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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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At least 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Permutationally Invariant Networks for Enhanced Sampling (PINES): Discovery of Multimolecular and Solvent-Inclusive Collective Variables

The typically rugged nature of molecular free energy landscapes can frustrate efficient sampling of the thermodynamically relevant phase space due to the presence of high free energy barriers. Enhanced sampling techniques can improve phase space exploration by accelerating sampling along particular collective variables (CVs). A number of techniques exist for data-driven discovery of CVs parameterizing the important large scale motions of the system. A challenge to CV discovery is learning CVs invariant to symmetries of the molecular system, frequently rigid translation, rigid rotation, and permutational relabeling of identical particles. Of these, permutational invariance has proved a persistent challenge in frustrating the data-driven discovery of multi-molecular CVs in systems of self-assembling particles and solvent-inclusive CVs for solvated systems. In this work, we integrate Permutation Invariant Vector (PIV) featurizations with autoencoding neural networks to learn nonlinear CVs invariant to translation, rotation, and permutation, and perform interleaved rounds of CV discovery and enhanced sampling to iteratively expand sampling of configurational phase space and obtain converged CVs and free energy landscapes. Here, we demonstrate the Permutationally Invariant Network for Enhanced Sampling (PINES) approach in applications to the self-assembly of a 13-atom Argon cluster, association/dissociation of a NaCl ion pair in water, and hydrophobic collapse of a C 45 H 92 n-pentatetracontane polymer chain. We make the approach freely available as a new module within the PLUMED2 enhanced sampling libraries.

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

Spatially Resolved Top-Down Proteomics of Tissue Sections Based on a Microfluidic Nanodroplet Sample Preparation Platform

Conventional proteomics measures the averaged signal from mixed cell populations or bulk tissues, leading to the dilution of significant changes in subpopulations of cells that might serve as important biomarkers. Recent developments in bottom-up proteomics have enabled spatial mapping of cellular heterogeneity in tissue microenvironments. However, bottom-up proteomics cannot precisely infer the abundance changes of intact proteins, which are presented as proteoforms. Herein, we described a spatially resolved top-down proteomics (TDP) platform for proteoform identification and quantification directly on thin tissue sections. The spatial TDP platform consisted of a nanoPOTS (nanodroplet Processing in One pot for Trace Samples)-based sample preparation system and an LCM (laser capture microdissection)-based cell isolation system. We improved the nanoPOTS sample preparation by adding benzonase in the extraction buffer to enhance the coverage of nucleus proteins. Using ~200 cultured cells as model samples, the improved approach increased proteoform identifications from 493 to 700; newly identified proteoforms primarily corresponded to nuclear proteins. To demonstrate the spatial TDP platform in tissue samples, we analyzed LCM-isolated tissue voxels from rat brain cortex and hypothalamus regions. We quantified 426 proteoforms by combining identifications from TopPIC and TDPortal with the quantitation from ProMex. Several proteoforms corresponding to the same gene exhibited mixed abundance profiles between two tissue regions, suggesting potential PTM-specific spatial distributions. The spatial TDP workflow has prospects for biomarker discovery at proteoform level from small tissue sections.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Comparative Analysis of Corrinoid Profiles across Host-Associated and Environmental Samples

Vitamin B 12 (the cyanated form of cobalamin cofactors) is best known for its essential role in human health. In addition to its function in human metabolism, cobalamin also plays important roles in microbial metabolism and can impact microbial community function. Cobalamin is a member of the structurally diverse family of cofactors known as cobamides that are produced exclusively by certain prokaryotes. Cobamides are considered shared nutrients in microbial communities because the majority of bacteria that possess cobamide-dependent enzymes cannot synthesize cobamides de novo. Furthermore, different microbes have evolved metabolic specificity for particular cobamides, and therefore, the availability of cobamides in the environment is important for cobamide-dependent microbes. Determining the cobamides present in an environment of interest is essential for understanding microbial metabolic interactions. By examining the abundances of different cobamides in diverse environments, including 10 obtained in this study, we find that, contrary to its preeminence in human metabolism, cobalamin is relatively rare in many microbial habitats. Comparison of cobamide profiles of mammalian gastrointestinal samples and wood-feeding insects reveals that host-associated cobamide abundances vary and that fecal cobamide profiles differ from those of their host gastrointestinal tracts. Environmental cobamide profiles obtained from aquatic, soil, and contaminated groundwater samples reveal that the cobamide compositions of environmental samples are highly variable. As the only commercially available cobamide, cobalamin is routinely supplied during microbial culturing efforts. Furthermore, these findings suggest that cobamides specific to a given microbiome may yield greater insight into nutrient utilization and physiological processes that occur in these habitats.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Decadal-Scale Hydrothermal Alteration of Basalt: Implications for Long-Term Reactivity in Subsurface Technologies

