A dislocation density-based meshfree computational framework for solid phase processing
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36 MATERIALS SCIENCE↗
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Results for “Computational materials science”
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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Consistency and reproducibility in atomic layer deposition
Atomic layer deposition (ALD) is a thin film synthesis technique that can provide exquisite accuracy and precision in film thickness and composition even on complex, large area substrates. Based on self-limiting surface chemistry, ALD can be insensitive to process conditions and reactor designs, allowing an ALD process developed in one lab to be easily reproduced in other labs. In practice, however, ALD is sometimes difficult to reproduce or replicate, and the results can vary substantially between ALD reactors and between labs. This is exemplified by large deviations in reports on the growth of, e.g., Al2O3, FeOx, and TiO2 given the same precursors under similar conditions. Furthermore, the problem of irreproducibility seems to be growing as ALD is adopted by more researchers and integrated into new applications. In this article, the authors highlight some of the major sources of variations and errors and common misconceptions related to ALD. In particular, the authors focus on issues related to precursors, substrates, and deposition tools. The authors illustrate these problems through examples from the literature, and they present results from numerical simulations that describe how nonidealities would manifest in thickness profiles in a typical cross-flow reactor. They also describe how reproducibility in ALD is linked to consistent experimental practice and reporting between labs. The authors’ hope is that by educating newcomers to ALD and advocating for consistent reporting of deposition conditions, they can minimize irreproducibility and enable ALD practitioners to realize the full potential afforded by self-limiting surface chemistry.
36 MATERIALS SCIENCE↗
Towards libraries of different ultrasmall amorphous silica cage topologies
Cage topology controlled at the nanometer length scale is expected to enable original functionalities, e.g., in catalysis and therapeutics. Cages are fundamental structural motifs of clathrates and their topological duals, Frank-Kasper phases, and are constituents of mesoporous silica frameworks. However, with one exception, they have not been synthesized as discrete particles. By varying reactant ratios of surfactant, oil, and silane, we report the discovery of 5(12)6(2) and 5(12)6(8) amorphous silica polyhedra, each coexisting with the previously identified 5(12) cage, using combined cryo-transmission electron microscopy (cryo-TEM) and single-particle reconstruction (SPR). Notably, the 5(12)6(8) polyhedron represents a topology not previously observed in mesoporous silica frameworks. Control over structural features is demonstrated, and insights into cage formation mechanisms are provided. Structural outcomes are summarized in a ternary morphology diagram alongside prior results, bridging serendipitous discovery and intentional design of silica cages displaying a level of control over silica polymerization rivaling nature.
Jang, Dong June [Department of Materials Science a↗
A parallel and performance portable implementation of a full-field crystal plasticity model
We have developed a parallel implementation of an Elasto-Viscoplastic Fast Fourier Transform-based (EVPFFT) micromechanical solver to enable computationally efficient crystal plasticity modeling for polycrystalline materials. Our primary focus lies in achieving performance portability, allowing a single EVPFFT implementation to run optimally on various homogeneous architectures, including multi-core Central Processing Units (CPUs), as well as on heterogeneous computer architectures comprising multi-core CPUs and Graphics Processing Units (GPUs) from different vendors. To accomplish this goal, we have leveraged MATAR, a C++ software library that simplifies the creation and utilization of multidimensional dense or sparse matrix and array data structures. These data structures are designed to be portable across diverse architectures through the use of Kokkos, a performance-portable library. Additionally, we have employed the Message Passing Interface (MPI) to efficiently distribute the computational workload among processors. The heFFTe (Highly Efficient FFT for Exascale) library is used to facilitate the performance portability of the fast Fourier transforms (FFTs) computation. The computational performance of EVPFFT is evaluated and presented in terms of parallel scalability and simulation runtime on different high-performance computing (HPC) architectures. As a result, the utility of the developed framework to efficiently simulate the micro-mechanical fields in polycrystalline microstructures in engineering applications is discussed.
36 MATERIALS SCIENCE↗
Comprehensive Material Characterization and Simultaneous Model Calibration for Improved Computational Simulation Credibility
Computational simulation is increasingly relied upon for high-consequence engineering decisions, and a foundational element to solid mechanics simulations is a credible material model. Our ultimate vision is to interlace material characterization and model calibration in a real-time feedback loop, where the current model calibration results will drive the experiment to load regimes that add the most useful information to reduce parameter uncertainty. The current work investigated one key step to this Interlaced Characterization and Calibration (ICC) paradigm, using a finite load-path tree to incorporate history/path dependency of nonlinear material models into a network of surrogate models that replace computationally-expensive finite-element analyses. Our reference simulation was an elastoplastic material point subject to biaxial deformation with a Hill anisotropic yield criterion. Training data was generated using either a space-filling or adaptive sampling method, and surrogates were built using either Gaussian process or polynomial chaos expansion methods. Surrogate error was evaluated to be on the order of 10 ⁻5 and 10 ⁻3 percent for the space-filling and adaptive sampling training data, respectively. Direct Bayesian inference was performed with the surrogate network and with the reference material point simulator, and results agreed to within 3 significant figures for the mean parameter values, with a reduction in computational cost over 5 orders of magnitude. These results bought down risk regarding the surrogate network and facilitated a successful FY22-24 full LDRD proposal to research and develop the complete ICC paradigm.
