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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

MS25: Materials Science-Focused Benchmark Data Set for Machine Learning Interatomic Potentials

Here, we present MS25, a benchmark data set for evaluating machine learning interatomic potentials (MLIPs) across diverse materials-relevant systems including MgO surfaces, liquid water, zeolites, a catalytic Pt surface reaction, high-entropy alloys (HEAs), and disordered Zr-oxides. Five MLIP architectures (MACE, NequIP, Allegro, MTP, and Torch-ANI) are trained and tested, focusing not only on traditional metrics (energies, forces, and stresses) but also explicitly validating derived physical observables such as lattice constants, volumes, and reaction barriers. We find that most models reach comparable accuracy on standard error metrics across the simple systems, although equivariant MLIPs offer 1.5–2× improvements over nonequivariant MLIPs in energy and force error for structurally complex or compositionally disordered environments such as HEAs and Zr–O systems. Our analysis highlights that low errors in energy and force predictions do not guarantee reliable observables, emphasizing the necessity of explicit validation. We demonstrate limitations in cross-framework transferability, as models trained on one zeolite framework (CHA) fail to reliably generalize to predictions of structurally distinct frameworks (e.g., MFI). Size-extensive tests show some dependence on system size for MgO, resulting from forced periodicity. The HEA and Zr–O data sets are identified as challenging tests for future benchmarks and MLIP model architecture developments as they show significant differentiation in error between MLIP architectures and are still relatively difficult at 1000 training images. Moving forward, we recommend that benchmarking efforts shift their focus from marginal accuracy improvements in energy and force errors toward identifying and understanding model failure modes, rigorously assessing transferability, and evaluating how their errors affect observable predictions. For researchers looking to choose an MLIP architecture, we suggest selecting equivariant MLIP architectures if the complexity of the system is a challenge. For simple materials problems, auxiliary features such as integration with molecular dynamics engines, trade-offs between computational data set generation cost vs MLIP inference speed, and framework integration may play a more important decision factor than small differences in error metrics that are unlikely to matter for production-level research.

chemical structure↗
DOE OSTI2025

A Machine Learning Framework for Modeling Ensemble Properties of Atomically Disordered Materials

Atomic disorder can strongly influence material properties such as charge transport, optical response, and catalytic activity. However, efficiently modeling these disorder effects remains challenging for first-principles methods due to the cost of sampling large configurational spaces and computing complex physical quantities. Recent advances of machine learning techniques, particularly graph neural networks (GNNs), has enabled the efficient and accurate predictions of complex material properties, offering promising tools for studying disordered systems. In this work, we present a general machine-learning-assisted computational framework that integrates equivariant GNNs with Monte Carlo simulations to compute the thermodynamic and ensemble-averaged functional properties of disordered materials. Using the surface-termination-disordered MXene monolayer Ti 3 C 2 T 2–x as a representative system, we find that electrical conductivity exhibits an emergent peak near the order–disorder phase transition temperature due to the interplay between electron scattering and doping. In contrast, optical conductivity remains largely insensitive to local atomic disorder and reflects the global surface chemical composition. These results highlight the role of atomic disorder in affecting material properties and demonstrate the potential of our approach for statistically modeling disorder effects in a wide range of materials such as high-entropy alloys and spin liquids.

MXene↗
DOE OSTI2022

Sampling lattices in semi-grand canonical ensemble with autoregressive machine learning

Calculating thermodynamic potentials and observables efficiently and accurately is key for the application of statistical mechanics simulations to materials science. However, naive Monte Carlo approaches, on which such calculations are often dependent, struggle to scale to complex materials in many state-of-the-art disciplines such as the design of high entropy alloys or multi-component catalysts. To address this issue, we adapt sampling tools built upon machine learning-based generative modeling to the materials space by transforming them into the semi-grand canonical ensemble. Furthermore, we show that the resulting models are transferable across wide ranges of thermodynamic conditions and can be implemented with any internal energy model U, allowing integration into many existing materials workflows. We demonstrate the applicability of this approach to the simulation of benchmark systems (AgPd, CuAu) that exhibit diverse thermodynamic behavior in their phase diagrams. Finally, we discuss remaining challenges in model development and promising research directions for future improvements.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Quantum annealing-assisted lattice optimization

