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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.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2011

Predicting dynamics and rheology of blood flow: A comparative study of multiscale and low-dimensional models of red blood cells

In this work we compare the predictive capability of two mathematical models for red blood cells (RBCs) focusing on blood flow in capillaries and arterioles. Both RBC models as well as their corresponding blood flows are based on the dissipative particle dynamics (DPD) method, a coarse-grained molecular dynamics approach. The first model employs a multiscale description of the RBC (MS-RBC), with its membrane represented by hundreds or even thousands of DPD-particles connected by springs into a triangular network in combination with out-of-plane elastic bending resistance. Extra dissipation within the network accounts for membrane viscosity, while the characteristic biconcave RBC shape is achieved by imposition of constraints for constant membrane area and constant cell volume. The second model is based on a low-dimensional description (LD-RBC) constructed as a closed torus-like ring of only 10 large DPD colloidal particles. They are connected into a ring by worm-like chain (WLC) springs combined with bending resistance. The LD-RBC model can be fitted to represent the entire range of nonlinear elastic deformations as measured by optical-tweezers for healthy and for infected RBCs in malaria. MS-RBCs suspensions model the dynamics and rheology of blood flow accurately for any size vessel but this approach is computationally expensive above 100 microns. Surprisingly, the much more economical suspensions of LD-RBCs also capture the blood flow dynamics and rheology accurately except for vessels with sizes comparable to RBC diameter. In particular, the LD-RBC suspensions are shown to properly capture the experimental data for the apparent viscosity of blood and its cell-free layer (CFL) in tube flow. Taken together, these findings suggest a hierarchical approach in modeling blood flow in the arterial tree, whereby the MS-RBC model should be employed for capillaries and arterioles below 100 microns, the LD-RBC model for arterioles, and the continuum description for arteries.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Crystal dissolution by particle detachment

Crystal dissolution, which is a fundamental process in both natural and technological settings, has been predominately viewed as a process of ion-by-ion detachment into a surrounding solvent. Here we report a mechanism of dissolution by particle detachment (DPD) that dominates in mesocrystals formed via crystallization by particle attachment (CPA). Using liquid phase electron microscopy to directly observe dissolution of hematite crystals — both compact rhombohedra and mesocrystals of coaligned nanoparticles — we find that the mesocrystals evolve into branched structures, which disintegrate as individual sub-particles detach. The resulting dissolution rates far exceed those for equivalent masses of compact single crystals. Applying a numerical generalization of the Gibbs-Thomson effect, we show that the physical drivers of DPD are curvature and strain inherently tied to the original CPA process. Based on the generality of the model, we anticipate that DPD is widespread for both natural minerals and synthetic crystals formed via CPA.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Power of Ensemble Diversity and Randomization for Energy Aggregation

Abstract We study an ensemble of diverse (inhomogeneous) thermostatically controlled loads aggregated to provide the demand response (DR) services in a district-level energy system. Each load in the ensemble is assumed to be equipped with a random number generator switching heating/cooling on or off with a Poisson rate, r , when the load leaves the comfort zone. Ensemble diversity is modeled through inhomogeneity/disorder in the deterministic dynamics of loads. Approached from the standpoint of statistical physics, the ensemble represents a non-equilibrium system driven away from its natural steady state by the DR. The ability of the ensemble to recover by mixing faster to the steady state after its DR’s use is advantageous. The trade-off between the level of the aggregator’s control, commanding the devices to lower the rate r , and the phase-space-oscillatory deterministic dynamics is analyzed. Then, we study the effect of the load diversity, investigating four different disorder probability distributions (DPDs) ranging from the case of the Gaussian DPD to the case of the uniform with finite support DPD. We show that stronger regularity of the DPD results in faster mixing, which is similar to the Landau damping in plasma physics. Our theoretical analysis is supported by extensive numerical validation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2014

Multi-scale coarse-graining of non-conservative interactions in molecular liquids

