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63 records · Page 4

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DOE OSTI2025

Towards robust surrogate models: Benchmarking machine learning approaches to expediting phase field simulations of brittle fracture

Data-driven approaches have the potential to make modeling complex, nonlinear physical phenomena significantly more computationally tractable. For example, computational modeling of fracture is a core challenge where machine learning techniques have the potential to provide a much needed speedup that would enable progress in areas such as multi-scale modeling and uncertainty quantification. Currently, phase field modeling (PFM) of fracture is one such approach that offers a convenient variational formulation to model crack nucleation, branching and propagation. To date, machine learning techniques have shown promise in approximating PFM simulations. While standard fracture benchmarks represent realistic scenarios frequently observed in practice, they typically do not provide sufficiently challenging tests for data-driven methods. Here, to address this gap, we introduce a challenging dataset based on PFM simulations designed to benchmark and advance ML methods for fracture modeling. This dataset includes three energy decomposition methods, two boundary conditions, and 1000 random initial crack configurations for a total of 6000 simulations. Each sample contains 100 time steps capturing the temporal evolution of the crack field. Alongside this dataset, we also implement and evaluate Physics Informed Neural Networks (PINN), Fourier Neural Operators (FNO), and UNet models as baselines, and explore the impact of ensembling strategies on prediction accuracy. With this combination of our dataset and baseline models drawn from the literature we aim to provide a standardized and challenging benchmark for evaluating machine learning approaches to solid mechanics. Our results highlight both the promise and limitations of popular current models, and demonstrate the utility of this dataset as a testbed for advancing machine learning in fracture mechanics research.

Benchmark dataset↗

DOE OSTI2025

A machine learning approach to quantify degradation of nuclear fuels and the effects of fission products

Nuclear fuel performance is critically dependent on understanding the evolution of fuel properties under operational conditions, a complex challenge driven by chemical changes and substantial radiation damage during fission. Traditionally, property evolution has been determined via empirical data collected following irradiation. However, these empirical correlations are limited in their applicability beyond the specific conditions in which they were obtained. This study explores a novel approach to address this challenge by applying materials informatics to develop a machine learning random forest (ML-RF) model that captures the effects of fission products on fuel compounds. The model predicts formation enthalpy (ΔH f ) by leveraging extensive quantum materials property data and correlating it with material descriptors such as composition, atomic and site features, and crystal lattice properties. This ML-RF model enables rapid interpolation across the compositional and structural spaces covered by the training data, thus supporting high-throughput screening and energetic ranking of candidate phases. The model demonstrates the ability to predict ΔH f with a mean absolute error (MAE) of approximately 0.1 to 0.2 eV/atom across a wide range of compounds, including key nuclear fuel systems (U-O, U-N, U-C, U-Si, and U-Mo). For example, it was used to assess shifts in stoichiometry for UO 2 (O/M) and UN (N/M) fuels, revealing their distinct tendencies in chemical potential variation and enabling preliminary convex hull analyses. Furthermore, the model provides insights into how individual fission products affect fuel properties. Results indicate that larger fission products (e.g., Nd, Pu, Ce) have a more pronounced impact on UO 2 , while lighter ones (e.g., Zr) strongly influence UN. Here, the model developed in this work can be used to support the Accelerated Fuel Qualification approach by facilitating preliminary evaluations prior to extensive materials modeling and experimentation. To this end, the trained model has been made available to the fuel community to support ongoing fuel development efforts.

Accelerated fuel qualification↗

DOE OSTI2026

Surface Reconstruction in Hydrated Amphiphilic Block Copolymer Thin Films Probed by Fluid Cell Atomic Force Microscopy

In many thin film materials, nuanced interplays of interfacial energies control the surface morphology and rearrangement. This work evaluates polymer−solvent interactions and solvent-driven surface reconstructions via ex situ and in situ fluid cell Atomic Force Microscopy (fc-AFM) analysis of amphiphilic block copolymer (BCP) thin films upon exposure to deionized (DI) water. We examine the differences in surface morphology, whole-film swelling, and force response in thin films of polystyrene-block-poly(ethylene oxide) (PS-b-PEO) and polystyrene- block-poly[(allyl glycidyl ether)-co-(ethylene oxide)] (PS-b- P[AGE-co-EO]) processed into standing-up cylinder morphologies perpendicular to a silicon substrate (⊥C). Using Amplitude Modulation AFM (AM-AFM) and Amplitude-Phase Distance (APD) force spectroscopy, this work probes the mechanoresponsive nature of the dynamic surface layers of these films, unveiling surface layer stratification and surface chain rearrangement via minimal tip−sample stimulation. To help rationalize the observed reconfigurations, the energetic driving forces were estimated using the harmonic mean approximations of interfacial energies. Given the nonionizable nature of the minority P(AGE-co-EO) block and the energetic driving forces for chain mobility, this work shows how the elimination of unfavorable PS−water interfaces drives chain rearrangement and coverage of the PS surface by chains of the hydrophilic block. This work highlights considerations for increasing the heterogeneity and complexity of BCP thin films via random blocks and how those changes to local interfacial energies may drive larger scale film morphology reconstructions, with broader implications for tuning interface hydrophilicity.