Global demands to secure critical mineral supply chains and managing water resources require innovative resource management strategies. Subsurface basalt reservoirs offer a dual-purpose application, where CO2 enhanced critical metal recovery can be coupled with water remediation strategies for the extraction of economically important and environmentally relevant elements. This study utilizes static batch experiments conducted on Columbia River Basalt Group samples to evaluate long-term fluid–rock interactions relevant to coupled subsurface applications. Samples were reacted under elevated temperature and pressure conditions, for 296 and 4,061 days, providing a unique dataset to assess geochemical evolution, element partitioning, and aqueous chemistry over decadal timescales. The alteration was dominated by silicate, aluminosilicate, and oxide phases. Progressive hydrothermal alteration increased the cation exchange capacity through the formation of secondary minerals (e.g. clays and zeolites), influencing solute retention, ion exchange behavior, and aqueous chemistry. Grain-scale and bulk analyses revealed selective cation release and retention patterns, establishing a mechanistic baseline for predicting element mobility. These geochemical constraints inform the prospective use of CO2-enhanced metal recovery for critical resources (e.g. Co, Ni, Mn, Mg, Ti, Fe) and associated water-quality impacts, providing a foundation for the design and management of engineered fluid–rock systems in basalt reservoirs.

Gibson, Marie N.↗
DOE OSTI2021

Monte Carlo Analysis of the Performance of the ITER Diagnostic Residual Gas Analyzer

The composition of exhausted gas is a key parameter in long-pulse plasma fusion experiments, and its evolution shall be monitored at timescales relevant to plasma dynamics and plasma-wall interactions. A diagnostic residual gas analyzer (DRGA) is a multisensor instrument particularly suited to these studies, and ITER will adopt DRGAs in the equatorial and in the divertor tokamak regions. In this work, we have revisited the design of the ITER divertor DRGA through simple vacuum analytical considerations supported by simulations conducted with Molflow+, a test particle Monte Carlo (TPMC) simulation code commonly used in the particle accelerator community. Here, starting with recommendations on the manufacturing of the vacuum piping of the DRGA, this work is followed by a complete vacuum characterization of the diagnostic vacuum setup (pressure profiles at base pressure and during sampling, orifice diameter, and length optimization), and finally, the in-vessel residence time of the most important gas species is simulated. These studies have allowed us to give insights into some experimental results recently found on the prototype DRGA installed in the Wendelstein W7-X stellarator.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

unWISE tomography of Planck CMB lensing

CMB lensing tomography, or the cross-correlation between CMB lensing maps and large-scale structure tracers over a well-defined redshift range, has the potential to map the amplitude and growth of structure over cosmic time, provide some of the most stringent tests of gravity, and break important degeneracies between cosmological parameters. In this work, we use the unWISE galaxy catalog to provide three samples at median redshifts z ~ 0.6, 1.1 and 1.5, fully spanning the Dark Energy dominated era, together with the most recent Planck CMB lensing maps. We obtain a combined cross-correlation significance S/N = 79.3 over the range of scales 100 < ℓ < 1000. We measure the redshift distribution of unWISE sources by a combination of cross-matching with the COSMOS photometric catalog and cross-correlation with BOSS galaxies and quasars and eBOSS quasars. We also show that magnification bias must be included in our analysis and perform a number of null tests. In a companion paper, we explore the derived cosmological parameters by modeling the non-linearities and propagating the redshift distribution uncertainties.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Enhanced magnetostriction through dilute Ce doping of Fe-Ga