A new bottom-up procedure for constructing non-conservative (dissipative and stochastic) interactions for dissipative particle dynamics (DPD) models is described and applied to perform hierarchical coarse-graining of a polar molecular liquid (nitromethane). The distant-dependent radial and shear frictions in functional-free form are derived consistently with a chosen form for conservative interactions by matching two-body force-velocity and three-body velocity-velocity correlations along the microscopic trajectories of the centroids of Voronoi cells (clusters), which represent the dissipative particles within the DPD description. The Voronoi tessellation is achieved by application of the K-means clustering algorithm at regular time intervals. Consistently with a notion of many-body DPD, the conservative interactions are determined through the multi-scale coarse-graining (MS-CG) method, which naturally implements a pairwise decomposition of the microscopic free energy. A hierarchy of MS-CG/DPD models starting with one molecule per Voronoi cell and up to 64 molecules per cell is derived. The radial contribution to the friction appears to be dominant for all models. As the Voronoi cell sizes increase, the dissipative forces rapidly become confined to the first coordination shell. For Voronoi cells of two and more molecules the time dependence of the velocity autocorrelation function becomes monotonic and well reproduced by the respective MS-CG/DPD models. A comparative analysis of force and velocity correlations in the atomistic and CG ensembles indicates Markovian behavior with as low as two molecules per dissipative particle. The models with one and two molecules per Voronoi cell yield transport properties (diffusion and shear viscosity) that are in good agreement with the atomistic data. The coarser models produce slower dynamics that can be appreciably attributed to unaccounted dissipation introduced by regular Voronoi re-partitioning as well as by larger numerical errors in mapping out the dissipative forces. The framework presented herein can be used to develop computational models of real liquids which are capable of bridging the atomistic and mesoscopic scales.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Dissipative particle dynamics for directed self-assembly of block copolymers

The dissipative particle dynamics (DPD) simulation method has been shown to be a promising tool to study self-assembly of soft matter systems. In particular, it has been used to study block copolymer (BCP) self-assembly. However, previous parameterizations of this model are not able to capture most of the rich phase behaviors of BCPs in thin films nor in directed self-assembly (chemoepitaxy or graphoepitaxy). Here, we extend the applicability of the DPD method for BCPs to make it applicable to thin films and directed self-assembly. Our new reparameterization not only is able to reproduce the bulk phase behavior but also manages to predict thin film structures obtained experimentally from chemoepitaxy or graphoepitaxy. A number of different complex structures, such as bilayer nanomeshes, 90° bend structures, circular cylinders/lamellae and Frank-Kasper phases directed by trenches, and post arrays or chemically patterned substrates, have all been reproduced in this work. This reparameterized DPD model should serves as a powerful tool to predict BCP self-assembly, especially in some complex systems where it is difficult to implement self-consistent field theory.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Operator learning for predicting multiscale bubble growth dynamics

We report simulating and predicting multiscale problems that couple multiple physics and dynamics across many orders of spatiotemporal scales is a great challenge that has not been investigated systematically by deep neural networks (DNNs). Herein, we develop a framework based on operator regression, the so-called deep operator network (DeepONet), with the long-term objective to simplify multiscale modeling by avoiding the fragile and time-consuming “hand-shaking” interface algorithms for stitching together heterogeneous descriptions of multiscale phenomena. To this end, as a first step, we investigate if a DeepONet can learn the dynamics of different scale regimes, one at the deterministic macroscale and the other at the stochastic microscale regime with inherent thermal fluctuations. Specifically, we test the effectiveness and accuracy of the DeepONet in predicting multirate bubble growth dynamics, which is described by a Rayleigh–Plesset (R–P) equation at the macroscale and modeled as a stochastic nucleation and cavitation process at the microscale by dissipative particle dynamics (DPD). First, we generate data using the R–P equation for multirate bubble growth dynamics caused by randomly time-varying liquid pressures drawn from Gaussian random fields (GRFs). Our results show that properly trained DeepONets can accurately predict the macroscale bubble growth dynamics and can outperform long short-term memory networks. We also demonstrate that the DeepONet can extrapolate accurately outside the input distribution using only very few new measurements. Subsequently, we train the DeepONet with DPD data corresponding to stochastic bubble growth dynamics. Although the DPD data are noisy and we only collect sparse data points on the trajectories, the trained DeepONet model is able to predict accurately the mean bubble dynamics for time-varying GRF pressures. Taken together, our findings demonstrate that DeepONets can be employed to unify the macroscale and microscale models of the multirate bubble growth problem, hence providing new insight into the role of operator regression via DNNs in tackling realistic multiscale problems and in simplifying modeling with heterogeneous descriptions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

