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DOE OSTI2021

Stability and mobility of tungsten clusters on tungsten (110) surface: Ab initio and atomistic simulations

Quantifying the surface transport properties of tungsten (W) is of prime importance to understand the formation of nano-fuzz in fusion plasma-facing conditions. The stability and mobility of W adatom clusters (W n , n = 2-9) on the W(110) surface has been investigated by computer simulations, including ab initio calculations using density functional theory (DFT) and molecular statics (MS) simulations with multiple W interatomic potentials. The DFT results demonstrate that the sequential binding energy generally increases with number of W adatoms, except for the 5 th and 7 th W adatoms. The most common elemental migration steps of W n (n>2) clusters are observed to consist of monomer and dimer hops, while larger W n clusters can also diffuse by dissociation and recombination of smaller clusters. The threshold migration energy of W 9 is the highest, then followed by W 8 , W 4 , and W 6 , while W 3 , W 5 , and W 7 have similar migration energies. Compared to DFT, each interatomic potential evaluated overestimates the binding energies of W n clusters. Additionally, the embedded-atom potential developed by Juslin and Wirth adequately predicts the threshold migration energy of W n (n>2) clusters, although it predicts different underlying migration mechanisms. The results show that interaction mechanism between W adatoms controls the stability and mobility of W n clusters on the W(110) surface.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Parameter uncertainties for imperfect surrogate models in the low-noise regime

Abstract Bayesian regression determines model parameters by minimizing the expected loss, an upper bound to the true generalization error. However, this loss ignores model form error, or misspecification, meaning parameter uncertainties are significantly underestimated and vanish in the large data limit. As misspecification is the main source of uncertainty for surrogate models of low-noise calculations, such as those arising in atomistic simulation, predictive uncertainties are systematically underestimated. We analyze the true generalization error of misspecified, near-deterministic surrogate models, a regime of broad relevance in science and engineering. We show that posterior parameter distributions must cover every training point to avoid a divergence in the generalization error and design a compatible ansatz which incurs minimal overhead for linear models. The approach is demonstrated on model problems before application to thousand-dimensional datasets in atomistic machine learning. Our efficient misspecification-aware scheme gives accurate prediction and bounding of test errors in terms of parameter uncertainties, allowing this important source of uncertainty to be incorporated in multi-scale computational workflows.

Swinburne, Thomas D. (ORCID:0000000232554257)↗

DOE OSTI2022

Exploration of the Application of Machine Learning to the improvement of Interatomic Potentials

Current methods for atomistic simulations of material systems suffer from limitations which restrict the ability of the simulation to correctly characterize certain material behavior and physical phenomena. Small scale ab initio (AIMD) modeling is highly accurate but is computationally expensive. Classical molecular dynamics (CMD) simulations use interatomic potentials (IPs) to describe larger systems at a reduced computational cost, but with a reduced accuracy. Creating more robust IPs enable more precise CMD simulations. In this thesis, the application of a specific machine learning process,artifical neural networks (ANNs), to the improvement of IPs and MD simulation is discussed.

O'Connor, Sean↗

DOE OSTI2022

1 / ω electric-field noise in surface ion traps from correlated adsorbate dynamics

Ion traps are promising architectures for implementing quantum computers, but they suffer from excessive “anomalous” ion motional heating that limit their overall coherence and practicality for scalable quantum computing. The exact microscopic origins of anomalous heating remain an open question, but experiments point to adsorbates on trap electrodes as one likely source. Many different models of anomalous heating have been proposed, but these models have yet to pinpoint the atomistic origin of the experimentally observed 1/ω electric-field noise scaling seen in ion traps at frequencies between 0.1–10 MHz. In this work, we show that a model based on previously proposed surface-induced dipole fluctuations on adsorbates, but which also incorporates interparticle interaction dynamics through molecular dynamics simulations of up to multiple monolayers of adsorbates, gives rise to 1/ω frequency scaling at the MHz frequencies typically employed in ion traps. Overall, these results demonstrate that moderate-to-high densities of adsorbates can give rise to a set of activated motions that produce the 1/ω noise observed in ion traps and that collective adsorbate motions produce the observed noise spectra that a noninteracting model does not capture.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Fusion Blanket and Fuel Cycle Research at PNNL: FY22 Year-end Report

