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Results for “Atomistic simulation”

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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At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

A high-throughput workflow to analyze sequence-conformation relationships and explore hydrophobic patterning in disordered peptoids

Understanding how a macromolecule’s primary sequence governs its conformational landscape is crucial for elucidating its function, yet these design principles are still emerging for macromolecules with intrinsic disorder. Herein, we introduce a high-throughput workflow that implements a practical colorimetric conformational assay, introduces a semi-automated sequencing protocol using matrix-assisted laser desorption/ionization and tandem mass spectrometry (MALDI-MS/MS), and develops a generalizable sequence-structure algorithm. Using a model system of 20mer peptidomimetics containing polar glycine and hydrophobic N-butylglycine residues, we identified nine classifications of conformational disorder and isolated 122 unique sequences across varied compositions and conformations. Conformational distributions of three compositionally identical library sequences were corroborated through atomistic simulations and ion mobility spectrometry coupled with liquid chromatography. A data-driven strategy was developed using existing sequence variables and data-derived “motifs” to inform a machine-learning algorithm toward conformation prediction. Here, this multifaceted approach enhances our understanding of sequence-conformation relationships and offers a powerful tool for accelerating the discovery of materials with conformational control.

data-driven analysis↗
DOE OSTI2021

Phase-field modeling of the interactions between an edge dislocation and an array of obstacles

Obstacles, such as voids and precipitates, are prevalent in crystalline materials. They strengthen crystals by serving as barriers to dislocation glide. Here in this work, we develop a phase-field dislocation dynamics (PFDD) technique for investigating the interactions between dislocations and second-phase obstacles, which can be either voids or precipitates. The PFDD technique is constructed to account for elastic heterogeneity, elastic anisotropy, dissociation of the dislocation, and dislocation transmission across bicrystalline interfaces. Within the framework, we present a model for “pseudo-voids”, which are voids shearable by dislocations, in contrast to unphysical, unshearable voids in conventional phase-field dislocation formulations. We employ the PFDD technique to investigate the in-plane interactions between an edge dislocation and an array of nano-scale obstacles with different spacings. In this application, the interactions take place in glide planes of either a face-centered cubic (FCC) Cu or a body-centered cubic (BCC) Nb matrix, while the precipitates have a Cu 1-x Nb x composition, with x varying from 0.1 to 0.9. Our atomistic simulations find that the alloy precipitates can have an FCC, an amorphous, or a BCC phase, depending on the compositional ratio between Cu and Nb, i.e., value of x. Among all types of obstacles, the critical stresses for dislocation bypass are the highest for unshearable amorphous precipitates, followed by shearable crystalline precipitates, and then the pseudo-voids.

42 ENGINEERING↗
DOE OSTI2021

Machine learning in materials science: From explainable predictions to autonomous design

The advent of big data and algorithmic developments in the field of machine learning (and artificial intelligence, in general) have greatly impacted the entire spectrum of physical sciences, including materials science. Materials data, measured or computed, combined with various techniques of machine learning have been employed to address a myriad of challenging problems, such as, development of efficient and predictive surrogate models for a range of materials properties, screening and down-selection of novel candidate materials for targeted applications, new methodologies to improve and further expedite molecular and atomistic simulations, with likely many more important developments to come in the foreseeable future. While the applications thus far have provided a glimpse of the true potential data-enabled routes have to offer, it has also become clear that further progress in this direction hinges on our ability to understand, explain and rationalize findings of a machine learning model in light of the domain-knowledge. This focused review provides an overview of the main areas where machine learning has been widely and successfully used in materials science. Subsequently, a brief discussion of several techniques that have been helpful in extracting physically-meaningful insights, causal relationships and design-centric knowledge from materials data is provided. Finally, we identify some of the imminent opportunities and challenges that materials community faces in this exciting and rapidly growing field.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A machine learning approach to predict thermal expansion of complex oxides