36 MATERIALS SCIENCE↗
A Co-Registered In-Situ and Ex-Situ Dataset from an Electron Beam Powder Bed Fusion Additive Manufacturing Process (Peregrine v2023-09)
This release contains a co-registered in-situ and ex-situ Peregrine dataset from a single Arcam Q10 Electron Beam Powder Bed Fusion (EB-PBF) Inconel 738 build. These data were collected at the Manufacturing Demonstration Facility (MDF) located at Oak Ridge National Laboratory (ORNL). The dataset includes layer-wise Near Infrared (NIR) in-situ imaging data, in-situ temporal sensor data, ex-situ X-Ray Computed Tomography (X-CT) scans, and the target part geometries. Additionally, anomaly detections produced by a trained Dynamic Segmentation Convolutional Neural Network (DSCNN) are provided.
36 MATERIALS SCIENCE↗
FAIR Data and Interpretable AI Framework for Architectured Metamaterials
Our interdisciplinary effort successfully generated FAIR (Findable, Accessible, Interoperable, and Reusable) benchmark datasets for mechanical metamaterials while introducing a novel Artificial Intelligence (AI) framework known as Learning Refined Compositional Rules (LRCR). This framework was specifically designed to bridge the gap across varying computational length scales and extract the underlying physical mechanisms that connect a material's structural geometry to its bulk acoustic properties. Historically, the discovery of such structured materials relied heavily on human intuition or opaque, black-box optimization algorithms that were difficult to generalize. By combining interpretable machine learning techniques with rigorous experimental validation, this project established clear, generalizable design guidelines for tuning wave dispersion and controlling vibrations. Ultimately, the public availability of these structured datasets and algorithms will significantly reduce computational costs and accelerate the design of advanced multi-functional acoustic devices, offering broad societal impacts across fields like aerospace engineering, telecommunications, and biomedical implant design.
36 MATERIALS SCIENCE↗
Energy Materials Chemistry Integrating Theory, Experiment and Data Science (Final Report)
The Energy Materials Chemistry Integrating Theory, Experiment and Data Science (EM-CITED) project is a multidisciplinary research effort focused on accelerating discovery of scientific knowledge via incorporation of data science and artificial intelligence in materials chemistry research. The project aims to advance materials chemistry-aware data science to unify theory and experiment knowledge streams. The work resulted in foundational AI frameworks for materials chemistry – Deep Reasoning Networks (DRNets), Hierarchical Correlation Learning for Multi-property Prediction (H-CLMP), and Material-to-Spectrum (Mat2Spec) prediction – as well as a host of strategies for accelerated scientific discoveries through principled incorporation of data science in computational and experimental research.
36 MATERIALS SCIENCE↗
Quantum Application Specifications and Benchmarks
This software describes computational tasks for quantum computers that are derived from LANL basic science research applications such as the modeling of materials, chemicals and compounds at atomic scales. The computations focus on quantum simulation tasks, such as quantum dynamics, thermal state preparation and ground state estimation. The primary function of the software is to develop estimates of the requirements for large-scale fault-tolerant quantum computers to solve these scientific computations. The secondary focus of the software are codes for assessing the limitations of conducting quantum computations on classical computers.
Coffrin, Carleton↗
Real time numerical computation of corrosion rates from corrosion sensors
A method for determining a corrosion rate includes receiving an input signal including corrosion data from a sensor. A signal-to-noise ratio (SNR) and a type of noise is determined from the input signal. A corrosion rate computation method is selected based on the SNR and the type of noise. The corrosion rate is determined, using a processor, based on a selected corrosion rate computation method.