During the reporting period, research at PNNL focused on two tasks within the DOE Fusion Blanket and Fuel Cycle Program. Research on Task 1, Tritium Extraction from Pb-Li and He Using a Vacuum Permeator, focused on atomistic modeling to better understand tritium transport in Pd-coated V vacuum permeators. As a lower cost alternative to Pd permeators, thin coatings of Pd (or other noble metals) can be deposited over a substrate like V. However, the permeation performance of composite metal membranes degrades over time, due to the formation of intermetallics at the coating-substrate interface. Computational studies were performed to better understand tritium transport through these Pd-V intermetallics. The results of the FY22 Pd-V modeling study were recently submitted for publication in Computational Materials Science and presented at the Technology of Fusion Energy conference. Future work in this area will focus on interdiffusion barriers to prevent intermetallic formation that is deleterious to tritium transport. There are opportunities for collaboration with researchers at the Colorado School of Mines, who are manufacturing and testing candidate interdiffusion barriers. Research on Task 3, Solid Breeder Materials, included ion irradiation and post-irradiation characterization of lithium orthosilicate (Li 4 SiO 4 ) and lithium metasilicate (Li 2 SiO 3 ) to improve fundamental understanding of irradiation effects, in combination with atomistic modeling focused on the energetics of He clustering in these two ceramic phases. The results of the study suggested that the Li 4 SiO 4 phase, which is more desirable as a solid breeder due to its higher Li density, was amorphized during ion irradiation while the Li 2 SiO 3 phase appeared to be more resistant to irradiation damage. It is possible that Li loss contributed to the poor irradiation performance of the Li 4 SiO 4 , and some thoughts are provided regarding coatings that could be applied to solid breeders like this to prevent Li loss at elevated temperature while not hindering tritium diffusion. The results of the FY22 ion irradiation study were recently submitted for publication in Journal of Nuclear Materials and presented at the 22nd International Conference on Ion Beam Modification of Materials. Future work in this area will focus on Li-rich ceramics such as Li 5 AlO 4 and Li 8 ZrO 6 that have high Li density and should provide rapid tritium release based on previous work with less Li-rich ceramics.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2022

Atomistic Materials Modeling of High-Pressure Hydrogen Interactions in Ethylene Propylene Diene Monomer (EPDM) Rubber

Elastomeric rubbers serve a vital role as sealing materials in the hydrogen storage and transport infrastructure. With applications including O-rings and hose-liners, these components are exposed to pressurized hydrogen at a range of temperatures, cycling rates, and pressure extremes. Cyclic (de)pressurization is known to degrade these materials through the process of cavitation. This readily visible failure mode occurs as a fracture or rupture of the material and is due to the oversaturated gas localizing to form gas bubbles. Computational modeling in the Hydrogen Materials Compatibility Program (H-Mat), co-led by Sandia National Laboratories and Pacific Northwest National Laboratory, employs multi-scale simulation efforts to build a predictive understanding of hydrogen-induced damage in materials. Modeling efforts within the project aim to provide insight into how to formulate materials that are less sensitive to high-pressure hydrogen-induced failure. In this document, we summarize results from atomistic molecular dynamics simulations, which make predictive assessments of the effects of compositional variations in the commonly used elastomer, ethylene propylene diene monomer (EPDM).