Although it is of scientific and practical importance, the state-of-the-art of predicting the thermal expansion of oxides over broad temperature and composition ranges by physics-based atomistic simulations is currently limited to qualitative agreements. We present an emerging machine learning (ML) approach to accurately predict the thermal expansion of cubic oxides with a dataset consisting of experimentally measured lattice parameters while using the metal cation polyhedron and temperature as descriptors. High-fidelity ML models that can accurately predict temperature- and composition-dependent lattice parameters of cubic oxides with isotropic thermal expansions have been successfully trained. The ML-predicted thermal expansions of oxides not included in the training dataset have shown good agreement with available experiments. The limitations of the current approach and challenges to go beyond cubic oxides with isotropic thermal expansion are also briefly discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Machine learning enabled discovery of superhard and ultrahard carbon polymorphs

The demand for multifunctional materials has motivated the move from near-equilibrium materials to metastable i.e. out-of-equilibrium phases that can meet several desired target properties. The search for such metastable phases with exotic properties is non-trivial and often serendipitous. Inverse design approaches based on evolutionary search have been powerful tools, but such traditional searches have focused on identifying primarily stable and metastable materials with the lowest enthalpy. The inverse design of materials, with a focus on a desired property such as, for example, hardness is a challenging task because of the expensive computational cost involved in sampling multiple structures. The recent advances in machine learning have brought new powerful AI techniques to the forefront which can potentially revolutionize the inverse design and discovery of materials, especially metastable phases capable of meeting multifunctionality. Here, in this work, we develop and apply an automated reinforcement learning workflow for inverse design that integrates first principles physics and atomistic simulations with machine learning (ML), and high-performance computing to allow rapid exploration of the superhard and ultrahard metastable phases of Carbon. We demonstrate an automatic machine learning based inverse design workflow to map new undiscovered metastable states ranging from near equilibrium to those far-from-equilibrium that satisfy multiple property objectives, specifically bulk moduli, shear moduli and hardness. We create a comprehensive library of carbon stable and metastable phases with varying hardness and subsequently shortlist 10 top performing candidate carbon structures, including two newly reported phases, based on their hardness and characterize their temperature dependent mechanical properties. A neural network model is built using featurization of allotropes of carbon to predict the quasi-harmonic Gibbs free energies. The Gibbs free energies of the top performing phases are analyzed to get an estimate of the experimental synthesizability of these superhard and ultrahard carbon phases. In general, we show using machine learning based inverse design approaches how hitherto inaccessible metastable states can be identified and potentially synthesized to meet the demand for multifunctional materials.

Balasubramanian, Karthik [Univ. of Illinois, Chica↗
DOE OSTI2025

Computational materials assessment of the D/Li-stripping neutron source as a prototypical facility for fusion materials testing

As the US fusion materials community awaits the selection and design of a fusion prototypical neutron source (FPNS), a risk reduction exercise has been conducted to (i) provide an updated materials performance evaluation using state-of-the-art computational materials modeling, (ii) expand on legacy analysis based on pure Fe to other relevant fusion structural materials types, and (iii) ensure that materials response under FPNS operational conditions is consistent with referential fusion reactor conditions. The current paper describes the efforts undertaken to assemble a comprehensive computational methodology that includes neutronics, primary damage calculations, atomistic simulations of displacement cascades, chemical inventory evolution calculations, and a computational thermodynamic analysis of emerging phases during irradiation. Our work extends existing studies in pure Fe to reduced-activation ferritic/martensitic steels, tungsten, silicon carbide, and vanadium alloys. We focus on the single-beam deuteron/lithium-stripping neutron source behind the IFMIF-DONES concept, which we assess against ITER, two DEMO designs, and an ideal pure 14-MeV flux. Our analysis indicates that, within standard uncertainties inherent to the models employed, the DONES concept adequately captures fusion conditions in the four materials analyzed. Our work is intended as a comprehensive irradiation damage analysis of fusion-representative neutron sources, to be used for further neutron source evaluation and fusion facility operation.