7-Deazaguanines in DNA: functional and structural elucidation of a DNA modification system

Abstract The modified nucleosides 2′-deoxy-7-cyano- and 2′-deoxy-7-amido-7-deazaguanosine (dPreQ0 and dADG, respectively) recently discovered in DNA are the products of the bacterial queuosine tRNA modification pathway and the dpd gene cluster, the latter of which encodes proteins that comprise the elaborate Dpd restriction–modification system present in diverse bacteria. Recent genetic studies implicated the dpdA, dpdB and dpdC genes as encoding proteins necessary for DNA modification, with dpdD–dpdK contributing to the restriction phenotype. Here we report the in vitro reconstitution of the Dpd modification machinery from Salmonella enterica serovar Montevideo, the elucidation of the roles of each protein and the X-ray crystal structure of DpdA supported by small-angle X-ray scattering analysis of DpdA and DpdB, the former bound to DNA. While the homology of DpdA with the tRNA-dependent tRNA-guanine transglycosylase enzymes (TGT) in the queuosine pathway suggested a similar transglycosylase activity responsible for the exchange of a guanine base in the DNA for 7-cyano-7-deazaguanine (preQ0), we demonstrate an unexpected ATPase activity in DpdB necessary for insertion of preQ0 into DNA, and identify several catalytically essential active site residues in DpdA involved in the transglycosylation reaction. Further, we identify a modification site for DpdA activity and demonstrate that DpdC functions independently of DpdA/B in converting preQ0-modified DNA to ADG-modified DNA.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Coarse-Grained Reactive Molecular Dynamics Simulations of Heterogeneities in Shocked Energetic Materials (LDRD Final Report)

This report summarizes the result of LDRD project 16-0161, titled "Coarse-Grained Re- active Molecular Dynamics Simulations of Heterogeneities in Shocked Energetic Materials." The purpose of the project was to develop a coarse-grained reactive molecular dynamics capability in LAMMPS enabling simulations of initiation in energetic materials comparable in accuracy to what is currently possible using large-scale reactive molecular dynamics, but with greatly reduced computational cost. The starting point for this work was the reactive dissipative particle dynamics (DPD) approach, which has been implemented as the new USER-DPD package in LAMMPS by researchers at Army Research Laboratory. Using modified versions of the examples provided with the new package, we examined the computational efficiency of the method, as well as its ability to model energy release in energetic materials. We observed that the Shardlow splitting method provides a great speed and accuracy advantage over conventional velocity Verlet time integration. We observed that the generic model of an energetic material provided with the USER-DPD package exhibited incomplete reaction under constant volume conditions. This was caused by quenching of the internal temperature of the molecules due to a rapid build-up of repulsive interactions between product gas components under constant volume conditions. Under constant pressure conditions, complete reaction was observed, as volume expansion prevented the buildup of strong repulsive interactions. Finally, a more realistic model calibrated to reproduce the equation of state of the RDX molecular crystal was examined. This model exhibited much less quenching of the internal temperature under constant volume conditions and reacted very rapidly under constant pressure conditions.

74 ATOMIC AND MOLECULAR PHYSICS↗
NASA NTRS1993

Changes in markers of bone formation and resorption in a bed rest model of weightlessness

To study the mechanism of bone loss in physical unloading, we examined indices of bone formation and bone resorption in the serum and urine of eight healthy men during a 7 day -6 degrees head-down tilt bed rest. Prompt increases in markers of resorption--pyridinoline (PD), deoxypyridinoline (DPD), and hydroxyproline (Hyp)/g creatinine--during the first few days of inactivity were paralleled by tartrate-resistant acid phosphatase (TRAP) with significant increases in all these markers by day 4 of bed rest. An index of formation, skeletal alkaline phosphatase (SALP), did not change during bed rest and showed a moderate 15% increase 1 week after reambulation. In contrast to SALP, serum osteocalcin (OC) began increasing the day preceding the increase in Hyp, remained elevated for the duration of the bed rest, and returned to pre-bed rest values within 5 days of reambulation. Similarly, DPD increased significantly at the onset of bed rest, remained elevated for the duration of bed rest, and returned to pre-bed rest levels upon reambulation. On the other hand, the other three indices of resorption, Hyp, PD, and TRAP, remained elevated for 2 weeks after reambulation. The most sensitive indices of the levels of physical activity proved to be the noncollagenous protein, OC, and the collagen crosslinker, DPD. The bed rest values of both these markers were significantly elevated compared to both the pre-bed rest values and the post-bed rest values. The sequence of changes in the circulating markers of bone metabolism indicated that increases in serum OC are the earliest responses of bone to head-down tilt bed rest.