Doping of magnetostrictive galfenol (Fe 82 Ga 18 , in at.%) with rare-earth elements significantly enhances magnetostriction, with the largest gains achieved in textured melt-spun ribbons. Here, it is demonstrated that even extremely dilute Ce, as little as 65 ppm, can double the magnetostrictive response of galfenol when coupled with an appropriate heat treatment. This improvement is correlated with a compression of the host lattice, both of which reach their maximum extent at the calculated solubility limit of Ce in in body-centered cubic (bcc) galfenol, ~ 50 ppm. Beyond this point, excess Ce segregates into CeGa 2 , which forms an interdendritic network throughout the sample at high Ce levels and cannot be resolutionized through heat treatments. These findings point to the importance of solubility limits (i.e., equilibrium thermodynamics) in determining appropriate doping levels or heat treatment couples to optimize magnetostrictive performance, confirming that overdoping is actively detrimental to both material properties and cost.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Automated Identification of Characteristic Droplet Size Distributions in Stratocumulus Clouds Utilizing a Data Clustering Algorithm

Abstract Droplet-level interactions in clouds are often parameterized by a modified gamma fitted to a “global” droplet size distribution. Do “local” droplet size distributions of relevance to microphysical processes look like these average distributions? This paper describes an algorithm to search and classify characteristic size distributions within a cloud. The approach combines hypothesis testing, specifically, the Kolmogorov–Smirnov (KS) test, and a widely used class of machine learning algorithms for identifying clusters of samples with similar properties: density-based spatial clustering of applications with noise (DBSCAN) is used as the specific example for illustration. The two-sample KS test does not presume any specific distribution, is parameter free, and avoids biases from binning. Importantly, the number of clusters is not an input parameter of the DBSCAN-type algorithms but is independently determined in an unsupervised fashion. As implemented, it works on an abstract space from the KS test results, and hence spatial correlation is not required for a cluster. The method is explored using data obtained from the Holographic Detector for Clouds (HOLODEC) deployed during the Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) field campaign. The algorithm identifies evidence of the existence of clusters of nearly identical local size distributions. It is found that cloud segments have as few as one and as many as seven characteristic size distributions. To validate the algorithm’s robustness, it is tested on a synthetic dataset and successfully identifies the predefined distributions at plausible noise levels. The algorithm is general and is expected to be useful in other applications, such as remote sensing of cloud and rain properties. Significance Statement A typical cloud can have billions of drops spread over tens or hundreds of kilometers in space. Keeping track of the sizes, positions, and interactions of all of these droplets is impractical, and, as such, information about the relative abundance of large and small drops is typically quantified with a “size distribution.” Droplets in a cloud interact locally, however, so this work is motivated by the question of whether the cloud droplet size distribution is different in different parts of a cloud. A new method, based on hypothesis testing and machine learning, determines how many different size distributions are contained in a given cloud. This is important because the size distribution describes processes such as cloud droplet growth and light transmission through clouds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

High-resolution analysis of clay minerals and amorphous materials in martian analog environments

Mudrocks and mud-sized sediments (i.e., silt to clay) dominate the surface of Earth and Mars. These fine-grained sediments preserve a rich history of sedimentary processes from source to sink and shed light on ancient climates. However, both the physical and chemical nature of these materials make them difficult to fully characterize with traditional laboratory techniques. Here, we explore a cross-disciplinary and high-resolution approach using synchrotron radiation for X-ray diffraction, pair distribution function analysis, and submicrometer-scale X-ray fluorescence, combined with transmission electron microscopy, to better understand the nanostructure and composition of mud-sized sediments from a glacio-fluvial watershed in southwest Iceland. Our results demonstrate that sediments in the cold and wet climate of Iceland are more altered than previously thought, as evidenced by the identification of kaolinite and mixed-layer kaolinite-smectite. Additionally, sediments are enriched in amorphous materials and nanocrystalline phases, as determined from grain morphologies and compositions consistent with allophane, hisingerite, ferrihydrite, and halloysite. These alteration products are present as intimate mixtures that vary across depositional sites, demonstrating the dynamic nature of the secondary assemblage from source to sink. This work has implications for Mars, where, for example, basalt-sourced sedimentary rocks from Gale crater are abundant in clay minerals and amorphous materials. Finally, this work underpins the importance of using high-resolution techniques, a coordinated methodology, and developing innovative approaches for future planetary sample return missions (e.g., Mars sample return).