Marian, Jaime [Univ. of California, Los Angeles, C↗
DOE OSTI2026

Agentic framework for programmatic crystal structure generation using a fine-tuned worker–supervisor large language model

Platinum group metals (PGMs) underpin many catalytic technologies but face severe supply constraints, motivating the search for alternative materials and computational methods to accelerate discovery. While atomistic simulation tools such as Pymatgen and ASE have streamlined structure manipulation, they require detailed inputs, limiting accessibility for experimentalists and slowing early-stage exploration. Here, in this study, we present an AI-driven agentic framework that orchestrates worker–supervisor large language models (LLMs). The worker translates natural-language prompts of varying abstraction into valid crystallographic structures using a compact LLM fine-tuned with low-rank adaptation on a curated text–code–CIF dataset, emphasizing energy-efficient training. Benchmarking against the baseline CodeGen-350M-mono model shows that fine-tuning reduces hallucination rates from 100% to as low as 5% and improves structural match accuracy to up to 82% for fully specified inputs. Accuracy declines with decreasing prompt detail but remains nontrivial even when only stoichiometry and space group are provided, underscoring the LLM’s capacity for crystallographic inference. The supervisor Claude LLM evaluates the outputs and triggers iterative refinement through the worker’s built-in structure manipulation capabilities (e.g., supercell scaling, strain, vacancy, and substitution operations). We further demonstrate use cases for technologically relevant catalysts, including IrO 2 , pyrochlore Pb 2 Ir 2 O 7 , Ni 2 FeO 4 , and Ni 3 Mo, where the framework generates physically consistent structures that can be refined via geometry optimization. This work introduces a low-energy, language-driven pathway for integrating human and machine intelligence in materials design, paving the way for AI-assisted synthesis planning and high-throughput screening of complex oxides.

AI agent↗
DOE OSTI2025

Interplay between hydrogen, temperature, and character angle on dissociated dislocation energies in Fe–Ni–Cr austenitic stainless steels

Dislocation energy has an important role in the mechanical performance of structural metals. While dislocation energies cannot be fully obtained from continuum theories due to the contribution of the dislocation core, they have been calculated via atomistic simulations in elemental metals. However, constraints on the local atomic environments have prevented the use of such approaches in systems that incorporate alloying or interstitial solutes. In this work, we develop robust molecular dynamics methods to resolve these issues through a geometric construction of dislocation dipoles and the calculation of time-averaged energies. Furthermore, we apply these methods to calculate dislocation energies (including core energies) in an Fe 70 Ni 11 Cr 19 austenitic steel at a variety of character angles, hydrogen concentrations, temperatures, and dipole spacings. The resulting highly converged energies show an excellent agreement with continuum expressions. Overall, hydrogen concentrations up to 1.0 % do not have a significant effect on the elastic parameters and dislocation energy. The methods and insights derived in this work have the potential to facilitate the calculation of dislocation energies in a wide range of systems, and to guide our understanding of hydrogen embrittlement.

Alloyed systems↗
DOE OSTI2024

Hydrogen effects on the deformation and slip localization in a single crystal austenitic stainless steel

Hydrogen is known to embrittle austenitic stainless steels, which are widely used in high-pressure hydrogen storage and delivery systems, but the mechanisms that lead to such material degradation are still being elucidated. The current work investigates the deformation behavior of single crystal austenitic stainless steel 316L through combined uniaxial tensile testing, characterization and atomistic simulations. Thermally precharged hydrogen is shown to increase the critical resolved shear stress (CRSS) without previously reported deviations from Schmid’s law. Molecular dynamics simulations further expose the statistical nature of the hydrogen and vacancy contributions to the CRSS in the presence of alloying. Slip distribution quantification over large in-plane distances (> 1 mm), achieved via atomic force microscopy (AFM), highlights the role of hydrogen increasing the degree of slip localization in both single and multiple slip configurations. The most active slip bands accumulate significantly more deformation in hydrogen precharged specimens, with potential implications for damage nucleation. For $\langle$110$\rangle$ tensile loading, slip localization further enhances the activity of secondary slip, increases the density of geometrically necessary dislocations and leads to a distinct lattice rotation behavior compared to hydrogen-free specimens, as evidenced by electron backscatter diffraction (EBSD) maps. Finally, the results of this study provide a more comprehensive picture of the deformation aspect of hydrogen embrittlement in austenitic stainless steels.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Effect of differently oriented interlayer phases on the radiation damage of Inconel-$\mathrm{Ni}$ multimetallic layered composite