08 HYDROGEN↗

DOE OSTI2021

Predicting 1 H NMR relaxation in Gd 3+ -aqua using molecular dynamics simulations

Atomistic molecular dynamics simulations are used to predict 1 H NMR T 1 relaxation of water from paramagnetic Gd 3+ ions in solution at 25 °C. Simulations of the T 1 relaxivity dispersion function r 1 computed from the Gd 3+ – 1 H dipole–dipole autocorrelation function agree within ≃8% of measurements in the range f 0 ≃ 5 ↔ 500 MHz, without any adjustable parameters in the interpretation of the simulations, and without any relaxation models. Here the simulation results are discussed in the context of the Solomon-Bloembergen-Morgan inner-sphere relaxation model, and the Hwang-Freed outer-sphere relaxation model. Below f 0 ≲ 5 MHz, the simulation overestimates r 1 compared to measurements, which is used to estimate the zero-field electron-spin relaxation time. The simulations show potential for predicting r 1 at high frequencies in chelated Gd 3+ contrast-agents used for clinical MRI.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Excited-state electronic structure of molecules using many-body Green’s functions: Quasiparticles and electron–hole excitations with VOTCA-XTP

Here, we present the open-source VOTCA-XTP software for the calculation of the excited-state electronic structure of molecules using many-body Green’s function theory in the GW approximation with the Bethe–Salpeter equation (BSE). This work provides a summary of the underlying theory and discusses the details of its implementation based on Gaussian orbitals, including resolution-of-identity techniques and different approaches to the frequency integration of the self-energy or acceleration by offloading compute-intensive matrix operations using graphics processing units in a hybrid OpenMP/Cuda scheme. A distinctive feature of VOTCA–XTP is the capability to couple the calculation of electronic excitations to a classical polarizable environment on an atomistic level in a coupled quantum- and molecular-mechanics (QM/MM) scheme, where a complex morphology can be imported from Molecular Dynamics simulations. The capabilities and limitations of the GW–BSE implementation are illustrated with two examples. First, we study the dependence of optically active electron–hole excitations in a series of diketopyrrolopyrrole-based oligomers on molecular-architecture modifications and the number of repeat units. Second, we use the GW–BSE/MM setup to investigate the effect of polarization on localized and intermolecular charge-transfer excited states in morphologies of low-donor content rubrene–fullerene mixtures. These showcases demonstrate that our implementation currently allows us to treat systems with up to 2500 basis functions on regular shared-memory workstations, providing accurate descriptions of quasiparticle and coupled electron–hole excited states of various characters on an equal footing.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2022

Extensive Attention Mechanisms in Graph Neural Networks for Materials Discovery

We present our research where attention mechanism is extensively applied to various aspects of graph neural net- works for predicting materials properties. As a result, surrogate models can not only replace costly simulations for materials screening but also formulate hypotheses and insights to guide further design exploration. We predict formation energy of the Materials Project and gas adsorption of crystalline adsorbents, and demonstrate the superior performance of our graph neural networks. Moreover, attention reveals important substructures that the machine learning models deem important for a material to achieve desired target properties. Our model is based solely on standard structural input files containing atomistic descriptions of the adsorbent material candidates. We construct novel methodological extensions to match the prediction accuracy of state-of-the-art models some of which were built with hundreds of features at much higher computational cost. We show that sophisticated neural networks can obviate the need for elaborate feature engineering. Our approach can be more broadly applied to optimize gas capture processes at industrial scale.

Cong, Guojing↗

DOE OSTI2022

Computational Engineering of Human Apolipoprotein for Enhanced Interaction with Bacterial Toxins

Sepsis is caused by an over-activation of the immune system in response to the presence of bacterial endotoxins in the bloodstream. LPS exemplifies a potent immune stimulator which can lead to an excessive activation leading to an overly pro-inflammatory reaction and if left untreated results in multi-organ failure and death. Human lipoproteins (HLP) have been shown to neutralize LPS and facilitate its clearance from circulation via the liver. However, host-derived lipoproteins are inhomogeneous in content and size, presenting a challenge for therapeutic applications. In this work, we have used IC facilities in order to computationally engineer HLP for an enhanced interaction with LPS. This work is a continuation of our previous efforts to derive an optimal Nanodisc (ND) lipid mixture for enhanced association with the endotoxin. We have performed fully atomistic Molecular dynamic (MD) simulations in order to carefully identify the most optimal residues in HLP, which can potentially lead to a stronger association to LPS.

