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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

The seventh blind test of crystal structure prediction: structure ranking methods

A seventh blind test of crystal structure prediction has been organized by the Cambridge Crystallographic Data Centre. The results are presented in two parts, with this second part focusing on methods for ranking crystal structures in order of stability. The exercise involved standardized sets of structures seeded from a range of structure generation methods. Participants from 22 groups applied several periodic DFT-D methods, machine learned potentials, force fields derived from empirical data or quantum chemical calculations, and various combinations of the above. In addition, one non-energy-based scoring function was used. Results showed that periodic DFT-D methods overall agreed with experimental data within expected error margins, while one machine learned model, applying system-specific AIMnet potentials, agreed with experiment in many cases demonstrating promise as an efficient alternative to DFT-based methods. For target XXXII, a consensus was reached across periodic DFT methods, with consistently high predicted energies of experimental forms relative to the global minimum (above 4 kJ mol −1 at both low and ambient temperatures) suggesting a more stable polymorph is likely not yet observed. The calculation of free energies at ambient temperatures offered improvement of predictions only in some cases (for targets XXVII and XXXI). Several avenues for future research have been suggested, highlighting the need for greater efficiency considering the vast amounts of resources utilized in many cases.

Chemistry↗
DOE OSTI2016

Hierarchical optimization for neutron scattering problems

We present a scalable optimization method for neutron scattering problems that determines confidence regions of simulation parameters in lattice dynamics models used to fit neutron scattering data for crystalline solids. The method uses physics-based hierarchical dimension reduction in both the computational simulation domain and the parameter space. We demonstrate for silicon that after a few iterations the method converges to parameters values (interatomic force-constants) computed with density functional theory simulations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Stress field and interaction forces between dislocations and precipitate distributions

A computational method is developed for calculation of the stress field and interaction forces between dislocations and precipitates of arbitrary shape and distribution. The internal stress generated by precipitates due to coherency strain is implemented within the discrete dislocation dynamics (DDD) framework. The s-version finite element method (s-FEM), which models a precipitate of arbitrary shape using a local mesh is used to calculate coherency stress fields. The method facilitates meshing precipitate volumes of arbitrary geometry, and multiple local meshes can be superimposed at various positions of the global mesh. Accuracy and convergence conditions of the method are established. For a single precipiate, the method is shown to be 3.5 times faster than a standard FEM calculation for the same accuracy, and the gain in speed increases with the number of precipitates. The CRSS of spherical precipitates is found to be higher than disk-shaped precipitates of the same volume fraction.

42 ENGINEERING↗
DOE OSTI2024

Bridging the time scale in exascale computing of chemical systems (Final Technical Report)

This report summarizes the work carried out with support of the United States Department of Energy under Award DE-SC0019441. The theme of this project was to develop and apply methods that allowed for the acceleration of atomistic calculations, particularly in challenging areas such as multiphase systems, electrified interfaces, uncertainty estimation, and applications requiring chemical accuracy, which tend to be applications where simulation time is severely bottlenecked by the computational time requirements. Much of the focus was on the application of emerging machine-learning methodologies, although a wide range of methodologies were employed. This report has two major sections. The first focuses on the methodological advances themselves. Within this part, we report a number of major advances, a few examples of which are described here. We report the first machine-learning scheme for the acceleration of electronically grand-canonical calculations (that is, those applicable to electrochemistry). We report new methods of performing transfer learning, in which physics-based priors can be used to provide predictions, often with uncertainty estimates, of images well outside of training sets; we also offer ways to fine-tune these transfer-learning models. We provide a new systematic means to generate and apply minimal training data sets to very large (10,000’s of atoms) systems, with only small training sets appropriate for electronic structure. We developed new methodologies to integrate surface vibrations into surface adsorption calculations. We made advances to the applicability of diffusion Monte Carlo methods to allow (learned) force prediction, finite-size error correction, and force-free means of searching for transition states. We integrated machine-learned atomistic predictions into mechanism generation codes. Additionally, we released new software including AmpTorch, a modernized version of our original atomistic machine-learning code Amp. The second part of this report focuses on the scientific applications that accompanied, and were often enabled by, the methodological advances described earlier. A few examples follow, but full details are in the individual chapters of the report. For example, we developed a general theory of phonon-induced friction on molecular adsorbates. We showed fundamentally how solvent influences the adsorption and desorption process and how it differs from the processes typically involved at the solid–gas interface, making aqueous-phase and electrocatalysis different from traditional thermocatalysis. We examined how metal–insulator and magnetic transitions can be probed, and accelerated exciton dynamics via Frenkel Hamiltonian parameters. We showed that the nearsighted force-training approach, developed within this project, can predict both the stability and reactivity of large nanoparticles, and can also lead to insights on catalyst coverage on binding energies and entropies. These applied studies, which generally integrated with our method development, allowed us to push forward the theoretical understanding of several reaction classes.