36 MATERIALS SCIENCE↗
DOE OSTI2021

Strength models for stainless steel 304L alloys

This report provides nominal, weak and strong calibrations of Preston- Tonks-Wallace (PTW) plasticity model [1] parameters for stainless steel 304L alloys. The data sets shown indicate that for these alloys, the material’s prove- nance plays an important role in accurate characterization. First we provide a parameter set fit to compression-tested annealed samples, both quasistatic (QS) and split Hopkinson pressure bar (SHPB), from the MST-8 group at LANL within the last year. Then we compare simulations using the FLAG hydrocode [2], with this and other parameter sets, to data from a Taylor cylinder impact test performed within the same group around 2013.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Artificial Intelligence and Machine Learning Support for Probabilistic Fracture Mechanics

In this research, artificial intelligence and machine learning (ML) methods are used to search an uncertain parameter space more efficiently for the most important inputs with respect to response sensitivities. These methods are applied to the Extremely Low Probability of Rupture (xLPR) probabilistic fracture mechanics code used at the U.S. Nuclear Regulatory Commission (NRC) in support of nuclear regulatory research. This report documents two separate but related sub-tasks: (1) ranking important uncertain input features with respect to target outputs, determined by convergence in confidence intervals for increasing sample sizes using simple random sampling; and (2) implementation of a reduced-order surrogate model for fast, approximate sample generation. Unoptimized readily available off-the-shelf ML models were used in both sub-tasks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Effects of Charged Solute-Solvent Interaction on Reservoir Temperature during Subsurface CO2 Injection

A short-term side-effect of CO2 injection is a developing low-pH front that forms ahead of the bulk water injectant, due to differences in solute diffusivity. Observations of downhole well temperature show a reduction in aqueous-phase temperature with the arrival of a low-pH front, followed by a gradual rise in temperature upon the arrival of a high concentration of bicarbonate ion. In this work, we model aqueous-phase transient heat advection and diffusion, with the volumetric energy generation rate computed from solute-solvent interaction using the Helgeson–Kirkham–Flowers (HKF) model, which is based on the Born Solvation model, for computing specific molar heat capacity and the enthalpy of charged electrolytes. A computed injectant water temperature profile is shown to agree with the actual bottom hole sampled temperature acquired from sensors. The modeling of aqueous-phase temperature during subsurface injection simulation is important for the accurate modeling of mineral dissolution and precipitation because forward dissolution rates are governed by a temperature-dependent Arrhenius model.

58 GEOSCIENCES↗
DOE OSTI2026

FY26 Progress Report on the Operation of the Activated Materials Laboratory at the Advanced Photon Source as a Nuclear Science User Facilities Partner Facility

The Activated Materials Laboratory (AML), located in the Long Beamline Building of the Advanced Photon Source (APS) at Argonne National Laboratory (ANL), provides a centralized radiological capability for preparing, handling, and supporting synchrotron experiments on activated materials. As a Nuclear Science User Facilities (NSUF) partner facility, the AML enables the receipt of radioactive shipments, open-form sample handling, encapsulation, transport of specimens to and from APS beamlines, and experimentation with dedicated equipment. The partnership includes the APS 1-ID and 20-ID beamlines, which provide high-energy x-ray scattering, tomography, and diffraction microscopy techniques for ex-situ, in-situ, and grainresolved three-dimensional (3D) characterization. All samples supported through the AML and partner beamlines must meet radiological limits of less than 100 mrem/h at 30 cm. This FY26 progress report summarizes the first full year of AML operations as an NSUF partner facility and highlights progress in both user support and capability development. By the end of FY26, the AML had received a total of 10 NSUF-awarded projects, including 2 Consolidated Innovative Nuclear Research (CINR), 7 Rapid Turnaround Experiment (RTE), and 1 Super RTE projects. Beamtime was fully delivered for 3 RTE projects and partially delivered for 1 CINR project, demonstrating successful workflows for receipt, encapsulation, beamline transfer, and radiological experiment execution. These efforts demonstrated safe radiological experiments at APS beamlines for samples with dose rates above the historical 5 mrem/h threshold and now up to 100 mrem/h at 30 cm, marking an important milestone for neutron-irradiated materials research. During FY26, the AML also expanded its experimental capabilities. A Psylotech xTS load frame with in-grip rotation was deployed for room-temperature mechanical testing with threedimensional x-ray characterization during interrupted loading. A Linkam TS1500V vacuum heater was commissioned for thermally driven studies, and temperature calibration experiments were performed to establish specimen-relevant thermal profiles. In parallel, a customized split-tube furnace for high-temperature mechanical testing entered commissioning, and initial work began on robotic sample handling to reduce worker dose and improve operational efficiency. Data workflow and beamline operations continued to mature. Together, these developments demonstrate that the AML is becoming a unique national resource for safe, efficient, and scientifically advanced characterization of activated materials at the APS.