High Entropy Alloys (HEAs) have drawn great interest due to their exceptional properties compared to conventional materials. The configuration of HEA system is considered a key to their superior properties, but exhausting all possible configurations of atom coordinates and species to find the ground energy state is extremely challenging. In this work, we proposed a quantum annealing-assisted lattice optimization (QALO) algorithm, which is an active learning framework that integrates the Field-aware Factorization Machine (FFM) as the surrogate model for lattice energy prediction, Quantum Annealing (QA) as an optimizer and Machine Learning Potential (MLP) for ground truth energy calculation. By applying our algorithm to the NbMoTaW alloy, we reproduced the Nb depletion and W enrichment observed in bulk HEA. We found our optimized HEAs to have superior mechanical properties compared to the randomly generated alloy configurations. Our algorithm highlights the potential of quantum computing in materials design and discovery, laying a foundation for further exploring and optimizing structure-property relationships.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Accurate and uncertainty-aware multi-task prediction of HEA properties using prior-guided deep Gaussian processes

Surrogate modeling techniques have become indispensable in accelerating the discovery and optimization of high-entropy alloys (HEAs), especially when integrating computational predictions with sparse experimental observations. This study systematically evaluates the training and testing performance of four prominent surrogate models—conventional Gaussian processes (cGP), Deep Gaussian processes (DGP), encoder-decoder neural networks for multi-output regression and eXtreme Gradient Boosting (XGBoost)—applied to a hybrid dataset of experimental and computational properties of the 8-component HEA system Al-Co-Cr-Cu-Fe-Mn-Ni-V. We specifically assess their capabilities in predicting correlated material properties, including yield strength, hardness, modulus, ultimate tensile strength, elongation, and average hardness under dynamic/quasi-static conditions, alongside auxiliary computational properties. The comparison highlights the strengths of hierarchical deep modeling approaches in handling heteroscedastic, heterotopic, and incomplete data commonly encountered in materials science. Our findings illustrate that combined surrogate models such as DGPs infused with machine-learned priors outperform other surrogates by effectively capturing inter-property correlations and by assimilating prior knowledge. This enhanced predictive accuracy positions the combined surrogate models as powerful tools for robust and data-efficient materials design.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Development of interatomic potential and effect of ordering on defect properties in CrMnV

Developing materials that can withstand extreme environments, such as high radiation doses and elevated temperatures, is crucial for next-generation particle accelerators, including the 2.4 MW Long-Baseline Neutrino Facility. High-Entropy Alloys have emerged as promising candidates for beam window materials due to their superior mechanical strength, corrosion resistance, and radiation tolerance. In this study, we focus on the Cr–Mn–V alloy system, developing and employing machine-learning interatomic potentials (MLIPs) to investigate the formation of an ordered phase and its influence on defect properties. Using hybrid Monte Carlo-Molecular Dynamics simulations, we observe the formation of a B2-ordered phase at lower temperatures, consistent with Density Functional Theory (DFT) predictions. Ordered structures display a bimodal distribution of migration energies and reduced mean square displacement values, indicating suppressed vacancy diffusion. Our results also show that the migration energy barrier varies based on the atomic species, with Mn and V exhibiting the highest and lowest average barriers, respectively. These findings suggest that atomic ordering inhibits defect mobility, potentially enhancing the radiation resistance of CrMnV alloys. The validated MLIP provides a reliable framework for simulations that are faster than traditional DFT while maintaining the accuracy required to study defect and ordering properties.