42 ENGINEERING↗

DOE OSTI2021

Robust Molecular Predictive Methods for Novel Polymer Discovery and Applications

Polymeric materials are ubiquitous in modern society and they play an instrumental role in almost all industries, undoubtedly including the energy and environment sectors. Increased demand of energy and awareness to sustainability both necessitates the development of novel polymers with enhanced properties. Unfortunately, their structural and behavioral complexity render such discovery challenging and impeded. To address this problem, scientists are developing various computational modeling techniques and leveraging their power to depict the relationship between structural characteristics of polymers and their properties (such as rheological behaviors), and use such prediction to guide the design and syntheses of novel polymeric materials with enhanced performances. Unfortunately, predicting the relationships between polymer structure and composition with rheological properties via atomistic modeling is still a major challenge because of the extended time and length scales involved. Studying dynamic shear viscosity and linear viscoelasticity using molecular models requires capabilities that have been elusive, including representation of large molecular weight chains with an effective internal scale capable of describing entanglement, shear-rates that are in the s-1 scale with accurate quantitative stresses, and chemically-realistic combinations of both homogeneous and heterogeneous systems. Motivated by these unmet challenges, the overall technical objective of this DOE-STTR Phase II project is to develop robust molecular predictive methods for advanced polymer discovery and applications and especially for designing and demonstrating the “smart” polymer-based waterflooding enhanced oil recovery (EOR) process. In particular, we apply state-of-the-art molecular modeling methods developed by our academic partner, Materials Stimulation Center (MSC) at California Institute of Technology (Caltech), to facilitate and accelerate the experimental discovery processes. During the Phase I of this project, we had focused on development and demonstration of the molecular modeling methods to describe rheological properties of non-Newtonian polymer fluids, and to improve our fundamental understandings of shear-thickening mechanism and kinetics. In Phase II, we further apply the theoretical models to guide our experimental programs to improve our design of smart rheology modifier (SRM) polymers and their optimization for EOR. Specifically, we have three objectives in the Phase II study: (1) to further improve out computational modeling methods, coupling with the advanced machine learning algorithms; (2) to develop cost-effective and efficient SRM-flooding process suitable for EOR applications under typical reservoir conditions; and (3) to further explore the application of our molecular predictive models for innovative material discovery in other industrial applications. The recent development of our multiscale predictive framework allows the successful prediction of rheological properties from the chemical structure for polymers of experimentally relevant molecular weights, and provides an in-silico machine learning engine for screening novel compositions and structures with optimized non-Newtonian response, required for both shear-thinning and shear-thickening applications. Our framework provides: (1) procedures and tools for systematic coarsening from atomistic models and reverse mapping of coarse-grain models to atomistic, (2) unique ab initio methods to characterize the atomistic origin of colloidal and interfacial interactions and phenomena, (3) systematic structure and composition builders based on practical descriptors that drive rheological changes in polymer melts and diluted polymer mixtures, (4) a rheological properties engine capable of predicting viscosity in the zero-shear limit and under realistic dynamic conditions (for shear-rates commensurate with experiments) for large heterogeneous systems, (5) coarse-grain force fields with improved non-bond descriptions based on accurate quantum mechanics, (6) an in-silico screening machine learning engine that feeds from the systematic model builders to cover the descriptors search space, computes the rheological properties from converged trajectories spanning sub-milliseconds and ranks them for each structure/composition using an automated viscosity-vs-shear rate fitness function that can be tuned for shear-thickening, shear-thinning and other rheological responses.

