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At least 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Molecular tail chemistry controls thermal transport in fullerene films

Herein we report on the thermal conductivities of alkyl- and indene-group functionalized fullerene derivative thin films as measured via time domain thermoreflectance and steady state thermoreflectance. The thermal conductivities vary from 0.064 ± 0.007 W m – 1 K – 1 for [6,6]-phenyl C 61 -butyric acid methyl ester (PCBM) to 0.15 ± 0.017 W m – 1 K – 1 for indene- C 60 bisadduct at room temperature and do not exhibit significant temperature dependence from 300 to 375 K. In comparison to the thermal conductivity of PCBM, increasing the length of the alkyl chain, as in the case of [6,6]-phenyl C 61 -butyric acid butyl ester, and [6,6]-phenyl C 61 -butyric acid octyl ester leads to higher thermal conductivities. Likewise, increasing the number of alkyl chains attached to the fullerenes as in the case of bisadduct PCBM leads to a higher thermal conductivity compared to that of PCBM. We present atomistic insights into the role of chemical functionalization on the overall heat transfer in these fullerene derivatives by conducting molecular dynamics simulations and lattice dynamics calculations. The thermal conductivities predicted via our atomistic simulations qualitatively agree with the experimental trends for our fullerene derivatives. Lattice dynamics calculations reveal that one of the main factors dictating the ultralow thermal conductivities in fullerene derivatives is the large reduction in modal diffusivities in the molecular crystals as calculated from the Allen-Feldman model, thus providing an explanation for their largely reduced thermal conductivities as compared to that of C 60 crystals. The low diffusivities result from high degrees of localization of Einstein-like vibrations in fullerene derivatives due to the molecular side chains, providing the ability to dial-in the properties of these low thermal conductivity solids via molecular engineering.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

The Single Residue K12 Governs the Exceptional Voltage Sensitivity of Mitochondrial Voltage-Dependent Anion Channel Gating

The voltage-dependent anion channel (VDAC) is a β-barrel channel of the mitochondrial outer membrane (MOM) that passively transports ions, metabolites, polypeptides, and single-stranded DNA. VDAC responds to a transmembrane potential by “gating,” i.e. transitioning to one of a variety of low-conducting states of unknown structure. The gated state results in nearly complete suppression of multivalent mitochondrial metabolite (such as ATP and ADP) transport, while enhancing calcium transport. Voltage gating is a universal property of β-barrel channels, but VDAC gating is anomalously sensitive to transmembrane potential. Here, we show that a single residue in the pore interior, K12, is responsible for most of VDAC’s voltage sensitivity. Using the analysis of over 40 μs of atomistic molecular dynamics (MD) simulations, we explore correlations between motions of charged residues inside the VDAC pore and geometric deformations of the β-barrel. Residue K12 is bistable; its motions between two widely separated positions along the pore axis enhance the fluctuations of the β-barrel and augment the likelihood of gating. Single channel electrophysiology of various K12 mutants reveals a dramatic reduction of the voltage-induced gating transitions. The crystal structure of the K12E mutant at a resolution of 2.6 Å indicates a similar architecture of the K12E mutant to the wild type; however, 60 μs of atomistic MD simulations using the K12E mutant show restricted motion of residue 12, due to enhanced connectivity with neighboring residues, and diminished amplitude of barrel motions. We conclude that β-barrel fluctuations, governed particularly by residue K12, drive VDAC gating transitions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Model-free estimation of completeness, uncertainties, and outliers in atomistic machine learning using information theory

Abstract An accurate description of information is relevant for a range of problems in atomistic machine learning (ML), such as crafting training sets, performing uncertainty quantification (UQ), or extracting physical insights from large datasets. However, atomistic ML often relies on unsupervised learning or model predictions to analyze information contents from simulation or training data. Here, we introduce a theoretical framework that provides a rigorous, model-free tool to quantify information contents in atomistic simulations. We demonstrate that the information entropy of a distribution of atom-centered environments explains known heuristics in ML potential developments, from training set sizes to dataset optimality. Using this tool, we propose a model-free UQ method that reliably predicts epistemic uncertainty and detects out-of-distribution samples, including rare events in systems such as nucleation. This method provides a general tool for data-driven atomistic modeling and combines efforts in ML, simulations, and physical explainability.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Unraveling fundamental mechanisms of silicon nitride crystallization in microelectronics manufacturing