Multimetallic layered composites (MMLCs) have shown an excellent potential for application under extreme environments, e.g., accident-tolerant fuel cladding, because of their low oxidation tendency and high corrosion resistance. Interfacial phases or complexions in nanocrystalline materials accelerate the annihilation of defects and enhance the radiation resistance of materials, making MMLCs with engineered interlayer phases compelling to deploy in extreme conditions. However, implementation of MMLCs in full capacity remained a challenge due to a lack of fundamental understanding of the underlying mechanisms governing the characteristics of the interface between the metallic layers. The precise role of interlayer phases in MMLCs and their interaction with defects, specifically under extreme conditions, is still unexplored. Pursuing atomistic simulations for various Inconel-Ni MMLCs model materials, we revealed accelerated defect mobility in interlayers with larger crystalline misorientation and the inverse relationship between the interface sink strength to the misorientation angle. Furthermore, we found a linear relation between interlayer misorientation angle with the density of radiation-induced defects and radiation enhanced displacements. So, our results indicate that radiation-induced material degradation is accelerated by the higher defect formation tendency of MMLCs with a high-angle interlayer interface.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Stress effects on the energy barrier and mechanisms of cross-slip in FCC nickel

The energy barrier for homogeneous cross-slip of a screw dislocation in face-centered cubic (FCC) nickel is calculated using atomistic simulations as a function of the Escaig stress on the glide plane, the Escaig stress on the cross-slip plane, and the Schmid stress on the cross-slip plane. Two cross-slip mechanisms, Friedel-Escaig (FE) and Fleischer, are examined and their energy barriers are calculated for a large number of stress combinations. For each mechanism, the energy barrier as a function of three stress components can be reduced into a one-dimensional function of an effective stress. The stress domains in which FE and Fleischer mechanisms operate respectively are determined. Additionally, the FE mechanism dominates when the Escaig stress on the glide plane (in the direction that reduces the stacking fault width) is the largest stress component. Increasing the Schmid stress and Escaig stress (in the direction that expands the stacking fault width) on the cross slip plane promotes the Fleischer mechanism. The cross slip energy barrier functions obtained here can be used as input functions for computing cross slip rates in mesoscale dislocation dynamics simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Grain-boundary fracture mechanisms in Li 7 La 3 Zr 2 O 12 (LLZO) solid electrolytes: When phase transformation acts as a temperature-dependent toughening mechanism

Garnet-type, solid electrolytes, such as Li 7 La 3 Zr 2 O 12 (LLZO), are a promising alternative to liquid electrolytes for lithium-metal batteries. However, such solid-electrolyte materials frequently exhibit undesirable lithium (Li) metal plating and fracture along grain boundaries. In this work, we employ atomistic simulations to investigate the mechanisms and key fracture properties associated with intergranular fracture along one such boundary. Our results show that, in the case of a Σ5 (310) grain boundary, this boundary exhibits brittle fracture behavior, i.e. the absence of dislocation activity ahead of the propagating crack tip, accompanied with a decrease in work of separation, peak stress, and maximum stress intensity factor as the temperature increases from 300 K to 1500 K. As the crack propagates, we predict two temperature-dependent Li clustering regimes. For temperatures at or below 900 K, Li tends to cluster in the bulk region away from the crack plane driven by a void-coalescence mechanism concomitant a simultaneous cubic-to-tetragonal phase transition. The tetragonalization of LLZO in this temperature regime acts as an emerging toughening mechanism. At higher temperatures, this phase transition mechanism is suppressed leading to a more uniform distribution of Li throughout the grain-boundary system and lower fracture properties as compared to lower temperatures.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Invariant surface elastic properties in FCC metals and their correlation to bulk properties revealed by machine learning methods