Copolymers↗

DOE OSTI2024

GLAD-M35: a joint P and S global tomographic model with uncertainty quantification

We present our third and final generation joint P and S global adjoint tomography (GLAD) model, GLAD-M35, and quantify its uncertainty based on a low-rank approximation of the inverse Hessian. Starting from our second-generation model, GLAD-M25, we added 680 new earthquakes to the database for a total of 2160 events. New P-wave categories are included to compensate for the imbalance between P- and S-wave measurements, and we enhanced the window selection algorithm to include more major-arc phases, providing better constraints on the structure of the deep mantle and more than doubling the number of measurement windows to 40 million. Two stages of a Broyden–Fletcher–Goldfarb–Shanno (BFGS) quasi-Newton inversion were performed, each comprising five iterations. With this BFGS update history, we determine the model’s standard deviation and resolution length through randomized singular value decomposition.

58 GEOSCIENCES↗

DOE OSTI2026

Model Parameter Development for Complex Materials: Species-Specific Diffusion Barriers in 316 Stainless Steel from Systematic DFT Calculations

Vacancy-mediated diffusion barriers in 316 stainless steel have been systematically calculated using density functional theory to provide essential parameters for mesoscale microstructure evolution models. A statistical sampling approach employing 210 nudged elastic band calculations across multiple special quasi-random structures captures the effects of local chemical environments in this concentrated alloy. The computational methodology addresses challenges specific to chemically disordered systems, including proper magnetic treatment throughout multi-step calculations and validation against experimental structural properties. The calculated activation barriers reveal clear species-dependent diffusion behavior with the hierarchy Ni >> Fe ˜ Cr >> Mo. Nickel exhibits the highest barriers (0.74–1.31 eV, mean 1.045 eV), confirming its role as the slowest-diffusing major component. Iron and chromium show similar moderate barriers averaging 0.587 eV and 0.522 eV, respectively. Remarkably, molybdenum demonstrates exceptionally low barriers (0.12–0.28 eV, mean 0.194 eV), suggesting much higher mobility than previously recognized and potentially significant implications for precipitation kinetics and microstructure evolution. The barrier ranges remain consistent across different 316 SS compositions, supporting parameter transferability for modeling applications. The overall mean barrier of 0.64 eV provides a practical approximation for phase field simulations, while species-specific values enable detailed treatments of diffusion-controlled processes. This systematic approach establishes a validated framework for generating diffusion parameters in other concentrated alloys where experimental data are limited, while providing the first systematic set of species-specific barriers for predictive modeling of 316 stainless steel microstructure evolution.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Anomalous elastic softening in ferroelectric hafnia under pressure

his study employs first-principles density-functional theory (DFT) calculations to explore the elastic and mechanical properties of ferroelectric hafnia (HfO 2 ) in its polar orthorhombic 𝑃⁢𝑐⁢𝑎⁢2 1 phase under varying hydrostatic pressure conditions up to 30 GPa. Utilizing a plane-wave basis set and Perdew-Burke-Ernzerhof generalized-gradient approximation for solids in our DFT calculations, we investigate both pure and yttrium-substituted HfO 2 . Our findings reveal an anomalous reduction in the 𝐶 33 component of the elastic tensor with increasing pressure, which becomes significant above 15 GPa and signals a potential pressure-driven structural phase transition at higher pressure. The analysis of atomic displacements under pressure sheds light on the unusual mechanical behavior and phase stability of this material. Additionally, we observe a transition from an indirect band gap to a direct band gap with increasing pressure, which could have significant implications for optical applications. Here, the effects of yttrium substitution on the mechanical and electronic properties are further examined, revealing that yttrium substitution softens the elastic response of this material and reduces the electronic band gap. These results enhance our understanding of elastic and mechanical responses of ferroelectric hafnia and its potential for applications in microelectronics, piezoelectric devices, and nonvolatile ferroelectric random-access memories. Further experimental validation is recommended to confirm our predictions and explore the practical implications of the observed phase transitions and electronic behavior of the ferroelectric hafnia under high-pressure conditions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

Understanding vortex dynamics in CaK(Fe,Ni) 4 As 4 and Ba(Fe,Co) 2 As 2 single crystals under the influence of random point disorder