NASA Discipline Number 26-10↗
NASA NTRS2004

Variability of collagen crosslinks: impact of sample collection period

Because of the variability of collagen crosslinks, their use as markers for bone resorption is often criticized. We hypothesized that the variability could be reduced by collecting urine for 24 hours (or longer) instead of using single voids, and by not normalizing to creatinine. Urine samples were collected from 22 healthy subjects during two or more 24-hour periods. Each 24-hour pool and each 2nd void of the day were analyzed for N-telopeptide (NTX), pyridinium (PYD), and deoxypyridinoline (DPD) crosslinks. Data were analyzed by using linear regression. For NTX, R2 for the two, 2nd-void samples (n = 38) was 0.55, whereas R2 for the two 24-hour pools was 0.51 or 0.52, expressed per day or per creatinine. For PYD and DPD, R2 for the 2nd-void samples was 0.26 and 0.18, R2 for the 24-hour pools expressed per day was 0.58 and 0.74, and R2 for the 24-hour pools expressed per creatinine was 0.65 and 0.76, respectively. Regression of the 2nd void and the corresponding 24-hour pool, expressed per day, yielded R2 = 0.19, 0.19, and 0.08, for NTX, PYD, and DPD, respectively (n = 76 each). For the 2nd-void sample and its corresponding 24-hour pool, expressed per creatinine, R2 = 0.24, 0.33, and 0.08, respectively. In a separate study, the coefficient of variation for NTX was reduced (P < 0.05) when data from more than one 24-hour collection were combined. Thus, the variability inherent in crosslink determinations can be reduced by collecting urine for longer periods. In research studies, the high variability of single-void collections, compounded by creatinine normalization, may alter or obscure findings.

Specimen Handling/methods↗
NASA NTRS2005

Amino acid supplementation alters bone metabolism during simulated weightlessness

High-protein and acidogenic diets induce hypercalciuria. Foods or supplements with excess sulfur-containing amino acids increase endogenous sulfuric acid production and therefore have the potential to increase calcium excretion and alter bone metabolism. In this study, effects of an amino acid/carbohydrate supplement on bone resorption were examined during bed rest. Thirteen subjects were divided at random into two groups: a control group (Con, n = 6) and an amino acid-supplemented group (AA, n = 7) who consumed an extra 49.5 g essential amino acids and 90 g carbohydrate per day for 28 days. Urine was collected for n-telopeptide (NTX), deoxypyridinoline (DPD), calcium, and pH determinations. Bone mineral content was determined and potential renal acid load was calculated. Bone-specific alkaline phosphatase was measured in serum samples collected on day 1 (immediately before bed rest) and on day 28. Potential renal acid load was higher in the AA group than in the Con group during bed rest (P < 0.05). For all subjects, during bed rest urinary NTX and DPD concentrations were greater than pre-bed rest levels (P < 0.05). Urinary NTX and DPD tended to be higher in the AA group (P = 0.073 and P = 0.056, respectively). During bed rest, urinary calcium was greater than baseline levels (P < 0.05) in the AA group but not the Con group. Total bone mineral content was lower after bed rest than before bed rest in the AA group but not the Con group (P < 0.05). During bed rest, urinary pH decreased (P < 0.05), and it was lower in the AA group than the Con group. These data suggest that bone resorption increased, without changes in bone formation, in the AA group.

