longer vary strongly with bin size and PRF–FWHM detrending is no longer required to remove correlated noise. For data sets that do not fall completely within the sweet spot, temporal binning should not be used in the analysis of Spitzer phase curves. We confirm nightside emission for WASP-43b with a disk-integrated nightside temperature of 806 ± 48 K at 4.5 μm. The 4.5 μm maps are available at github.com/kevin218/POET.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2018

On-board Radiation Sensor and Low-Altitude Contour Mapping of Radiation Fields using UAS Swarm - 18239

This paper addresses the design of light weight radiation sensors for the small-scale unmanned aerial system (UAS), and its implementation for low-altitude radiation source localization and contour mapping. The compact, high-resolution gamma-ray CZT sensors were integrated to UAS platforms as plug-and-play components using Robot Operation System (ROS). In this work, the UAS swarm consists of three platforms positioned in circular formation. The onboard data analysis provides time and position stamped intensities of gamma-ray peaks for each UAS-based sensor that are used as the input data for the swarm flight control algorithm. Each UAS measures radiation simultaneously for estimating gradient of radiation to compute a bulk heading angle of the swarm for contour mapping, which can provide relatively precise boundary of the safe area for potential human exploration. The UAS radiation sensors allow measurements from just few meters up to hundreds of meters thus filling the gap between land- and helicopter- based platforms. The swarm of UAS has advantages over a single agent based approach in detecting radiative sources and effectively mapping the area. The proposed method can locate sources of radiation as well as mapping the contaminated area for enhancing situation awareness capabilities for first responders. Computer simulation is presented for UAS swarm based source localization and contour mapping in the simulated radiation fields created by the MCNP code. Selected key gradient estimation and contour mapping algorithms are validated experimentally with small scale multicopter UAS in the indoor flight test bed. (authors)

42 ENGINEERING↗

DOE OSTI2019

R and DE Robotic Sensor Intern Team: Remote 3D Mapping Sensor Suite

The primary goal of the sensor suite is to provide an internal 3D map of the HCAEX tunnel. When looking for a sensor that would provide XYZ point cloud data for mapping the decision was between a stereo camera, a 3D Lidar, or a 2D Lidar that would need to be spun around. The issues with the stereo camera were that it does not have a large field-of-view and that its depth information was not nearly as accurate as even the baseline Lidars. The selection between Lidars is detailed. The Hokuyo UTM-30LX-EW shown provides a 270 deg. FOV with high accuracy and resolution while the largest FOV on a 3D Lidar is 40 deg.. This was the reason a 2D Lidar was chosen over the 3D Lidar. Even though the 2D Lidar requires additional components and software to generate a 3D map, its FOV advantage is too great given that the HCAEX tunnel is a relatively confined space. The rotating planar scan from the 2D Lidar can be stitched into a 3D scan as shown in the graphic below. Along with XYZ data, the Lidar provides return intensity data in color which can be used to distinguish between different materials within the tunnel. To provide visual feedback a camera system needed to be identified. The common solution for this is a high resolution camera with a small FOV mounted on a pan-tilt system. But, given the critical nature of the visual feedback we looked into solutions without the need for moving parts. Two panospheric cameras give a FOV large enough to get a full 360 deg. view.. Camera color data (RGB) can also be combined with the 3D mapping data from the Lidar and overlaid on the map to give the user the ability to see inside the entire length of the tunnel as if they were inside. Situational awareness is required for the crawler in the tunnel in order for it to be able to register the 3D scans to create a final map. For this we procured a Microstrain 3DM-GX5-25 Inertial Measurement Unit (IMU). To perform the Lidar rotation we have two servos driving the pan and tilt. The servo controls and most data processing will be performed via an onboard computer. The sensor components are to be packaged together using 3D printed mounts into a standalone unit.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2022

A New Residual Strain Mapping Program Using Energy Dispersive X-Ray Diffraction at the Advanced Photon Source