This research project investigates the fundamental mechanisms of silicon nitride (SiN) crystallization, aiming to enhance the understanding of this critical material in microelectronics manufacturing. Through a collaborative effort between Sandia National Laboratories, the University of Tennessee, and the University of Florida, we developed a comprehensive framework that integrates experimental techniques, atomistic modeling, meso-scale simulations, and an integrated multi-scale model to capture this physical phenomenon on multiple time and length scales . The project developed a new machine learning based atomistic potential and utilized advanced phase field modeling to capture the complexities of polycrystalline growth and the influence of mechanical stresses on crystallization dynamics. By employing a grain tracker algorithm, the meso-scale model effectively identified and tracked individual crystal grains, enabling the simulation of anisotropic growth behaviors reflective of SiN’s physical properties. The integration of atomistic simulations with meso-scale modeling created a powerful multi-scale framework that validated atomistic inputs and enhanced predictive accuracy for crystallization dynamics at larger scales, validated experimentally. This adaptable modeling capability not only accelerates development times by informing manufacturing processes but also serves as a valuable starting point for understanding crystallization physics in similar materials. The insights gained from this research unlock new opportunities for the development of advanced materials tailored for future microelectronics and photonics applications. Overall, this project represents a significant advancement in understanding of fundamental physics of SiN and establishes a foundation for future research in material science, bridging the gap between atomic-level phenomena and macroscopic material behaviors for practical applications.

36 MATERIALS SCIENCE↗
DOE OSTI2023

CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling

Large-scale simulations with complex electron interactions remain one of the greatest challenges for atomistic modelling. Although classical force fields often fail to describe the coupling between electronic states and ionic rearrangements, the more accurate ab initio molecular dynamics suffers from computational complexity that prevents long-time and large-scale simulations, which are essential to study technologically relevant phenomena. Here we present the Crystal Hamiltonian Graph Neural Network (CHGNet), a graph neural network-based machine-learning interatomic potential (MLIP) that models the universal potential energy surface. CHGNet is pretrained on the energies, forces, stresses and magnetic moments from the Materials Project Trajectory Dataset, which consists of over 10 years of density functional theory calculations of more than 1.5 million inorganic structures. The explicit inclusion of magnetic moments enables CHGNet to learn and accurately represent the orbital occupancy of electrons, enhancing its capability to describe both atomic and electronic degrees of freedom. We demonstrate several applications of CHGNet in solid-state materials, including charge-informed molecular dynamics in Li x MnO 2 , the finite temperature phase diagram for Li x FePO 4 and Li diffusion in garnet conductors. We highlight the significance of charge information for capturing appropriate chemistry and provide insights into ionic systems with additional electronic degrees of freedom that cannot be observed by previous MLIPs.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Kinetics and Thermodynamics of Sr Permeation in CeO 2 -Based Barrier Layers for Solid-Oxide Electrolyzer Cells

Solid-oxide electrolyzer cells (SOECs) convert steam to hydrogen efficiently at high temperatures. However, during operation, the diffusion of cations or impurities through the cells due to electrode degradation can cause unwanted secondary phases to form, which may degrade device performance. Here, in this study, we use atomistic and mesoscale simulations coupled with experimental analysis to study the diffusion of Sr through the Gd-doped CeO 2 (GDC) barrier layer used to protect the yttria-stabilized zirconia (YSZ) electrolyte in SOECs. From our atomistic calculations, we find Sr diffusion to be negligibly slow in bulk GDC; however, surface diffusion is much more favorable. Subsequent mesoscale simulations show that Sr diffusion is activated when the porosity of GDC exceeds ∼10% and significantly exceeds diffusion in bulk and grain boundary regions. We also find that SrO-based species can accumulate at GDC surfaces; however, SrO aggregation and coarsening will be limited by the large lattice mismatch between GDC and SrO. Energy-dispersive X-ray spectroscopy (EDS) and electron diffraction confirm that Sr can accumulate within GDC pores and form disperse Sr-containing secondary phases. Altogether, Sr diffusion in dense GDC is unlikely to give rise to thick SrO layers, which would severely limit device performance. The formation of Sr-containing secondary phases can largely be avoided by restricting the porosity of the GDC layer as much as possible.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Molecular dynamics simulations of alkaline earth metal ions binding to DNA reveal ion size and hydration effects