Zhang, Xuan↗
DOE OSTI2024

A comparison of model validation approaches for echo state networks using climate model replicates

As global temperatures continue to rise, climate mitigation strategies such as stratospheric aerosol injections (SAI) are increasingly discussed, but the downstream effects of these strategies are not well understood. As such, there is interest in developing statistical methods to quantify the evolution of climate variable relationships during the time period surrounding an SAI. Feature importance applied to echo state network (ESN) models has been proposed as a way to understand the effects of SAI using a data-driven model. This approach depends on the ESN fitting the data well. If not, the feature importance may place importance on features that are not representative of the underlying relationships. Typically, time series prediction models such as ESNs are assessed using out-of-sample performance metrics that divide the times series into separate training and testing sets. However, this model assessment approach is geared towards forecasting applications and not scenarios such as the motivating SAI example where the objective is using a data driven model to capture variable relationships. Here, in this paper, we demonstrate a novel use of climate model replicates to investigate the applicability of the commonly used repeated hold-out model assessment approach for the SAI application. Simulations of an SAI are generated using a simplified climate model, and different initialization conditions are used to provide independent training and testing sets containing the same SAI event. The climate model replicates enable out-of-sample measures of model performance, which are compared to the single time series hold-out validation approach. For our case study, it is found that the repeated hold-out sample performance is comparable, but conservative, to the replicate out-of-sample performance when the training set contains enough time after the aerosol injection.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

The impact of sampling scale: A comparison of methods for estimating external contaminant exposure in free-ranging wildlife

The impacts of contaminants on wildlife are dose dependent, and thus being able to track or predict exposure following contamination events is important for monitoring ecosystem health. However, the ability to track exposure in free-ranging wildlife is often severely limited. Consequently, researchers have predominantly relied on simple methods for estimating contaminant exposures in wildlife with little regard for spatial contaminant heterogeneity or an animal’s use of diverse habitats. We evaluated the influence sampling scale (i.e., how finely contaminant distribution and organism’s spatial use of the landscape is mapped) has on (1) realism and (2) conservativeness of exposure estimates. To do this, we monitored the actual exposure of wild boar (Sus scrofa) in Fukushima, Japan to radioactive contamination using GPS27 coupled contaminant monitors placed on individual animals. We compared empirical exposures to estimates generated by combining varying amounts of information about an individual boar’s location and/or movement, with the distribution of contamination on the landscape. Here we found that the most realistic exposure estimates were produced when finer-scale contaminant distribution surveys (e.g., airborne surveys) were combined with more accurate estimates of an individual’s space use (e.g., home ranges or core areas). Importantly, estimates of exposure based on single point surveys at a trap site (a simple method commonly used in the literature), did not correlate with actual exposure rates, suggesting dose-effects studies using this method may result in spurious conclusions. These results suggest that researchers seeking realistic estimates of exposure, such as in dose-effect studies, should ensure they have adequately accounted for fine-scale contaminant distribution patterns and areas of higher use by study organisms. However, conservative estimates of exposure i.e., intentionally over-predicting exposure as is done in initial tiers of ecological risk analyses) were not as scale sensitive and could be achieved with a single known location and coarse contaminant distribution maps.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2024