Here, the ability to non-destructively map the residual strain field inside an engineering component is important for predicting its fatigue life or developing processing methods to prevent failure or enhance performance. In this paper, we describe a new residual strain mapping program at the Advanced Photon Source, Argonne National Laboratory. The new program is based on energy dispersive x-ray diffraction (EDXRD). It is capable of non-destructively penetrating a several- cm thick polycrystalline sample fabricated from engineering alloys using high-energy x-rays and measuring the residual strain field with mm or better spatial resolution and approximately ±1 x 10 -4 strain resolution. A multi-element detector array is employed to measure multiple strain components simultaneously. The residual strain mapping setup is augmented with a high-energy tomography capability, allowing precise alignment of the material volume of interest for residual strain mapping and providing a complementary view of the structure to understand the measured strain field. These measurement capabilities are demonstrated using several strain mapping examples ranging from polycrystalline structural alloys to biological materials. We also provide some guidance for the future users of the program for a successful residual strain mapping experiment. We are expanding the capabilities of the new setup with various in situ capabilities including thermo-mechanical loading.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2018

Characteristics of the Hot Workability of an AZ91D Magnesium Alloy Using 3D Processing Maps

Hot compression tests were carried out on an a homogenized as-cast AZ91D magnesium alloy at temperatures ranging from 220 °C to 380 °C and strain rates ranging from 0.001 to 10 s{sup −1} by means of a Gleeble-3500 thermo mechanical simulator. True stress-–true strain curves were given. A constitutive modeling of the homogenized alloy was proposed to describe the flow characteristic. The deformation activation energy was estimated to be 142.27 kJ mol{sup −1}. Continuous three-dimensional (3D) processing maps were developed based on the true stress–true strain data as a function of strain, temperature, and strain rate. A detailed interpretation of the established 3D processing maps was conducted. The results showed that the variation of the 3D processing maps was significantly strain dependent. Dynamic recrystallization domains with high-power dissipation efficiencies and instability domains with negative instability coefficient were identified from the 3D power dissipation maps and the 3D instability maps, respectively. It was also revealed that the instability domains distinctly became enlarged with the increasing strain. Optimized temperature and strain rate ranges at various strains were determined. Furthermore, hot workability characteristics were clarified through examining the microstructures obtained under conditions falling within stability and instability domains with various strains. The results proved that the developed 3D processing maps can provide an accurate prediction of the hot deformation behavior for the homogenized AZ91D alloy.

36 MATERIALS SCIENCE↗

DOE OSTI2018

A New Method for Optimizing Hot Processing Parameters of Mg-6.0Zn-0.5Mn-0.5Er Alloy Based on Kinetic Model of Dynamic Recrystallization and Processing Map

Hot deformation behavior of the cast-homogenized Mg-6.0Zn-0.5Mn-0.5Er alloy was studied using dynamic recrystallization (DRX) kinetic model and processing map. The compressing tests were conducted in the temperature range of 250-450 °C and strain rates of 0.001-1 s{sup −1}. According to the evolution of microstructures, under lower strain rates, the main DRX mechanism of Mg-6.0Zn-0.5Mn-0.5Er alloy is continuous DRX (CDRX); twinning induced DRX (TDRX) and CDRX both become the main DRX mechanism under higher strain rates. The DRX kinetics model of Mg-6.0Zn-0.5Mn-0.5Er alloy is calculated as X{sub DRX}=1−exp[−1.9463(((ε−ε{sub c})/(ε{sup ∗}))){sup 1.4608}], which is corresponding to the microstructure evolutions of DRX under different deformation conditions. The contour map of DRX was proposed based on the calculation results of DRX kinetics model. The processing maps are constructed to predict processing parameters of the alloy, and the predictability was evaluated combining with the contour map of DRX and dynamic materials model (DMM) processing map. It is deduced from the microstructures evolution and processing map that the optimum processing domain is mainly at 380-450 °C and 0.01-0.001 s{sup −1}, and 410-420 °C and 0.01-1 s{sup −1}.