Randomized Controlled Trial↗
NASA NTRS2005

Heritability of markers of bone metabolism

Several classic twin studies show genetic effects on markers of bone health, including bone mineral density and parathyroid hormone (PTH). This study was performed to assess the relative contribution of genetics to biochemical markers of bone metabolism. Fifteen sets of identical twins (8 male, 7 female) were housed in a clinical research center where diet was controlled (15% protein, 55% carbohydrate, 30% fat) for 3 consecutive days. Each day, 24-h urine pools were collected and N-telopeptide (NTX), deoxypyridinoline (DPD), calcium, and serum PTH were measured. The broad-sense heritability factor (H2) is an estimation of the portion of the total variance of a given phenotype that is attributable to genetic variance. H2 was estimated from the correlation coefficient of the phenotype data. H2 for NTX was 94% for males and 80% for females, DPD was 88% for males and 97% for females, urinary calcium excretion was 97% for males and 90% for females, and PTH was 92% for males and 79% for females. Since environmental variability was minimized for the 3 days of data collection, these heritability factors are likely overestimated. Nonetheless, the data support the concept that PTH is a predominantly heritable trait, and suggest that NTX, DPD, and calcium excretion are as well. These biochemical data support the previously documented heritability of bone health.

Smith, Scott M.↗
NASA NTRS2009

Bone Remodeling Monitor

The impact of bone loss due to different mechanical loadings in microgravity is a major concern for astronauts upon reintroduction to gravitational forces in exploration missions to the Moon and Mars. it has been shown that astronauts not only lose bone at differing rates, with levels up to 2% per month, but each astronaut will respond to bone loss treatments differently. Pre- and post-flight imaging techniques and frozen urine samples for post-flight laboratory immunoassays To develop a novel, non-invasive, highly . sensitive, portable, intuitive, and low-powered device to measure bone resorption levels in 'real time' to provide rapid and Individualized feedback to maximize the efficacy of bone loss countermeasures 1. Collect urine specimen and analyze the level of bone resorption marker, DPD (deoxypridinoline) excreted. 2. Antibodies specific to DPD conjugated with nanoshells and mixed with specimen, the change in absorbance from agglutination is measured by an optical device. 3. The concentration of DPD is displayed and recorded on a PDA

Foucar, Charlie↗
DOE OSTI2013

Computational Biorheology of Human Blood Flow in Health and Disease

Hematologic disorders arising from infectious diseases, hereditary factors and environmental influences can lead to, and can be influenced by, significant changes in the shape, mechanical and physical properties of red blood cells (RBCs), and the biorheology of blood flow. Hence, modeling of hematologic disorders should take into account the multiphase nature of blood flow, especially in arterioles and capillaries. We present here an overview of a general computational framework based on dissipative particle dynamics (DPD) which has broad applicability in cell biophysics with implications for diagnostics, therapeutics and drug efficacy assessments for a wide variety of human diseases. This computational approach, validated by independent experimental results, is capable of modeling the biorheology of whole blood and its individual components during blood flow so as to investigate cell mechanistic processes in health and disease. DPD is a Lagrangian method that can be derived from systematic coarse-graining of molecular dynamics but can scale efficiently up to arterioles and can also be used to model RBCs down to the spectrin level. We start from experimental measurements of a single RBC to extract the relevant biophysical parameters, using single-cell measurements involving such methods as optical tweezers, atomic force microscopy and micropipette aspiration, and cell-population experiments involving microfluidic devices. We then use these validated RBC models to predict the biorheological behavior of whole blood in healthy or pathological states, and compare the simulations with experimental results involving apparent viscosity and other relevant parameters. Finally, while the approach discussed here is sufficiently general to address a broad spectrum of hematologic disorders including certain types of cancer, this paper specifically deals with results obtained using this computational framework for blood flow in malaria and sickle cell anemia.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Accuracy of 99mTc-Hydroxymethylene diphosphonate scintigraphy for diagnosis of transthyretin cardiac amyloidosis