The identity of metal ions surrounding DNA is key to its biological function and materials applications. In this work, we compare atomistic molecular dynamics simulations of double strand DNA (dsDNA) with four alkaline earth metal ions (Mg 2+ , Ca 2+ , Sr 2+ , and Ba 2+ ) to elucidate the physical interactions that govern DNA–ion binding. Simulations accurately model the ion–phosphate distance of Mg 2+ and reproduce ion counting experiments for Ca 2+ , Sr 2+ , and Ba 2+ . Our analysis shows that alkaline earth metal ions prefer to bind at the phosphate backbone compared to the major groove and negligible binding occurs in the minor groove. Larger alkaline earth metal ions with variable first solvation shells (Ca 2+ , Sr 2+ , and Ba 2+ ) show both direct and indirect binding, where indirect binding increases with ion size. Mg 2+ does not fit this trend because the strength of its first solvation shell predicts indirect binding only. Ions bound to the phosphate backbone form fewer contacts per ion compared to the major groove. Within the major groove, metal ions preferentially bind to guanine–cystosine base pairs and form simultaneous contacts with the N7 and O6 atoms of guanine. Overall, we find that the interplay among ion size, DNA–ion interaction, and the size and flexibility of the first solvation shell are key to predicting how alkaline earth metal ions interact with DNA.

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

Molecular Dynamics Simulations of a Catalytic Multivalent Peptide–Nanoparticle Complex

Molecular modeling of a supramolecular catalytic system is conducted resulting from the assembling between a small peptide and the surface of cationic self-assembled monolayers on gold nanoparticles, through a multiscale iterative approach including atomistic force field development, flexible docking with Brownian Dynamics and µs-long Molecular Dynamics simulations. Self-assembly is a prerequisite for the catalysis, since the catalytic peptides do not display any activity in the absence of the gold nanocluster. Atomistic simulations reveal details of the association dynamics as regulated by defined conformational changes of the peptide due to peptide length and sequence. Our results show the importance of a rational design of the peptide to enhance the catalytic activity of peptide–nanoparticle conjugates and present a viable computational approach toward the design of enzyme mimics having a complex structure–function relationship, for technological and nanomedical applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Extended Lagrangian Born–Oppenheimer molecular dynamics for orbital-free density-functional theory and polarizable charge equilibration models

We report extended Lagrangian Born–Oppenheimer molecular dynamics (XL-BOMD) is formulated for orbital-free Hohenberg–Kohn density-functional theory and for charge equilibration and polarizable force-field models that can be derived from the same orbital-free framework. The purpose is to introduce the most recent features of orbital-based XL-BOMD to molecular dynamics simulations based on charge equilibration and polarizable force-field models. These features include a metric tensor generalization of the extended harmonic potential, preconditioners, and the ability to use only a single Coulomb summation to determine the fully equilibrated charges and the interatomic forces in each time step for the shadow Born–Oppenheimer potential energy surface. The orbital-free formulation has a charge-dependent, short-range energy term that is separate from long-range Coulomb interactions. This enables local parameterizations of the short-range energy term, while the long-range electrostatic interactions can be treated separately. The theory is illustrated for molecular dynamics simulations of an atomistic system described by a charge equilibration model with periodic boundary conditions. The system of linear equations that determines the equilibrated charges and the forces is diagonal, and only a single Ewald summation is needed in each time step. The simulations exhibit the same features in accuracy, convergence, and stability as are expected from orbital-based XL-BOMD.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Clustering Effects on the Structure of Ionomer Solutions: A Combined SANS and Simulations Study

Ionic assemblies, or clusters, determine the structure and dynamics of ionizable polymers and enable their many applications. Fundamental to attaining well-defined materials is controlling the balance between van der Waals interactions that govern the backbone behavior and the forces that drive the formation of ionic clusters. Here, using small angle neutron scattering and fully atomistic molecular dynamics simulations, the structure of a model ionomer, sulfonated polystyrene in toluene solutions, was investigated as the cluster cohesion was tweaked by the addition of ethanol. The static structure factor was measured by both techniques and correlated with the size of the ionic clusters as the polymer concentration was varied. The conjunction of SANS results and molecular insight from MD simulations enabled the determination of the structure in these inhomogeneous networks on multiple length scales. Additionally, we find that across the entire concentration range studied, a network driven by the formation of ionic clusters was formed, where the size of the clusters drives the inhomogeneity of these systems. Tweaking the ionic clusters through the addition of ethanol impacts the packing of the sulfonated groups, their shape, and their size distribution, which in turn, affects the structure of these networks.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Theory of Defect-Induced Crystal Field Perturbations in Rare-Earth Magnets