02 PETROLEUM↗

NASA NTRS2009

An Overview of the State of the Art in Atomistic and Multiscale Simulation of Fracture

The emerging field of nanomechanics is providing a new focus in the study of the mechanics of materials, particularly in simulating fundamental atomic mechanisms involved in the initiation and evolution of damage. Simulating fundamental material processes using first principles in physics strongly motivates the formulation of computational multiscale methods to link macroscopic failure to the underlying atomic processes from which all material behavior originates. This report gives an overview of the state of the art in applying concurrent and sequential multiscale methods to analyze damage and failure mechanisms across length scales.

Saether, Erik↗

DOE OSTI2025

Minimization of Cathode|Solid-Electrolyte Interfacial Delamination through the Application of Interphase Layers

Next-generation lithium-ion batteries are expected to use solid electrolytes (SEs) to enable higher energy density and extreme fast-charge capabilities. One major mode of degradation at the cathode|SE interface is delamination between the cathode active materials and SEs, which leads to performance decay. Experimental observations indicate that implementation of interphase layers can minimize the cathode|SE delamination induced capacity fade. A multiscale computational methodology is developed here to investigate the applicability of boron substituted lithium carbonate (Li 2+x B x C 1–x O 3 , x = 0.5, or LBCO) to minimize the delamination at the cathode|SE interface. Atomistic simulations indicate that the fracture energies at both the cathode|LBCO and LBCO|SE interfaces are higher than those at the cathode|SE interface, which reduces the extent of delamination. Mesoscale simulations indicate that, apart from increasing the fracture energy, decreasing the evolution of strain energy by lowering the elastic modulus of the interphase layer can also minimize the extent of delamination at the cathode|SE interface. However, the adoption of an interphase layer with high ionic conductivity is necessary to minimize the ohmic losses during operation at higher current densities. This study provides guidance on selecting interphase layers with specific properties and thicknesses to minimize both interfacial delamination and impedance growth.

LBCO↗

DOE OSTI2026

From molecular to macroscopic: predicting liquid–liquid phase equilibria and small-angle scattering of mixtures of organic liquids from atomistic simulation using Kirkwood–Buff theory

Macroscopic phase equilibria between solutions define the functionality of many biological and industrial processes, yet they are challenging to predict due to the inherent complexity of liquids containing large molecules. This work introduces an approach for the purely predictive calculation of such phase equilibria in temperature-composition space from molecular dynamics (MD) simulations at one temperature in the single-phase region. We use an approach developed previously to obtain the entropic and enthalpic contributions to the free energy of mixing from the atomic-scale information given by MD simulations via Kirkwood–Buff theory. This allows us to accurately estimate the free energy of mixing as a function of temperature, and thus obtain liquid–liquid phase equilibria, including liquid–liquid critical points, associated binodal and spinodal lines, and composition fluctuations across a region of temperature and composition. Results for binary malonamide–alkane systems are validated by comparison to a direct experimental probe of the fluctuations: the small angle X-ray scattering intensity near zero wavenumber. The MDKB → Phase method demonstrated here provides a significant improvement in predicting liquid–liquid equilibria and free energy as a function of temperature for our systems of interest compared to conventional thermodynamic models. The accurate performance of this purely predictive approach lies in its preservation of atomistic details when determining thermodynamic properties. Furthermore, its inherent extensibility to multi-component systems will likely make the MDKB → Phase approach a valuable general tool for connecting molecular interactions to macroscopic phase equilibria and for the computational screening of materials for targeted thermodynamic behavior.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Strain Fluctuations Unlock Ferroelectricity in Wurtzites