36 MATERIALS SCIENCE↗

DOE OSTI2015

Self-Consistent Chaotic Transport in a High-Dimensional Mean-Field Hamiltonian Map Model

We studied the self-consistent chaotic transport in a Hamiltonian mean-field model. This model provides a simplified description of transport in marginally stable systems including vorticity mixing in strong shear flows and electron dynamics in plasmas. Self-consistency is incorporated through a mean-field that couples all the degrees-of-freedom. The model is formulated as a large set of N coupled standard-like area-preserving twist maps in which the amplitude and phase of the perturbation, rather than being constant like in the standard map, are dynamical variables. Of particular interest is the study of the impact of periodic orbits on the chaotic transport and coherent structures. Furthermore, numerical simulations show that self-consistency leads to the formation of a coherent macro-particle trapped around the elliptic fixed point of the system that appears together with an asymptotic periodic behavior of the mean field. To model this asymptotic state, we introduced a non-autonomous map that allows a detailed study of the onset of global transport. A turnstile-type transport mechanism that allows transport across instantaneous KAM invariant circles in non-autonomous systems is discussed. As a first step to understand transport, we study a special type of orbits referred to as sequential periodic orbits. Using symmetry properties we show that, through replication, high-dimensional sequential periodic orbits can be generated starting from low-dimensional periodic orbits. We show that sequential periodic orbits in the self-consistent map can be continued from trivial (uncoupled) periodic orbits of standard-like maps using numerical and asymptotic methods. Normal forms are used to describe these orbits and to find the values of the map parameters that guarantee their existence. Numerical simulations are used to verify the prediction from the asymptotic methods.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2018

Myocardial Imaging with CMR Parametric Mapping: Clinical Applications

Purpose of Review: Cardiovascular magnetic resonance (CMR) parametric mapping techniques enable the direct visualization and quantification of changes in myocardial T1, T2, T2* relaxation times and extracellular volume fraction. Recent updates on clinical applications of CMR parametric mapping techniques are reviewed in this article. Recent Findings: CMR parametric mapping techniques are useful in the evaluation of multiple conditions, including iron overload, amyloid infiltration, glycosphingolipid accumulation, myocardial edema, and fibrosis. An advantage of parametric mapping over conventional tissue characterization sequences is that these techniques are quantitative, allowing for standardization of measurements and assessment of longitudinal changes over time. Parametric mapping techniques are useful in clinical practice, including aiding in establishing a diagnosis, monitoring disease progression, evaluating response to therapy, and informing prognosis. Summary: This article provides a summary of the existing evidence for the clinical application of cardiac parametric mapping techniques.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2024

Mapping use cases and dataset needs for benchmarking buildings data

A perennial challenge in buildings research is the lack of high-quality datasets that can be relied upon for a wide array of tasks, including model calibration and improving energy efficiency and load flexibility. Instrumenting a building for data collection is resource intensive, so it is important to be methodical in the approach and ensure that resulting data are flexible and useful for a broad range of analyses. This study aims to fill the gaps in characterizing potential use cases for buildings datasets and mapping them to dataset needs using a well-defined data infrastructure. Here, we have developed a systematic mapping strategy between buildings dataset needs and use cases to help streamline the processes of efficiently targeting datasets, designing building sensing systems, and determining buildings research use cases. We selected 14 prospective use cases and 11 refined buildings data categories for developing the preliminary dataset-needs-to-use-cases mapping matrix (‘DN-UC mapping matrix’) with generic ‘Tags’—a detailed sub-level of data categories extracted by justifying the needs of an aspect of the datasets to use cases. We present two example applications of the developed mapping matrix to demonstrate use of the mapping matrix and its effectiveness.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Learning high-dimensional parametric maps via reduced basis adaptive residual networks

We propose a scalable framework for the learning of high-dimensional parametric maps via adaptively constructed residual network (ResNet) maps between reduced bases of the inputs and outputs. When just few training data are available, it is beneficial to have a compact parametrization in order to ameliorate the ill-posedness of the neural network training problem. By linearly restricting high-dimensional maps to informed reduced bases of the inputs, one can compress high-dimensional maps in a constructive way that can be used to detect appropriate basis ranks, equipped with rigorous error estimates. A scalable neural network learning framework is thus to learn the nonlinear compressed reduced basis mapping. Unlike the reduced basis construction, however, neural network constructions are not guaranteed to reduce errors by adding representation power, making it difficult to achieve good practical performance. Inspired by recent approximation theory that connects ResNets to sequential minimizing flows, we present an adaptive ResNet construction algorithm. This algorithm allows for depth-wise enrichment of the neural network approximation, in a manner that can achieve good practical performance by first training a shallow network and then adapting. We prove universal approximation of the associated neural network class for $L^2_v$ functions on compact sets. Our overall framework allows for constructive means to detect appropriate breadth and depth, and related compact parametrizations of neural networks, significantly reducing the need for architectural hyperparameter tuning. Numerical experiments for parametric PDE problems and a 3D CFD wing design optimization parametric map demonstrate that the proposed methodology can achieve remarkably high accuracy for limited training data, and outperformed other neural network strategies we compared against.