We present a theory describing the single-ion anisotropy of rare-earth (RE) magnets in the presence of point defects. Taking the RE-lean 1 ∶ 12 magnet class as a prototype, we use first-principles calculations to show how the introduction of Ti substitutions into SmFe 12 perturbs the crystal field, generating new coefficients due to the lower symmetry of the RE environment. We then demonstrate that these perturbations can be described extremely efficiently using a screened point charge model. We provide analytical expressions for the anisotropy energy that can be straightforwardly implemented in atomistic spin dynamics simulations, meaning that such simulations can be carried out for an arbitrary arrangement of point defects. The significant crystal field perturbations calculated here demonstrate that a sample that is single phase from a structural point of view can nonetheless have a dramatically varying anisotropy profile at the atomistic level if there is compositional disorder, which may influence localized magnetic objects like domain walls or skyrmions. Published by the American Physical Society 2024

25 ENERGY STORAGE↗
DOE OSTI2022

Phase-field simulations to inform nuclear fuel performance modeling

Software tools to simulate nuclear fuel performance at the engineering scale, such as Idaho National Laboratory (INL)’s BISON code, are increasingly relied upon in regulatory and economic decision-making. However, accurate results from these tools depends on the availability of materials parameters that are used as input. In recent years, atomistic and mesoscale simulation methods have emerged as a cost-effective, expedient means to obtain such input parameters. Phase-field simulations using INL’s Marmot application have been used to obtain microstructure-level parameters and to improve material models for fuel performance modeling using BISON. In this talk, recent examples of this process are given, including applications in UO2, U3Si2, and UZr fuel.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Platinum nanoparticle compression: Combining in situ TEM and atomistic modeling

The mechanical behavior of nanoparticles governs their performance and stability in many applications. However, the small sizes of technologically relevant nanoparticles, with diameters in the range of 10 nm or less, significantly complicate experimental examination. These small nanoparticles are difficult to manipulate onto commercial test platforms and deform at loads that are below the typical noise floor of the testing instruments. Here, we synthesized small platinum nanoparticles directly onto a mechanical tester and used a modified nanomanipulator to enhance load resolution to the nanonewton scale. We demonstrated the in situ compression of an 11.5-nm platinum nanoparticle with simultaneous high-resolution measurements of load and particle morphology. Molecular dynamics simulations were performed on similarly sized particles to achieve complementary measurements of load and morphology, along with atomic resolution of dislocations. The experimental and simulation results revealed comparable values for the critical resolved shear stress for failure, 1.28 and 1.15 GPa, respectively. Altogether, this investigation demonstrated the promise of, and some initial results from, the combination of atomistic simulations and in situ experiments with an unprecedented combination of high spatial resolution and high load resolution to understand the behavior of metal nanoparticles under compression.

42 ENGINEERING↗
DOE OSTI2023

Pair distribution function analysis of discrete nanomaterials in PDFgui

Pair distribution functions (PDFs) are a leading tool for atomic structure analysis of nanomaterials. However, the most widely used programs for refining atomic structure against PDF data are based on extended crystallographic models, which cannot be applied to discrete, whole nanoparticles. Furthermore, this work describes a straightforward approach to simulate and refine atomistic models of discrete clusters and nanoparticles employing widely used PDF modelling programs such as PDFgui that utilize extended crystallographic models. In this approach, the whole particle to be modelled is contained within an expanded, and otherwise empty, unit cell that is sufficiently large to avoid correlations between atoms in neighbouring unit cells over the r range analyzed. The PDF of the particle is simulated as a composite using two conventional `phases': one that calculates the atom–atom correlations and one that approximates the local number density. This approach is first validated for large nanoparticles that are well modelled by a conventional shape factor model, and then applied to simulate the PDF of discrete particles and low-dimensional materials (graphene and MXene) and to model the experimental PDF data for single-layer FeS nanosheets. A comparison of this approach with the DiffPy-CMI program, which calculates the PDF of discrete species, shows that the composite modelling approach is equally or more accurate. Example input files for implementing this approach within PDFgui and TOPAS, and recommendations for selecting model parameters for reliable application of this refinement strategy, are provided.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Investigating structure and dynamics of unentangled poly(dimethyl- co -diphenyl)siloxane via molecular dynamics simulation