Spatially Resolved Raman Spectroscopic Investigation of Uranyl Fluoride: A Case Study in the Importance of Instrument Optimization

Raman spectroscopy is an emerging technique for rapid and nondestructive analysis of nuclear materials for forensic and nonproliferation applications as it is a powerful tool for distinguishing multiple chemical forms of materials with similar stoichiometries. Recent developments in spectroscopic software have enabled rapid data collection with high-speed Raman spectroscopic mapping capabilities. However, some uranium-rich materials are susceptible to degradation in humid air and/or laser-induced phase transformations. To mitigate environmental or measurement-related sample degradation of potential samples of interest, we have taken a systematic approach to define optimized data collection parameters for high-throughput measurements of uranyl fluoride (UO 2 F 2 ), which is an important intermediate material in the nuclear fuel cycle. First, we systematically describe the influence of optical magnification (5× to 100×), laser power, and exposure time on obtained signal for identical particles of UO 2 F 2 and find that at low laser power and exposure times, comparable signal is obtained regardless of optical magnification. Second, we ensure sample integrity during data collection, and third, collect spectroscopic maps that employ optimized parameters to reduce the time required to obtain spatially resolved spectroscopic information. Reductions of 90% and 99% in measurement times are discussed as they relate to differences in resolving spectroscopic features of particles in identical mapping areas. Finally, during this work, we found that additional data processing options were needed and thus developed a customized Python script for importing, processing, analyzing, and visualizing Raman spectroscopic map data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

In-situ synchrotron X-ray micro-diffraction investigation of ultra-low-strain deformation microstructure in laminated Ti-Al composites

An ultra-low-strain deformation microstructure was revealed for the first time non-destructively in the bulk interior of an annealed laminated Ti-Al composite-in the "fully recrystallized" Al layer-by a synchrotron-based micro-diffraction technique, namely differential aperture X-ray microscopy (DAXM), through real space mapping with a very high angular resolution (0.01 degrees). This ultra-low-strain deformation microstructure was found to result from the thermal stress, induced during cooling after annealing, due to the different coefficients of thermal expansion for the Ti and Al layers. The annealed sample was further tensile deformed to a strain of 1.66% and followed by in situ DAXM and analyzed by various misorientation mapping methods. The results pointed to the important effects of the initial microstructure and the interface constraint, as well as the grain size and crystal orientation, on the plastic deformation. A gradient in dislocation density from the layer interface to the center was found in the Al layer of the annealed sample, and this gradient increased slightly during tensile deformation. The variation of the dislocation density was further discussed based on the activation and interaction of dislocations in grains of different sizes and orientations during plastic deformation. The findings of this study provided valuable insights in understanding the constraint effect of the laminated metal composite and the design of novel composite materials. (c) 2020 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Crystallographic orientation↗
DOE OSTI2021

Toward Automated Sampling of Polymorph Nucleation and Free Energies with the SGOOP and Metadynamics

Understanding the driving forces behind the nucleation of different polymorphs is of great importance for material sciences and the pharmaceutical industry. This includes understanding the reaction coordinate that governs the nucleation process and correctly calculating the relative free energies of different polymorphs. Here, we demonstrate, for the prototypical case of urea nucleation from the melt, how one can learn such a one-dimensional reaction coordinate as a function of prespecified order parameters and use it to perform efficient biased all-atom molecular dynamics simulations. The reaction coordinate is learnt as a function of the generic thermodynamic and structural order parameters using the “spectral gap optimization of order parameters (SGOOP)” approach [Tiwary, P. and Berne, B. J. Proc. Natl. Acad. Sci. U.S.A. (2016)] and is biased using well-tempered metadynamics simulations. The reaction coordinate gives insights into the role played by different structural and thermodynamics order parameters, and the biased simulations obtain accurate relative free energies for different polymorphs. This includes an accurate prediction of the approximate pressure at which urea undergoes a phase transition and one of the metastable polymorphs becomes the most stable conformation. Here, we believe the ideas demonstrated in this work will facilitate efficient sampling of nucleation in complex, generic systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