In this work, we present a combination of machine-learned models that predicts the surface elastic properties of general free surfaces in face-centered cubic (FCC) metals. These models are built by combining a semi-analytical method based on atomistic simulations to calculate surface properties with the artificial neural network (ANN) method or the boosted regression tree (BRT) method. The latter is also used to link bulk properties and surface orientation to surface properties. The surface elastic properties are represented by their invariants considering plane elasticity within a polar method. The resulting models are shown to accurately predict the surface elastic properties of seven pure FCC metals (Cu, Ni, Ag, Au, Al, Pd, Pt). The BRT model reveals the correlations between bulk and corresponding surface properties in terms of invariants, which can be used to guide the design of complex nano-sized particles, wires and films. Finally, by expressing the surface excess energy density as a function of surface elastic invariants, fast predictions of surface energy as a function of in-plane deformations can be made from these model constructs.

36 MATERIALS SCIENCE↗
DOE OSTI2024

In-situ HRTEM observations of intermediate phase transformation in lattice reorientation in HCP rhenium

In-situ high-resolution transmission electron microscopy (HRTEM) is performed to investigate the deformation behavior of hexagonal close-packed rhenium (Re) which is compressed along the $\langle1\overline{1}00\rangle$ direction. Atomistic simulations are also conducted to better understand the deformation mechanisms. Two types of lattice reorientation are observed during compression. The first type involves the reorientation of one lattice by ~90° around $\langle1\overline{1}00\rangle$, which is accomplished by the formation of an intermediate face-center-cubic (FCC) phase at the interface. This transformation sequence can be described as {$1\overline{1}00$} matrix → {$111$} FCC → ($0001$) twin . In the second type, a new grain is formed but does not satisfy any known twin relationship with the matrix, and an intermediate FCC phase is also formed. The transformation sequence can be described as {$1\overline{1}00$} matrix → {$111$} FCC → ($0001$) grain . Mechanisms responsible for the observed lattice reorientation and sequential phase transitions are analyzed by conducting lattice correspondence analyses on the simulation results. Strain accommodation is also analyzed to explain the mechanisms for lattice reorientation and the intermediate phase transformations. In conclusion, the results provide new insight into the deformation behavior of HCP metals

36 MATERIALS SCIENCE↗
DOE OSTI2020

Vacancy and interstitial interactions with crystal/amorphous, metal/covalent interfaces

In this work, we use atomistic simulations to investigate the interaction of vacancies and interstitials with interfaces between a crystalline metal and an amorphous, covalently-bonded solid. We select the gold (Au)/silicon (Si) binary system as a model material and construct interface models along two different facets of crystalline Au and with amorphous Si (a-Si) created at three different quench rates. We compute formation energies of vacancies, self-interstitials, and interstitial impurities as a function of position relative to the interface and find that they have markedly lower values near the interface than in the interior of the adjoining phases. We conclude that crystal/amorphous, metal/covalent interfaces may be as effective at removing radiation-induced point defects as interfaces in polycrystalline metals composites. Moreover, irrespective of interface character, the average formation energies of all point defects at all the Au/a-Si interfaces we investigated are comparable. Thus, unlike in polycrystalline metals, where an interface’s crystallographic character has a marked effect on its interactions with point defects, all interface types in crystal/amorphous, metal/covalent composites may be equally effective at absorbing all radiation-induced defects.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Helium causing disappearance of a/2<111> dislocation loops in binary Fe-Cr ferritic alloys