Polysiloxane is one of the most important polymeric materials in technological use. Polydimethylsiloxane displays glass-like mechanical properties at low temperatures. Incorporation of phenyl siloxane, via copolymerization for example, improves not only the low-temperature elasticity but also enhances its performance over a wide range of temperatures. Copolymerization with the phenyl component can significantly change the microscopic properties of polysiloxanes, such as chain dynamics and relaxation. However, despite much work in the literature, the influence of such changes is still not clearly understood. In this work, we systematically study the structure and dynamics of random poly(dimethyl- co -diphenyl)siloxane via atomistic molecular dynamics simulations. As the molar ratio Φ of the diphenyl component increases, we find that the size of the linear copolymer chain expands. At the same time, the chain-diffusivity slows down by over an order of magnitudes. In conclusion, the reduced diffusivity appears to be a result of a complex interplay between the structural and dynamic changes induced by phenyl substitution.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Role of element-specific damping in ultrafast, helicity-independent, all-optical switching dynamics in amorphous (Gd,Tb)Co thin films

Ultrafast control of the magnetization in ps timescales by fs laser pulses offers an attractive avenue for applications such as fast magnetic devices for logic and memory. However, ultrafast helicity-independent all-optical switching (HI-AOS) of the magnetization has thus far only been observed in Gd-based, ferrimagnetic amorphous (a-) rare earth-transition metal (a-RE-TM) systems, and a comprehensive understanding of the reversal mechanism remains elusive. Here, we report HI-AOS in ferrimagnetic a-Gd 22-x Tb x Co 78 thin films, from x=0 to 18, and elucidate the role of Gd in HI-AOS in a-RE-TM alloys and multilayers. Increasing Tb content results in increasing perpendicular magnetic anisotropy and coercivity, without modifying magnetization density, and slower remagnetization rates and higher critical fluences for switching but still shows picosecond HI-AOS. Simulations of the atomistic spin dynamics based on the two-temperature model reproduce these results qualitatively and predict that the lower damping on the RE sublattice arising from the small spin-orbit coupling of Gd (with L=0) is instrumental for the faster dynamics and lower critical fluences of the Gd-rich alloys. Annealing a-Gd 10 Tb 12 Co 78 leads to slower dynamics which we argue is due to an increase in damping. These simulations strongly indicate that accounting for element-specific damping is crucial in understanding HI-AOS phenomena. The results suggest that engineering the element-specific damping of materials can open up new classes of materials that exhibit low-energy, ultrafast HI-AOS.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Emerging Atomistic Modeling Methods for Heterogeneous Electrocatalysis

Heterogeneous electrocatalysis lies at the center of various technologies that could help enable a sustainable future. However, its complexity makes it challenging to accurately and efficiently model at an atomic level. Herein, we review emerging atomistic methods to simulate the electrocatalytic interface with special attention devoted to the components/effects that have been challenging to model, such as solvation, electrolyte ions, electrode potential, reaction kinetics, and pH. Additionally, we review relevant computational spectroscopy methods. Then, we showcase several examples of applying these methods to understand and design catalysts relevant to green hydrogen. We also offer experimental views on how to bridge the gap between theory and experiments. Finally, we provide some perspectives on opportunities to advance the field.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Fingerprinting Superconductors by Disentangling Andreev and Quasiparticle Currents Across Tunable Tunnel Junctions

Tunneling Andreev reflection (TAR) spectroscopy provides a new approach to identify superconducting pairing symmetry at the atomic scale. Using atomistic superconducting transport simulations, we reveal the mechanism by which TAR can distinguish between pairing symmetries, which is complementary to traditional conductance-based techniques. In particular, owing to the additivity of the excess tunneling decay rate, the TAR spectrum is a weighted average of the contributions from quasiparticle currents, Andreev reflection, and higher-order scattering processes, and their relative weights depend on both the superconducting order parameter and the coupling strength. Within the local tunneling model, TAR dominates mid-gap conductance for s-wave superconductors, is suppressed for d-wave, and coexists with quasiparticle tunneling in sign-changing symmetries if the expectation value for the superconducting gap remains finite. Meanwhile, higher-order processes generally enhance the TAR signal when GN exceeds approximately 0.1G0. As a result, TAR provides a rich spectral fingerprint of the underlying pairing symmetry and electronic structure, enabling atomically resolved identification of unconventional superconducting states.

Maksymovych, Petro [Clemson University]↗