36 MATERIALS SCIENCE↗
Prediction of Probabilistic Shock Initiation Thresholds of Energetic Materials Through Evolution of Thermal-Mechanical Dissipation and Reactive Heating
The ignition threshold of an energetic material (EM) quantifies the macroscopic conditions for the onset of self-sustaining chemical reactions. The threshold is an important theoretical and practical measure of material attributes that relate to safety and reliability. Historically, the thresholds are measured experimentally. In this work, we present a new Lagrangian computational framework for establishing the probabilistic ignition thresholds of heterogeneous EM out of the evolutions of coupled mechanical-thermal-chemical processes using mesoscale simulations. Furthermore, the simulations explicitly account for microstructural heterogeneities, constituent properties, and interfacial processes and capture processes responsible for the development of material damage and the formation of hotspots in which chemical reactions initiate. The specific mechanisms tracked include viscoelasticity, viscoplasticity, fracture, post-fracture contact, frictional heating, heat conduction, reactive chemical heating, gaseous product generation, and convective heat transfer. To determine the ignition threshold, the minimum macroscopic loading required to achieve self-sustaining chemical reactions with a rate of reactive heat generation exceeding the rate of heat loss due to conduction and other dissipative mechanisms is determined. Probabilistic quantification of the processes and the thresholds are obtained via the use of statistically equivalent microstructure sample sets (SEMSS). The predictions are in agreement with available experimental data.
36 MATERIALS SCIENCE↗
Basin-Size Mapping: Prediction of Metastable Polymorph Synthesizability Across TaC–TaN Alloys
The sizes of the basins of attraction on the potential energy surface are helpful indicators in determining the experimental synthesizability of metastable phases. In principle, these basins can be controlled with changes in thermodynamic conditions such as composition, pressure, and surface energy. Herein, we use random structure sampling to computationally study how alloying smoothly perturbs basin of attraction sizes. The TaC 1-x N x pseudobinary is an ideal test system given the structural and polymorphic contrast of its parent compounds and their technological relevance as epitaxial substrates for Al 1-x Ga x N. While we find limited thermodynamic stability across all computationally observed phases, random structure sampling shows a significant composition region where the rocksalt basin dominates. As such, we predict the potential for the nonequilibrium synthesis of metastable rocksalt TaC 1-x N x alloys as substrates for Al 1-x Ga x N. At higher nitrogen concentrations, other low-energy metastable polymorphs emerge that continue to retain the hexagonal close packing suitable for III-N growth. Confidence in these trends was established through uncertainty quantification of the basin sizes and energy distributions; such analysis utilized the Beta and Dirichlet distributions. In conclusion, we also find (a) polymorph basin sizes can be rationalized in terms of energetic preferences for different coordination environments; and (b) basin sizes universally shrink with increasing nitrogen content, making the system more prone to amorphous growth.
36 MATERIALS SCIENCE↗
Calculating Radiation Damage (DPA) from Transmutation Products
This is a poster for an INL poster session. Accurate models for radiation damage are crucial for predicting material performance in radiation environments. The uncertainty of state-of-the-art radiation damage models is large, contributing to excessive safety margins. A major source of this uncertainty is neglecting the effect that transmutation products have on radiation damage. Transmutation products are new nuclides formed by neutron activation during irradiation; they can contribute to radiation damage by additional neutron capture or decay events. Ignoring the contribution of transmutation products leads to a significant underprediction of the radiation damage (e.g., >10% error in 316 stainless steel). This underprediction is accounted for in part by adding larger safety margins to designs. Currently, the state of the art explicitly accounts for only a single transmutation product, namely nickel-59, during the radiation damage calculation. All other transmutation products are assumed to not contribute to the radiation damage, because there is currently no established method to systematically track all or a selection of radiation damage contributions of transmutation products during activation. In the case of nickel-59, the current method is to apply a precalculated correlation that cannot be used for any other nuclide and is largely dependent on all nuclear engineers being experts in this niche topic. This project proposed to methodically find other transmutation products that cause significant radiation damage, and then to develop a general framework for systematically tracking the radiation damage from these nuclides. This was accomplished by combining the radiation damage calculation into the transmutation calculation already performed for irradiated structural materials. The key idea of our framework is to introduce radiation-damage "pseudo-nuclides" to the list of nuclides used in the transmutation analysis. This allows radiation damage to be tracked alongside the creation and destruction of transmutation products. The main deliverable of this project is a general framework for computing radiation damage while the damaged material undergoes transmutation; this capability allows a significantly more accurate estimation of radiation damage, and in turn reduce required safety margins thereby reducing the cost to construct reactors.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
A Finite Difference informed Random Walk solver for simulating radiation defect evolution in polycrystalline structures with strongly inhomogeneous diffusivity
Diffusivity of species and defects on grain boundaries is usually several orders of magnitude larger than that inside grains. Such strongly inhomogeneous diffusivity requires prohibitively high computational demands for modeling microstructural evolution. Here, this paper presents a highly-efficient numerical solver, combining the Finite Difference method and Random Walk model, designed for accurately modeling strongly inhomogeneous diffusion within polycrystalline structures. The proposed solver, termed Finite Difference informed Random Walk (FDiRW), integrates a customized Finite Difference (cFD) scheme tailored for fast diffusion along thin grain boundaries represented by a single-layer of nodes. Numerical experiments demonstrate that the FDiRW solver achieves an impressive efficiency gain of 1560x compared to traditional Finite Difference methods while maintaining accuracy, making it feasible for personal computer machines to handle diffusional systems with strongly inhomogeneous diffusivity across static polycrystalline microstructures. The model has been successfully applied to simulate radiation defect evolution, showcasing its scalability to engineering scales in both length and time dimensions.