08 HYDROGEN↗
DOE OSTI2019

Critical wind speeds suggest wind could be an important disturbance agent in Amazonian forests

Abstract Recent research in the central Amazon suggests that wind is a major agent of disturbance, however, a mechanistic understanding of how wind may lead to tree mortality in Amazonian forests remains unclear. Here we estimated wind speeds necessary to topple central Amazon trees by linking both static and dynamic versions of two wind speed estimation methods (four methods total) to field data on tree failure derived from a static winching study. Static versions of these methods assumed invariant wind characteristics as more trees failed, while dynamic versions updated tree spacing, leaf area index and wind profiles progressively after each tree failure. First, we used a profile method which estimates wind force on individual trees by segments. We calculated drag on each segment and converted drag into basal turning moment, and compared the summed turning moments to the critical turning moment measured in the winching study. Estimated critical wind speeds from the static profile method varied greatly, from 10.75 m s−1 to >120.0 m s−1 with a mean of 45.70 m s−1. Critical wind speeds estimated with static approaches decreased with tree size but were not significantly different between two focal genera. Primary drivers of variation in critical wind speed were tree height and crown size. Second, we used the turning moment coefficient method of Hale, S.E., Gardiner, B., Peace, A., Nicoll, B., Taylor, P. and Pizzirani, S. 2015 Comparison and validation of three versions of a forest wind risk model. Environ. Model. Softw.68, 27–41. doi:10.1016/j.envsoft.2015.01.016.; the static version of this method yielded less-variable estimates, ranging from 18.98 to 52.01 m s−1, with a mean of 30.88 m s−1. Notably, the two static methods for estimating critical wind speeds differed in the trees they identified as having the highest and lowest critical wind speeds. Dynamic variants of the above two methods produced greatly reduced ranges in CWS estimates for our study trees, because after the early tree failures, remaining trees were subject to greater wind penetration into the stand and thus greater loading for a given above-canopy wind speed. CWS estimated with dynamic approaches differed significantly between the focal taxa. Nevertheless, both estimates suggest that wind speeds commonly observed during Amazon storms are sufficient to produce widespread tree damage and mortality.

Peterson, Chris J.↗
DOE OSTI2023

Machine learning nonequilibrium electron forces for spin dynamics of itinerant magnets

Abstract We present a generalized potential theory for conservative as well as nonconservative forces for the Landau-Lifshitz magnetization dynamics. Importantly, this formulation makes possible an elegant generalization of the Behler-Parrinello machine learning (ML) approach, which is a cornerstone of ML-based quantum molecular dynamics methods, to the modeling of force fields in adiabatic spin dynamics of out-of-equilibrium itinerant magnetic systems. We demonstrate our approach by developing a deep-learning neural network that successfully learns the electron-mediated exchange fields in a driven s-d model computed from the nonequilibrium Green’s function method. We show that dynamical simulations with forces predicted from the neural network accurately reproduce the voltage-driven domain-wall propagation. Our work also lays the foundation for ML modeling of spin transfer torques and opens a avenue for ML-based multi-scale modeling of nonequilibrium dynamical phenomena in itinerant magnets and spintronics.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Force field for calcium sulfate minerals to predict structural, hydration, and interfacial properties