Background and Aim: Either 99mTechnetium diphosphonate (Tc-DPD) or pyrophosphate (Tc-PYP) scintigraphy plays a relevant role in diagnosing transthyretin cardiac amyloidosis (CA), and labeled radiotracers have been extensively studied in diagnosing CA. Few studies have analyzed and validated 99mTc-Hydroxymethylene diphosphonate (Tc-HMDP). Our aim was to validate the diagnostic accuracy of Tc-HMDP total-body scintigraphy in a cohort of patients with biopsy-proven transthyretin CA. Methods and Results: We retrospectively evaluated all patients undergoing 99mTc-HMDP total-body scintigraphy, in adjunct to a comprehensive diagnostic work-up for suspected CA. Sixty-five patients were finally diagnosed with CA, while it was excluded in 20 subjects with left ventricular hypertrophy of various etiologies. Twenty-six patients had AL-CA, 39 had TTR CA (16 TTRm, 23 TTRwt). At Tc-HMDP scintigraphy, 2 AL patients showed a Perugini score grade 1 heart uptake, while 24 showed no uptake. All TTR patients showed Tc-HMDP uptake, with three patients showing a Perugini score grade 1, 16 grade 2, and 20 grade 3, respectively. No uptake was observed in patients with left ventricular hypertrophy. A positive Tc-HMDP scintigraphy showed a 100% sensitivity and a 96% specificity for TTR CA identification. Conclusions: Tc-HMDP scintigraphy is as accurate as Tc-DPD or Tc-PYP, and may therefore de facto be considered a valuable tool for the diagnosis of TTR CA.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2011

Pseudo-random number generation for Brownian Dynamics and Dissipative Particle Dynamics simulations on GPU devices

Highlights: {yields} Molecular Dynamics codes implemented on GPUs have achieved two-order of magnitude computational accelerations. {yields} Brownian Dynamics and Dissipative Particle Dynamics simulations require a large number of random numbers per time step. {yields} We introduce a method for generating small batches of pseudorandom numbers distributed over many threads of calculations. {yields} With this method, Dissipative Particle Dynamics is implemented on a GPU device without requiring thread-to-thread communication. - Abstract: Brownian Dynamics (BD), also known as Langevin Dynamics, and Dissipative Particle Dynamics (DPD) are implicit solvent methods commonly used in models of soft matter and biomolecular systems. The interaction of the numerous solvent particles with larger particles is coarse-grained as a Langevin thermostat is applied to individual particles or to particle pairs. The Langevin thermostat requires a pseudo-random number generator (PRNG) to generate the stochastic force applied to each particle or pair of neighboring particles during each time step in the integration of Newton's equations of motion. In a Single-Instruction-Multiple-Thread (SIMT) GPU parallel computing environment, small batches of random numbers must be generated over thousands of threads and millions of kernel calls. In this communication we introduce a one-PRNG-per-kernel-call-per-thread scheme, in which a micro-stream of pseudorandom numbers is generated in each thread and kernel call. These high quality, statistically robust micro-streams require no global memory for state storage, are more computationally efficient than other PRNG schemes in memory-bound kernels, and uniquely enable the DPD simulation method without requiring communication between threads.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Systematic parameter inference in stochastic mesoscopic modeling

We propose a method to efficiently determine the optimal coarse-grained force field in mesoscopic stochastic simulations of Newtonian fluid and polymer melt systems modeled by dissipative particle dynamics (DPD) and energy conserving dissipative particle dynamics (eDPD). The response surfaces of various target properties (viscosity, diffusivity, pressure, etc.) with respect to model parameters are constructed based on the generalized polynomial chaos (gPC) expansion using simulation results on sampling points (e.g., individual parameter sets). To alleviate the computational cost to evaluate the target properties, we employ the compressive sensing method to compute the coefficients of the dominant gPC terms given the prior knowledge that the coefficients are “sparse”. The proposed method shows comparable accuracy with the standard probabilistic collocation method (PCM) while it imposes a much weaker restriction on the number of the simulation samples especially for systems with high dimensional parametric space. Fully access to the response surfaces within the confidence range enables us to infer the optimal force parameters given the desirable values of target properties at the macroscopic scale. Moreover, it enables us to investigate the intrinsic relationship between the model parameters, identify possible degeneracies in the parameter space, and optimize the model by eliminating model redundancies. The proposed method provides an efficient alternative approach for constructing mesoscopic models by inferring model parameters to recover target properties of the physics systems (e.g., from experimental measurements), where those force field parameters and formulation cannot be derived from the microscopic level in a straight forward way.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