36 MATERIALS SCIENCE↗
DOE OSTI2020

The emergence of small-scale self-affine surface roughness from deformation

Most natural and man-made surfaces appear to be rough on many length scales. There is presently no unifying theory of the origin of roughness or the self-affine nature of surface topography. One likely contributor to the formation of roughness is deformation, which underlies many processes that shape surfaces such as machining, fracture, and wear. Using molecular dynamics, we simulate the biaxial compression of single-crystal Au, the high-entropy alloy Ni 36.67 Co 30 Fe 16.67 Ti 16.67 , and amorphous Cu 50 Zr 50 and show that even surfaces of homogeneous materials develop a self-affine structure. By characterizing subsurface deformation, we connect the self-affinity of the surface to the spatial correlation of deformation events occurring within the bulk and present scaling relations for the evolution of roughness with strain. These results open routes toward interpreting and engineering roughness profiles.

42 ENGINEERING↗
DOE OSTI2021

Control of the Plasma-Material Interface for Long Pulse Optimization in EAST and KSTAR

This project's major goal was to understand and control the plasma-material interface to improve long pulse discharge and performance in the EAST and KSTAR devices, emphasizing the unique strengths of each machine. In particular, MIT has developed implanted depth markers to diagnose the net erosion of high-Z materials. The goals included the characterization and utilization of these markers in various high-Z materials of interest, including TZM, tungsten, and refractory metal base high entropy alloys.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Differential Scanning Calorimetry Calibration and Heat Capacity

The differential scanning calorimeter (DSC) 404 F3 is a machine that heats up to 1500 degrees Celsius, and collects/evaluates thermal properties such as heat capacity, crystallization, glass transition, and more. The first part of the project is based around calibrating the DSC 404 F3 using the given procedures and manuals. This ensures that the DSC 404 F3 is working properly and does not have any issues moving forward. After the calibration is complete, the second part is to start testing. There will be tests conducted on known metals, such as steel and aluminum, in which there is data to compare it to. If the data collected is not accurate compared to its known values, further troubleshooting will need to ensue to ensure that the following process does not give wrong readings. Then, testing will begin for High Entropy Alloys(HEA) and the interesting issue with these is that there is no data to compare these to. The goal of finding the values of the HEA is to find a compatible alloy that we can use in future projects.

Szczech, Sebastian↗
DOE OSTI2024

High-Power Targetry R&D for Next-Generation Accelerator Target Facilities

Beam-intercepting devices such as beam windows and particle-production targets are critical components of accelerator target facilities for High Energy Physics (HEP) experiments. The high-power, pulsed structure of the particle beams used for these experiments leads to thermal shock and high-cycle fatigue in addition to radiation damage resulting from the accumulated particle fluence. This can lead to degradation of the target system s mechanical and thermal properties; considerably reducing their lifetimes and presenting substantial challenges to reliable operation of multi-MW class facilities. Recently several major accelerator facilities have been forced to operate at reduced power levels due to target survivability concerns. Furthermore, at Fermilab it is planned to increase the neutrino production beam power up to 2.4 MW in coming years. Therefore, timely R&D on the irradiated behavior of target system materials is critical to efficient operation of accelerator facilities and full utilization of recent accelerator power upgrades for HEP research. This talk will begin with an overview of high-power targetry, and the significant challenges presented by beam power increases expected for future HEP experiments. We will then cover several past materials irradiation studies that have been completed by the High-Power Targetry R&D group at Fermilab and its collaborators on common accelerator and target materials such as graphite, beryllium, titanium, and tungsten. Finally, we will conclude with a discussion of two novel materials investigations under way within the group; high-entropy alloys for beam window applications, and electrospun nanofibers to serve as particle production targets.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

Finite Element Analysis and Measurement of Coefficients of Thermal Expansion on HEAs

This poster covers the introducing why Finite Element Analysis is essential in understanding HiRadMat. It also covers on the experimental procedures and obtained result of the measurement of Coefficient of thermal expansion (CTE) of High Entropy Alloys, and the relevance of these results in application of selecting beam intercepting devices

Itemba, Leo [U. Chicago (main)]↗
DOE OSTI2025

CRADA 2022-03 Final Report: Advanced Bearing Materials for Harsh Service Conditions

Develop advanced bearing materials for hydrogen gas turbines and wind turbines. Two candidate materials have been selected based on Ames lab’s recent work on high entropy alloys and shape memory alloys. These materials are designed to exhibit a balance of hardness, strength, toughness, and corrosion resistance. The hydrogen diffusion coefficient in the NiTi based shape memory alloy is two order of magnitude lower than that of the conventional case hardened bearing steel. We plan to atomize these materials into powders, additive manufacture cylindrical bearings, then test these bearing for rolling-contact fatigue, corrosion properties, and hydrogen embrittlement. We are a vertically integrated team comprises of national labs, bearing manufacturers, raw material powders makers, and aircraft gas turbine manufacturer.