Calcium sulfates such as anhydrite, hemihydrate, and gypsum find widespread use in building materials, implants, and tissue healing. We introduce a simple and compatible atomistic force field for all calcium sulfate phases that reproduces a wide range of experimental data including lattice parameters, surface, hydration, mechanical, and thermal properties in 1% to 5% accuracy relative to experiments. The performance is several times better than prior force fields and DFT methods, which lead to errors in structures and energies up to 100%. We explain (hkl) cleavage energies, the dynamics of (hkl) water interfaces, and new insights into molecular origins of crystal-facet specific hydration and solubility. Impressive agreement of computed and experimentally measured hydration energies is shown. The models add to the Interface force field (IFF) and are compatible with multiple force fields (CHARMM, AMBER, GROMOS, CVFF, PCFF, OPLS-AA) for property predictions of sulfate-containing materials from atoms to the large nanometer scale.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Modeling and simulation of earthquake soil structure interaction excited by inclined seismic waves

Presented is an application of wave potential formulation (WPF) together with domain reduction method (DRM) to modeling earthquake soil structure interaction (ESSI) behavior in horizontally layered ground under inclined incident seismic waves. Wave potential formulation is used to develop a spatially varying, inclined seismic wave field from incident Primary (P) and Secondary (S) waves that propagate through layered ground. Developed seismic wave field is then used to develop effective forces for Domain Reduction Method that are then used for analyzing ESSI response of a soil structure system. Developed methodology, called WPF-DRM, is verified using analytic solution for a free field response of layered ground subjected to inclined incident waves. Developed WPF-DRM methodology is illustrated through analysis of an ESSI response of a deeply embedded structure, a small modular reactor (SMR) subjected to incident S wave polarized in vertical plane (SV) with variation in inclinations and frequencies. Presented example highlights the influences of incident wave inclination and frequency on ESSI response of analyzed SMR.

58 GEOSCIENCES↗
DOE OSTI2016

Communication: Charge-population based dispersion interactions for molecules and materials

We introduce a system-independent method to derive effective atomic C{sub 6} coefficients and polarizabilities in molecules and materials purely from charge population analysis. This enables the use of dispersion-correction schemes in electronic structure calculations without recourse to electron-density partitioning schemes and expands their applicability to semi-empirical methods and tight-binding Hamiltonians. We show that the accuracy of our method is en par with established electron-density partitioning based approaches in describing intermolecular C{sub 6} coefficients as well as dispersion energies of weakly bound molecular dimers, organic crystals, and supramolecular complexes. We showcase the utility of our approach by incorporation of the recently developed many-body dispersion method [Tkatchenko et al., Phys. Rev. Lett. 108, 236402 (2012)] into the semi-empirical density functional tight-binding method and propose the latter as a viable technique to study hybrid organic-inorganic interfaces.

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

Toward Fast and Reliable Potential Energy Surfaces for Metallic Pt Clusters by Hierarchical Delta Neural Networks

Data-driven machine learning force fields (MLF) are more and more popular in atomistic simulations, and exploit machine learning methods to predict energies and forces for unknown structures based on the knowledge learned from an existing reference database. The latter usually comes from density functional theory calculations. One main drawback of MLFs is that physical laws are not incorporated in the machine learning models and instead, MLFs are designed to be very flexible to simulate complex quantum chemistry potential energy surface (PES). In general, MLFs have poor transferability, and hence a very large trainset is required to span all the target feature space to get a reliable MLF. This procedure becomes more troublesome when the PES is complicated, with a large number of degrees of freedom, in which building a large database is inevitable and very expensive, especially when accurate but costly exchange-correlation functionals have to be used. In this manuscript, we exploit a high dimensional neural network potential (HDNNP) on Pt clusters of size 6 to 20 as one example. Furthermore, our standard level of energy calculation is DFT GGA (PBE) using a plane wave basis set. We introduce an approximate but fast level with the PBE functional and a minimal atomic orbital basis set, then a more accurate but expensive level, using a hybrid functional or non-local vdw functional and a plane wave basis set, is reliably predicted by learning the difference with HDNNP. Findings show that such a differential approach (named ΔHDNNP) can deliver very accurate predictions (error < 10 meV/atom) in reference to converged basis set energies as well as more accurate but expensive xc functional. The overall speedup can be as large as 900 for 20 atom Pt cluster. More importantly, ΔHDNNP shows much better transferability due to the intrinsic smoothness of delta potential energy surface, and accordingly one can use much smaller trainset data to obtain better accuracy than the conventional HDNNP. A multi-layer ΔHDNNP is thus proposed to obtain very accurate predictions versus expensive non-local vdW functional calculations in which the required trainset is further reduced. The approach can be easily generalized to any other machine learning methods and opens a path to study the structure and dynamics of Pt clusters and nanoparticles.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Development of a precision reverse offset printing system