42 ENGINEERING↗

DOE OSTI2021

Dynamic recrystallization of a wrought magnesium alloy: Grain size and texture maps and their application for mechanical behavior predictions

Effect of dynamic recrystallization (DRX) on the grain refinement and texture modification was studied by conducting a series of hot compression on AZ31B Mg alloy. Processing-grain size-texture maps were established as a function of temperature, strain rate, strain, and the Zener-Hollomon parameter (Z). Moreover, influence of simultaneous changes in the grain size and texture on tensile yield strength and ductility of the hot-worked Mg alloy was studied using the grain size and texture maps established. The processing-grain size map showed that the DRX grain size decreased with the increase in Z, also revealing various characteristics ranging from a grain growth, grain refinement, to a bimodal distribution of ultrafine grains and partially recrystallized grains. The effect of twinning on the grain refinement was also evident at high Z conditions. The processing-texture map revealed that the initial fiber texture was altered significantly to a shear, off-normal, or extension-twin texture with the increase in Z. The Schmid-factor maps were calculated to identify a dominant deformation mode during a subsequent tensile deformation of the hot-worked samples. Finally, a corresponding Hall-Petch relationship for each dominant deformation mode was used to establish tensile yield-strength maps, which agree well with the measured data.

36 MATERIALS SCIENCE↗

DOE OSTI2014

A Cluster-based Method to Map Urban Area from DMSP/OLS Nightlights

Accurate information of urban areas at regional and global scales is important for both the science and policy-making communities. The Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime stable light data (NTL) provide a potential way to map urban area and its dynamics economically and timely. In this study, we developed a cluster-based method to estimate the optimal thresholds and map urban extents from the DMSP/OLS NTL data in five major steps, including data preprocessing, urban cluster segmentation, logistic model development, threshold estimation, and urban extent delineation. Different from previous fixed threshold method with over- and under-estimation issues, in our method the optimal thresholds are estimated based on cluster size and overall nightlight magnitude in the cluster, and they vary with clusters. Two large countries of United States and China with different urbanization patterns were selected to map urban extents using the proposed method. The result indicates that the urbanized area occupies about 2% of total land area in the US ranging from lower than 0.5% to higher than 10% at the state level, and less than 1% in China, ranging from lower than 0.1% to about 5% at the province level with some municipalities as high as 10%. The derived thresholds and urban extents were evaluated using high-resolution land cover data at the cluster and regional levels. It was found that our method can map urban area in both countries efficiently and accurately. Compared to previous threshold techniques, our method reduces the over- and under-estimation issues, when mapping urban extent over a large area. More important, our method shows its potential to map global urban extents and temporal dynamics using the DMSP/OLS NTL data in a timely, cost-effective way.

Cities↗

DOE OSTI2023

Voltage mapping and local defects identification in solar cells using non-contact method

Electroluminescence, infrared imaging, and current–voltage curve techniques are used to detect and map underperforming cells in a photovoltaic module or the modules in a photovoltaic string. In this work, we present a non-contact electrostatic voltmeter technique to detect and map the underperforming spots in a cell and the cells in a module. This non-contact technique directly maps the charged surface voltage (671mV) of the superstrate glass, and it had an 11-mV difference with the voltmeter values (660mV). Another data set of voltage values obtained by electroluminescence images conversion into a voltage map showed a difference of 7mV with the non-contact voltmeter values. Further, the direct voltage values obtained at various good and poor-performing spots of the cells using this technique are 3.69V and 3.79V and are validated using the voltage values obtained in electroluminescence analysis and the difference in voltage obtained by the two techniques is determined to be less than 2%. In this work, we combine the strengths of two complementary techniques of the electrostatic voltmeter (strength: quantitative) and electroluminescence (strength: spatial mapping) to obtain a quantitative spatial mapping of defects. Furthermore, this work is extendable to detect the poor-performing modules in solar PV power plants.