Argibay, Nicolas [Ames Laboratory (AMES), Ames, IA↗
DOE OSTI2022

Vibrational Entropy of Crystalline Solids from Covariance of Atomic Displacements

The vibrational entropy of a solid at finite temperature is investigated from the perspective of information theory. Ab initio molecular dynamics (AIMD) simulations generate ensembles of atomic configurations at finite temperature from which we obtain the N-body distribution of atomic displacements, ρN. We calculate the information-theoretic entropy from the expectation value of lnρN. At a first level of approximation, treating individual atomic displacements independently, our method may be applied using Debye–Waller B-factors, allowing diffraction experiments to obtain an upper bound on the thermodynamic entropy. At the next level of approximation we correct the overestimation through inclusion of displacement covariances. We apply this approach to elemental body-centered cubic sodium and face-centered cubic aluminum, showing good agreement with experimental values above the Debye temperatures of the metals. Below the Debye temperatures, we extract an effective vibrational density of states from eigenvalues of the covariance matrix, and then evaluate the entropy quantum mechanically, again yielding good agreement with experiment down to low temperatures. Our method readily generalizes to complex solids, as we demonstrate for a high entropy alloy. Further, our method applies in cases where the quasiharmonic approximation fails, as we demonstrate by calculating the HCP/BCC transition in Ti.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Manufacturing of HEAs at Different Scales

The knowledge gained from melting and processing of high-entropy alloys (HEAs) at different scales is discussed with respect to melt parameters and characteristics. The melting techniques considered are button melting, vacuum induction melting (VIM) and electroslag remelting (ESR). While VIM produces HEAs with enhanced chemical homogeneity, particularly after being subjected to a homogenization heat treatment, and refined grain structure, other concerns arise from elemental contaminants associated with industrial-grade melt stock. Although ESR of the VIM product decreases the concentration of tramp elements, the narrow melt range typically found in HEAs or medium entropy alloys was found to decrease the melt efficiency. The experiments presented were performed on ingots ranging from 100 g to 75 kg.

Detrois, Martin↗
DOE OSTI2021

Bifunctional nanoprecipitates strengthen and ductilize a medium-entropy alloy

Single-phase high- and medium-entropy alloys with face-centred cubic (fcc) structure can exhibit high tensile ductility and excellent toughness, but their room-temperature strengths are low. Dislocation obstacles such as grain boundaries, twin boundaries, solute atoms and precipitates can increase strength. However, with few exceptions, such obstacles tend to decrease ductility. Interestingly, precipitates can also hinder phase transformations. Here, using a model, precipitate-strengthened, Fe–Ni–Al–Ti medium-entropy alloy, we demonstrate a strategy that combines these dual functions in a single alloy. The nanoprecipitates in our alloy, in addition to providing conventional strengthening of the matrix, also modulate its transformation from fcc-austenite to body-centred cubic (bcc) martensite, constraining it to remain as metastable fcc after quenching through the transformation temperature. During subsequent tensile testing, the matrix progressively transforms to bcc-martensite, enabling substantial increases in strength, work hardening and ductility. This use of nanoprecipitates exploits synergies between precipitation strengthening and transformation-induced plasticity, resulting in simultaneous enhancement of tensile strength and uniform elongation. Further, our findings demonstrate how synergistic deformation mechanisms can be deliberately activated, exactly when needed, by altering precipitate characteristics (such as size, spacing, and so on), along with the chemical driving force for phase transformation, to optimize strength and ductility.

36 MATERIALS SCIENCE↗