36 MATERIALS SCIENCE↗
Computational models and experimental studies of mold filling in thin channels with yield stress fluids [Slides]
We propose developing numerical methods informed by novel experimental diagnostics that transition from solid-to-fluid, while accurately predicting the stress and deformation regardless of phase.
36 MATERIALS SCIENCE↗
JARVIS-Leaderboard: a large scale benchmark of materials design methods
Abstract Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many fields. Materials science, in particular, encompasses a variety of experimental and theoretical approaches that require careful benchmarking. Leaderboard efforts have been developed previously to mitigate these issues. However, a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with perfect and defect materials data is still lacking. This work introduces JARVIS-Leaderboard, an open-source and community-driven platform that facilitates benchmarking and enhances reproducibility. The platform allows users to set up benchmarks with custom tasks and enables contributions in the form of dataset, code, and meta-data submissions. We cover the following materials design categories: Artificial Intelligence (AI), Electronic Structure (ES), Force-fields (FF), Quantum Computation (QC), and Experiments (EXP). For AI, we cover several types of input data, including atomic structures, atomistic images, spectra, and text. For ES, we consider multiple ES approaches, software packages, pseudopotentials, materials, and properties, comparing results to experiment. For FF, we compare multiple approaches for material property predictions. For QC, we benchmark Hamiltonian simulations using various quantum algorithms and circuits. Finally, for experiments, we use the inter-laboratory approach to establish benchmarks. There are 1281 contributions to 274 benchmarks using 152 methods with more than 8 million data points, and the leaderboard is continuously expanding. The JARVIS-Leaderboard is available at the website: https://pages.nist.gov/jarvis_leaderboard/
36 MATERIALS SCIENCE↗
Machine learning guided prediction of solute segregation at coherent and semi-coherent metal/oxide interfaces
Investigation of semi-coherent metal/oxide interfaces with misfit dislocations using density functional theory (DFT) is computationally intensive to the point of being prohibitive, as it involves several hundreds to many thousands of atoms. In this study, we examined the solute segregation behavior at the Fe/Y 2 O 3 interface—a model interface for cladding applications in nuclear fission reactors—using a combination of DFT calculations and machine learning (ML) approaches. Both coherent and semi-coherent interfaces were considered. ML models were trained on DFT-calculated segregation energies to identify the key chemical, geometric and strain energy related features that govern solute segregation behavior at coherent Fe/Y 2 O 3 interfaces. Furthermore, it was found that ML models when trained on DFT calculated segregation energy of elements at a coherent interface, comprising of about a hundred-atom supercell, can predict the segregation energy of elements at a semi-coherent Fe/Y 2 O 3 interface (with multiple hundreds of atoms) at a fraction of computational cost (1/35th), with an accuracy comparable to DFT calculations.
11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Acceleration of Thermochemistry Solves in MOOSE and Pronghorn
This work focuses on the development and implementation of strategies to accelerate thermochemical calculations within MOOSE-based multiphysics simulations, particularly for applications in MSRs. We highlight the inherent complexity of nuclear materials, which require a multiscale approach to accurately model their behavior across various physical domains, including mechanical, chemical, and thermal phenomena. Thermochemical equilibrium calculations are crucial for predicting material properties and enhancing the fidelity of these simulations. The integration of Thermochimica, a Gibbs energy minimizer, into MOOSE allows for the direct minimization of Gibbs energy at every point on the mesh. However, the computational cost of such integration is significant. To address this, we explored acceleration strategies such as multi-threading support and the use of a thermodynamic ValueCache to reduce redundant calculations. Additionally, we investigated modifications to Thermochimica to enable phase constraints and improve its coupling with phase-field models, which are essential for simulating microstructural evolution and corrosion in MSR. These efforts aim to optimize the computational efficiency and accuracy of multiphysics simulations, thereby supporting the development of reliable and efficient nuclear materials for next-generation reactor technologies.
36 - MATERIALS SCIENCE↗