Ferroelectrics are of practical interest for non-volatile data storage due to their reorientable, crystallographically defined polarization. Yet efforts to integrate conventional ferroelectrics into ultrathin memories have been frustrated by film-thickness limitations, which impede polarization reversal under low applied voltage. Wurtzite materials, including magnesium-substituted zinc oxide (Zn,Mg)O, have been shown to exhibit scalable ferroelectricity as thin films. In this work, the origins of ferroelectricity in (Zn,Mg)O are explained, showing that large strain fluctuations emerge locally in (Zn,Mg)O and can reduce local barriers to ferroelectric switching by more than 40%. Concurrent experimental and computational evidence of these effects are provided by demonstrating polarization switching in ZnO/(Zn,Mg)O/ZnO heterostructures featuring built-in interfacial strain gradients. These results open up an avenue to develop scalable ferroelectrics by controlling strain fluctuations atomistically.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Cross-scale covariance for material property prediction

A simulation can stand its ground against an experiment only if its prediction uncertainty is known. The unknown accuracy of interatomic potentials (IPs) is a major source of prediction uncertainty, severely limiting the use of large-scale classical atomistic simulations in a wide range of scientific and engineering applications. Here we explore covariance between predictions of metal plasticity, from 178 large-scale (~10 8 atoms) molecular dynamics (MD) simulations, and a variety of indicator properties computed at small-scales (≤10 2 atoms). All simulations use the same 178 IPs. In a manner similar to statistical studies in public health, we analyze correlations of strength with indicators, identify the best predictor properties, and build a cross-scale “strength-on-predictors” regression model. This model is then used to estimate regression error over the statistical pool of IPs. Small-scale predictors found to be highly covariant with strength are computed using expensive quantum-accurate calculations and used to predict flow strength, within the statistical error bounds established in our study.

36 MATERIALS SCIENCE↗

DOE OSTI2025

PyLRO: A Python calculator for analyzing long-range structural order

We present PyLRO, an open-source Python calculator designed to detect, quantify, and display long-range order in periodic structures. The program’s design methodology, workflow, and approach to order quantification are described and demonstrated using a simple toy model. Additionally, we apply PyLRO to a series of metastable AlPO 4 structural intermediates from a prior high-pressure study, demonstrating how to compute and visualize structural order in all directions on a Miller sphere. We further highlight the program’s capabilities through a high-throughput analysis of structural patterns in the pressure-induced amorphization of AlPO 4 , revealing atomistic insights into specific energy regions of massive amorphous structures. These results suggest that PyLRO can be a valuable tool for investigating crystal–amorphous transition in materials research.

36 MATERIALS SCIENCE↗

NASA NTRS2019

AladynPi – Adaptive Neural Network Molecular Dynamics Simulation Code with Physically Informed Potential: Computational Materials Mini-Application

This report provides an overview and description of commands used in the Computational Materials mini-application, AladynPi. AladynPi is an extension of a previously released mini-application, Aladyn (https://github.com/nasa/aladyn; Yamakov, V.I., and Glaessgen, E.H., NASA/TM-2018-220104). Aladyn and AladynPi are basic molecular dynamics codes written in FORTRAN 2003, which are designed to demonstrate the use of adaptive neural networks (ANNs) in atomistic simulations. The role of ANNs is to efficiently reproduce the very complex energy landscape resulting from the atomic interactions in materials with the accuracy of the more expensive quantum mechanics-based calculations. The ANN is trained on a large set of atomic structures calculated using the density functional theory method. An input for the ANN is a set of structure coefficients, characterizing the local atomic environment of each atom, for which the atomic energy is obtained in the ANN inference process. In Aladyn, the ANN gives directly the energy of interatomic interactions. In AladynPi, the ANN gives optimized parameters for a predefined empirical function, known as bond-order-potential (BOP). The parameterized BOP function is then used to calculate the energy. AladynPi code is being released to serve as a training testbed for students and professors in academia to explore possible optimization algorithms for parallel computing on multicore central processing unit (CPU) computers or computers utilizing manycore architectures based on graphic processing units (GPUs). The effort is supported by the High Performance Computing incubator (HPCi) project at NASA Langley Research Center.

Yamakov, Vesselin I.↗