In this work, single and dual-beam self-ion irradiations were performed at 500°C on ultra-high purity Fe14%Cr alloy to ~0.33 displacements-per-atom (dpa), and 0 or 3030 atomic-parts-per-million (appm) helium/dpa, respectively. Using transmission electron microscopy, we reveal that helium can drastically modify the dislocation loop Burgers vector in Fe-Cr alloys. Helium co-implantation caused complete disappearance of a/2<111> type dislocation loops, and the microstructure consisted of only a<100> loops. Conversely, a/2<111> type loops were predominant without He co-implantation. The total loop density remained largely unaffected. The results strikingly contrast literature asserting that helium stabilizes a/2<111> type loops in bcc Fe alloys, based on low temperature irradiations. Collectively analyzing the results with literature suggest that the small positive interaction between helium and self-interstitial atoms (SIA) in Fe predicted by atomistic simulations maybe insufficient to holistically explain the dislocation loop microstructure development in presence of helium. Helium-SIA positive binding inadvertently implies elevated a/2<111> loop fraction and higher loop densities that the present results contradict. Helium induced high cavity density causing a preferential loss of highly glissile <111> clusters, leaving the matrix saturated with <100> type clusters is proposed as a potential mechanism. Further, the in-situ irradiations combined with Burgers vector analysis strengthened the evidence of Cr-induced dislocation loop mobility reduction that appears to stabilize the a/2<111> type loops and causes higher loop densities in Fe-Cr alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Microstructurally informed synchrotron x-ray analysis revealing helium defect transitions in ultrafine grained tungsten

The formation of insoluble gaseous defects in materials due to nuclear transmutation or ion implantation involves the diffusion of impurity atoms to form atomic defect clusters that coalesce into bubbles or cavities and ultimately degrade the material properties. Transmission electron microscopy (TEM) is limited in its ability to resolve sub-nanometer gas clusters whereas X-ray diffraction (XRD) provides information pertaining to local atomic changes. Here, in this study, helium (He) implanted ultrafine grained tungsten is explored through a multimodal defect characterization campaign combining TEM-informed Small Angle X-ray Scattering (SAXS) analysis, XRD lattice parameter measurements, and nanoscale He cluster quantification from a region of reciprocal space accessible via Wide Angle X-ray Scattering (WAXS). Moderate elevated temperature implantations are shown to produce high concentrations of sub-nanoscale He clusters and small, homogeneously distributed cavities, which collectively are linked to lattice expansion and further substantiated through complementary atomistic simulations. Increased implantation temperatures encourage the diffusion of these defects to the grain boundaries (GBs), leading to lattice relaxation and the growth of large GB cavities manifesting as bimodal size distributions in the SAXS analysis. Overall, our results demonstrate the utility of multimodal synchrotron X-ray analysis in bridging the gap between microscale He cavity quantification and atomic-scale defect analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Machine learning-enabled multiscale modeling of mechanical deformation of aluminum and Al-SiC nanocomposites

A machine learning-enabled multiscale framework is developed for modeling the mechanical response of both pure metal and nanoparticle-reinforced metal matrix nanocomposites (MMNCs). Using aluminum–silicon carbide (Al-SiC) as an example MMNC, atomistic simulations reveal three distinct deformation mechanisms (i.e., defect-free, dislocation-based, and interface separation) governed by the interfaces between the Al matrix and SiC nanoparticles. As compared with single crystal Al, the lattice undergoes a more abrupt failure once the dislocation network becomes extensive and void nucleation initiates, whereas in Al-SiC, nanoparticle interfaces enable a more gradual progression of damage. These mechanisms are captured through a combined classification-regression neural network surrogate model that bridges atomic-scale insights with continuum-scale finite element analysis. Machine learning-enabled multiscale modeling of pure Al accurately predicted strain localization and confirmed by in-situ scanning electron microscopic tensile testing on perforated Al specimens. This study underscores the promise of integrating physics-informed machine learning with hierarchical modeling to capture the interface dominated phenomena and guide the design of advanced MMNCs.

Al-SiC↗