In printed electronics technology, the overlay accuracy of printed patterns is a very important issue when applying printing technology to the production of electric devices. In order to achieve accurate positioning of the printed patterns, this study proposes a novel precision reverse offset printing system. Furthermore, the study evaluates the effects of synchronization and printing force on position errors of the printed patterns, and presents methods of controlling synchronization and printing force so as to eliminate positional errors caused by the above-mentioned reasons. Finally, the printing position repeatability of 0.40 μm and 0.32 μm (x and y direction, respectively) at a sigma level is obtained over the dimension of 100 mm under repeated printing tests with identical printing conditions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Exactness of the normal-ordered two-body truncation of three-nucleon forces

Reference-state-based many-body methods start from Hamiltonians that are normal ordered with respect to the reference state. In low-energy nuclear physics applications, normal-ordered Hamiltonians consisting of two- and three-nucleon forces are usually truncated at the two-body rank with residual three-nucleon operators being discarded. Benchmark computations have shown that this truncation is accurate, but we lack an understanding about why it works. Here, we show that the normal-ordered two-body truncation is exact for zero-range three-body forces when nuclei are computed using the coupled cluster with singles and doubles method. As the nuclear three-nucleon force is short ranged and a three-body contact is a leading term in effective field theories of quantum chromodynamics, our result provides an analytical basis for the popular normal-ordered two-body approximation.

Rothman, Maxwell [Univ. of Tennessee, Knoxville, T↗
DOE OSTI2022

Active learning of reactive Bayesian force fields applied to heterogeneous catalysis dynamics of H/Pt

Abstract Atomistic modeling of chemically reactive systems has so far relied on either expensive ab initio methods or bond-order force fields requiring arduous parametrization. Here, we describe a Bayesian active learning framework for autonomous “on-the-fly” training of fast and accurate reactive many-body force fields during molecular dynamics simulations. At each time-step, predictive uncertainties of a sparse Gaussian process are evaluated to automatically determine whether additional ab initio training data are needed. We introduce a general method for mapping trained kernel models onto equivalent polynomial models whose prediction cost is much lower and independent of the training set size. As a demonstration, we perform direct two-phase simulations of heterogeneous H 2 turnover on the Pt(111) catalyst surface at chemical accuracy. The model trains itself in three days and performs at twice the speed of a ReaxFF model, while maintaining much higher fidelity to DFT and excellent agreement with experiment.

42 ENGINEERING↗
DOE OSTI2022

Trends in Siting of Metals in Heterometallic Nd–Yb Metal–Organic Frameworks and Molecular Crystals

Several studies suggest that metal ordering within metal–organic frameworks (MOFs) is important for understanding how MOFs behave in relevant applications; however, these siting trends can be difficult to determine experimentally. To garner insight into the energetic driving forces that may lead to nonrandom ordering within heterometallic MOFs, we employ density functional theory (DFT) calculations on several bimetallic metal–organic crystals composed of Nd and Yb metal atoms. We also investigate the metal siting trends for a newly synthesized MOF. Our DFT-based energy of mixing results suggest that Nd will likely occupy sites with greater access to electronegative atoms and that local homometallic domains within a mixed-metal Nd–Yb system are favored. We also explore the use of less computationally extensive methods such as classical force fields and cluster expansion models to understand their feasibility for large system sizes. Here, this study highlights the impact of metal ordering on the energetic stability of heterometallic MOFs and crystal structures.

energy of mixing↗
DOE OSTI2021

Collective Analysis of Alpha Particle Losses Due to Self-Absorption by Mass Loading on Radioactive Particulate Glass Fiber Filters