14 SOLAR ENERGY↗

DOE OSTI2018

Application of multi-angle scattering maps to stepped surfaces

This study examines channeling, multiple scattering, and neutralization/re-ionization of ions scattered along the stepped Al(332) plane. Our experimental approach involves probing the surface with 1–2 keV He + and Ne + beams, and then systematically mapping the scattered ion fluxes over a large solid angle. This provides comprehensive ion channeling information over all directions, rather than along a few low-index azimuths, as is common practice in ion scattering spectroscopy. We first probe the surface with 2 keV He + at near-normal incidence, and then map the backscattered particle flux (both ions and neutrals) via time of flight (TOF) spectrometry. The features contained in these maps can be correlated with axial and inter-planar channeling effects, and are reproduced well via binary collision simulations. Sensitivity to the stepped surface topography is heightened considerably for oblique ion incidence in the forward-scattering direction. In this geometry, we used 2 keV Ne + to probe the surface and mapped the corresponding scattered fluxes of both single and multiply-charged ions. In both cases, the scattering intensity depends strongly on the precise trajectory taken along the surface, and is particularly sensitive to how extensively the incident ions interact with the step edges. We interpret the information contained in these maps by considering several mechanisms for charge transfer and double ion production. The formation of Ne ++ appears to be correlated with a previously observed inelastic mechanism that occurs when the collision apsis, R min , is less than 0.65 Å. In conclusion, this contributes to an energy loss of 48 ± 8 eV for Ne + undergoing single scattering; the R min threshold for this inelastic step coincides with the emergence of a distinct Ne ++ peak. Using the information gained from the maps, we propose methods for extending this approach to chemisorbed layers.

47 OTHER INSTRUMENTATION↗

DOE OSTI2018

Configurational-Bias Monte Carlo Back-Mapping Algorithm for Efficient and Rapid Conversion of Coarse-Grained Water Structures into Atomistic Models

Coarse-grained molecular dynamics (MD) simulations represent a powerful approach to simulate longer time scale and larger length scale phenomena than those accessible to all-atom models. The gain in efficiency, however, comes at the cost of atomistic details. The reverse transformation, also known as back-mapping, of coarse grained beads into their atomistic constituents represents a major challenge. Most existing approaches are limited to specific molecules or specific force-fields and often rely on running a long time atomistic MD of the back-mapped configuration to arrive at an optimal solution. Such approaches are problematic when dealing with systems with high diffusion barriers. Here, we introduce a new extension of the configurational-bias-Monte-Carlo (CBMC) algorithm, which we term the crystalline-configurational-bias-Monte-Carlo (C-CBMC) algortihm, that allows rapid and efficient conversion of a coarse-grained model back into its atomistic representation. Although the method is generic, we use a coarse-grained water model as a representative example and demonstrate the back-mapping or reverse transformation for model systems ranging from the ice-liquid water interface to amorphous and crystalline ice configurations. A series of simulations using the TIP4P/Ice model are performed to compare the new CBMC method to several other standard Monte Carlo and Molecular Dynamics based back-mapping techniques. In all the cases, the C-CBMC algorithm is able to find optimal hydrogen bonded configuration many thousand evaluations/steps sooner than the other methods compared within this paper. For crystalline ice structures such as a hexagonal, cubic, and cubic-hexagonal stacking disorder structures, the C-CBMC was able to find structures that were between 0.05 and 0.1 eV/water molecule lower in energy than the ground state energies predicted by the other methods. Detailed analysis of the atomistic structures show a significantly better global hydrogen positioning when contrasted with the existing simpler back-mapping methods. The error in the RDFs of backmapped relative to reference configuration for the CCBMC, MD, and MC were found to be 6.9, 8.7, and 12.9 respectively for the hexagonal system. For the cubic system the relative error of the RDFs for the CCBMC, MD, and MC were found to be 18.2, 34.6, and 39.0 respectively. Finally, our results demonstrate the efficiency and efficacy of our new back-mapping approach, especially for crystalline systems where simple force-field based relaxations have a tendency to get trapped in local minima.

74 ATOMIC AND MOLECULAR PHYSICS↗