We report on the influence of doping on vortex dynamics in 3 MeV proton-irradiated single crystals of CaK(Fe 1–x Ni x ) 4 As 4 (1144, x = 0.015, 0.025, and 0.03) and Ba(Fe 1–x Co x ) 2 As 2 (x = 0.04, 0.062, 0.066 and 0.074). Non-irradiated crystals of the 1144 system display superconducting critical temperatures ranging from 31 K for x = 0.015–20.5 K, as doping increases to 0.03. On the other hand, pristine crystals of the 122 system show T c values between 14.6 and 23.6 K, with the maximum T c occurring at intermediate doping levels. The fluence was set at 3 × 10 16 p cm –2 , resulting in a decrease in the T c by around 1.5 K for all samples and significantly affecting the vortex dynamics by reducing the flux creep relaxation compared to previously reported values for unirradiated crystals. Parameters such as vortex pinning energy U 0 and the glassy exponent μ dependencies on doping and magnetic field strength are identified. For the 1144 system, U 0 reaches values approaching 500 K for small fields in samples with T c = 29.3 K (x = 0.015), systematically decreasing to around 200 K as T c falls below 20 K. Furthermore, U 0 decreases as the field increases to 3 T for the same sample, varying from approximately 250 K to 100 K as T c decreases. These changes are typically accompanied by modifications in μ, gradually increasing from values around 1 towards 1.5, corresponding to small bundle relaxation in the collective creep theory. Despite differences in the substitutional disorder and magnetic phase diagram with respect to the 1144 system, the results for 122 single crystals follow a similar tendency in which U 0 usually reduces and μ increase rise as the applied magnetic field is increased. Due to moderate U 0 in these systems (few hundreds of kelvins), the resulting decay of persistent current at liquid helium temperatures is primarily determined by a balance between U 0 and bundle size contribution. Furthermore, these findings provide valuable insights for potential applications of these systems, particularly in the context of intrinsic superconducting parameters and the resulting pinning landscape.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

A graphics processing unit accelerated sparse direct solver and preconditioner with block low rank compression

We present the GPU implementation efforts and challenges of the sparse solver package STRUMPACK. The code is made publicly available on github with a permissive BSD license. STRUMPACK implements an approximate multifrontal solver, a sparse LU factorization which makes use of compression methods to accelerate time to solution and reduce memory usage. Multiple compression schemes based on rank-structured and hierarchical matrix approximations are supported, including hierarchically semi-separable, hierarchically off-diagonal butterfly, and block low rank. Here, in this paper, we present the GPU implementation of the block low rank (BLR) compression method within a multifrontal solver. Our GPU implementation relies on highly optimized vendor libraries such as cuBLAS and cuSOLVER for NVIDIA GPUs, rocBLAS and rocSOLVER for AMD GPUs and the Intel oneAPI Math Kernel Library (oneMKL) for Intel GPUs. Additionally, we rely on external open source libraries such as SLATE (Software for Linear Algebra Targeting Exascale), MAGMA (Matrix Algebra on GPU and Multi-core Architectures), and KBLAS (KAUST BLAS). SLATE is used as a GPU-capable ScaLAPACK replacement. From MAGMA we use variable sized batched dense linear algebra operations such as GEMM, TRSM and LU with partial pivoting. KBLAS provides efficient (batched) low rank matrix compression for NVIDIA GPUs using an adaptive randomized sampling scheme. The resulting sparse solver and preconditioner runs on NVIDIA, AMD and Intel GPUs. Interfaces are available from PETSc, Trilinos and MFEM, or the solver can be used directly in user code. We report results for a range of benchmark applications, using the Perlmutter system from NERSC, Frontier from ORNL, and Aurora from ALCF. For a high frequency wave equation on a regular mesh, using 32 Perlmutter compute nodes, the factorization phase of the exact GPU solver is about 6.5× faster compared to the CPU-only solver. The BLR-enabled GPU solver is about 13.8× faster than the CPU exact solver. For a collection of SuiteSparse matrices, the STRUMPACK exact factorization on a single GPU is on average 1.9× faster than NVIDIA’s cuDSS solver.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Experimental and computational studies on high-entropy carbide MoNbTaVWC 5 under high pressures

High-entropy carbide, MoNbTaVWC 5 , was synthesized from oxide precursors of the constituent metals, mixed with graphite powder in a microwave-generated hydrogen plasma at 26.66 kPa and 2100 °C. Ambient x-ray diffraction analysis confirms the full conversion of oxide precursors into a single-phase, face-centered cubic structure with a lattice parameter a = 4.3309 Å. Nanoindentation measured a hardness of 24.5 ± 1.3 GPa and an elastic modulus of 386 ± 22 GPa. The synthesized sample, mixed with a copper pressure marker, was studied by the radial x-ray diffraction technique with beryllium gasketing in a diamond anvil cell up to 70 GPa. The experimentally measured pressure–volume curve and shear strength were compared with theoretical predictions using the special quasi-random structure technique and density functional theory. MoNbTaVWC 5 achieved a 12% volume compression at 70 GPa and exhibited a high shear strength of 6.6 GPa. The present study demonstrates that the high-entropy carbide MoNbTaVWC 5 exhibits exceptional incompressibility and high strength under extreme conditions.

36 MATERIALS SCIENCE↗