In this study, we derived a relationship between filter mass loading and the percent loss during analysis using the mass loading data collected from six previous studies of self-absorption. Components of mass loading include particulate dust, radioactive particulates, and filter material. In a research report published in 1984, Higby calculated a minimum burial depth for an alpha particle to be lost due to absorption (100% loss) of about 3.7 mg/cm 2 based on calculations for the range of 239-Pu alpha particles in glass fiber filters. From there, Higby concluded that a correction factor of 0.85 assumes approximately 15% losses in the count rate of both alpha and beta particles. In 2000, Luetzelschwab et al. recommended assuming a 40% loss at a loading of 3.3 mg/cm 2 and a 28% loss for a loading of 2.3 mg/cm 2 which included the frontal face mass of the filter. More recently, the 100% losses due to absorption were reported to be in the 10 mg/cm 2 range. Presented here is a trinomial relationship method of relating percent loss due to self-absorption to filter mass loading, based on data reported by Higby, Luetzelschwab et al., Huang et al., Barnett et al., Smith et al., and Hogue et al. Under normal operating conditions at the stacks monitored by Effluent Management, the mass loading of sample filters averages 0.09 ± 0.12 (2s) mg/cm 2 (excluding negative values and outliers) and ranges from 0 mg/cm 2 to 0.24 mg/cm 2 . Based on current mass loading results for Effluent Management stack sample filters, the forced-zero trinomial relationship method estimated self-absorption losses of less than 5%. Because American National Standards Institute/Health Physics Society N13.1-2011 guidelines indicate a correction factor should be used when the penetration of radioactive material into the collection media or self-absorption of radiation by the material collected would reduce the count rate by more than 5%, it is possible continued application of a correction factor to the Effluent Management stack samples is no longer necessary. Nevertheless, continuing to assign a correction factor at the 5% threshold (i.e., 0.95) would be a conservative approach.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Measurement of ablation threshold of oxide-film-coated aluminium nanoparticles irradiated by femtosecond laser pulses

We report the results of experiments on estimation of femtosecond laser threshold intensity at which nanoparticles are removed from the substrate surface. The studies are performed with nanoparticles obtained by femtosecond laser ablation of pure aluminium in distilled water. The attenuation (or extinction, i.e. absorption and scattering) spectra of nanoparticles are measured at room temperature in the UV and optical wavelength ranges. The size of nanoparticles is determined using atomic force microscopy. A new method of scanning photoluminescence is proposed to evaluate the threshold of nanoparticle removal from the surface of a glass substrate exposed to IR femtosecond laser pulses with intensities 10{sup 11} – 10{sup 13} W cm{sup -2}. (interaction of laser radiation with matter)

36 MATERIALS SCIENCE↗
DOE OSTI2016

p{sup +}-doping analysis of laser fired contacts for silicon solar cells by Kelvin probe force microscopy

Local rear contacts for silicon passivated emitter and rear contact solar cells can be established by point-wise treating an Al layer with laser radiation and thereby establishing an electrical contact between Al and Si bulk through the dielectric passivation layer. In this laser fired contacts (LFC) process, Al can establish a few μm thick p{sup +}-doped Si region below the metal/Si interface and forms in this way a local back surface field which reduces carrier recombination at the contacts. In this work, the applicability of Kelvin probe force microscopy (KPFM) to the investigation of LFCs considering the p{sup +}-doping distribution is demonstrated. The method is based on atomic force microscopy and enables the evaluation of the lateral 2D Fermi-level characteristics at sub-micrometer resolution. The distribution of the electrical potential and therefore the local hole concentration in and around the laser fired region can be measured. KPFM is performed on mechanically polished cross-sections of p{sup +}-doped Si regions formed by the LFC process. The sample preparation is of great importance because the KPFM signal is very surface sensitive. Furthermore, the measurement is responsive to sample illumination and the height of the applied voltage between tip and sample. With other measurement techniques like micro-Raman spectroscopy, electrochemical capacitance-voltage, and energy dispersive X-ray analysis, a high local hole concentration in the range of 10{sup 19 }cm{sup −3} is demonstrated in the laser fired region. This provides, in combination with the high spatial resolution of the doping distribution measured by KPFM, a promising approach for microscopic understanding and further optimization of the LFC process.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