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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Assessing the Effect of Explicit Polarizability on Models of Carbon Dioxide Solvation in Ionic Liquids

Ionic liquids are an important possible carbon capture material because of their anomalously high sorption selectivity for carbon dioxide over other gases common in air. Many research groups have investigated the molecular origins of this property and provided important insights, including using 1D and 2D-IR spectroscopy. Molecular dynamics simulations have been indispensable to the interpretation of these experiments. In prior molecular dynamics simulation work, charge-scaled force fields have typically been used to provide a mean-field treatment of effects vital to ionic liquid systems such as charge transfer and polarization. Here, we compare models of carbon dioxide solvated in ionic liquids with explicit polarization to models of the same with implicit polarizability through charge-scaling. We calculate structural, dynamical, and spectroscopic properties, and make comparisons to the same items measured in experiment. In this study, we focus on two ionic liquids: 1-butyl-3- methylimidazolium (BMIM + ) paired with bis(trifluoromethane sulfonyl imide) (Tf 2 N − ) and 1-butyl-3-methylimidazolium (BMIM+) paired with hexafluorophosphate (PF 6 − ). We find that many structural, dynamical, and spectroscopic properties are changed when polarization is modeled explicitly. We also find that explicit polarizability softens local ion cages around the carbon dioxide and that the long-time diffusion of the carbon dioxide is gated by the reorganization of the ionic liquid molecules. Comparisons to experiment show modest improvement of many observables compared with experiment for the explicitly polarizable model over the charge-scaled model. Overall, our results show that charge-scaled force fields are likely sufficient to compute spectroscopic properties of carbon dioxide in ionic liquids and suggest some interpretive rules for understanding their structural and dynamical properties. Those using charge-scaled force fields should generally assume that the ion cages around solutes such as carbon dioxide are too stiff and cation-rich in their models and adjust their interpretations and predictions accordingly.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Effect of Solvent on the Local Structure, Dynamics, and Vibrational Density of States in Sn-BEA Zeolite

Lewis acid zeolites are attractive catalysts for epoxidation and biomass valorization, as they are highly active and selective in the liquid phase and can operate at or near ambient conditions. While a rich experimental literature exists on liquid-phase Lewis acid zeolite catalysis, our understanding of the molecular organization and solvent dynamics in the vicinity of Lewis acid sites with differing metal site speciation remains limited. In this work, we investigate the molecular coordination and diffusion of two common solvents (methanol and water) around the closed and open Sn-BEA zeolite active sites using molecular dynamics simulations with a machine-learned interatomic potential trained on ab initio molecular dynamics trajectories. Molecular dynamics simulations reveal that introducing active sites significantly enhances local order in the first and second solvation shells compared to the pure silica case. For methanol, both closed and open active sites are singly coordinated, while more than two water molecules coordinate the open site. In contrast to methanol, we observed that water molecules dissociate, leading to the formation of additional Sn-OH and silanol groups away from the active site. The diffusion coefficients of water and methanol are functions of the solvent population in the pore. Here, our work provides insights into how active site speciation in Lewis acid zeolites affects solvent coordination, diffusion, and vibrational signature. This information is foundational for catalyst design and optimization of liquid-phase catalytic processes in zeolites. It also demonstrates the suitability of machine-learned interatomic potentials for modeling reactive systems, enabling sufficiently long trajectories for appropriate statistical averaging.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Fouling behavior of zwitterionic membranes compared to polyamide membranes

Membrane fouling remains a critical bottleneck for reverse osmosis (RO) desalination, driving energy consumption and reducing membrane lifetime. Here, we employ all-atom molecular dynamics simulations to investigate the antifouling behavior of random zwitterionic amphiphilic copolymer (r-ZAC) membranes composed of sulfobetaine methacrylate (SBMA) and allyl methacrylate (AMA), benchmarked against conventional polyamide (PA) RO membranes. Structural and dynamical analyses—including radial distribution functions, coordination numbers, tetrahedral order parameters, vector orientation, and residence-time correlation functions—reveal that r-ZAC surfaces sustain tightly bound, long-lived hydration layers with preserved tetrahedrality and anisotropic water orientation, in sharp contrast to the weak and disordered hydration of PA. Steered molecular dynamics simulations demonstrate that r-ZAC membranes impose substantial free-energy barriers to foulant approach (alginate ≈ 90 kcal/mol, sucrose ≈ 35 kcal/mol, humic acid ≈ 15 kcal/mol), whereas PA membranes exhibit negligible barriers (< 1 kcal/mol) and thermodynamically favorable adsorption. Detailed foulant–surface interaction analyses show that zwitterionic hydration and electrostatic heterogeneity in r-ZAC suppress adhesion, except in the case of amphiphilic humic acid, which exploits multiple binding modes. Together, these results establish molecular-level design principles for antifouling membranes: the combination of zwitterionic hydration, structured interfacial water, and controlled amphiphilic balance in r-ZAC membranes provides superior resistance to organic fouling relative to PA.

Cross-linked polyamide↗
DOE OSTI2025

Shock-induced chemistry and high strain-rate viscoelastic behavior of a phenolic polymer

We use impact experiments and a finite element model (up to 1.2 GPa), and molecular dynamics simulations (up to 60 GPa), to examine the behavior of a phenolic polymer under shock compression, spanning both nonreactive and reactive regimes. In the nonreactive regime, relaxation following compression at strain rates of ∼105 s−1 can be explained by viscoelasticity observed at ordinary laboratory rates (≲1 s−1) by accounting for the temperature dependence of the phenolic β-transition. Reasonable agreement is found between the measured shock Hugoniot up to 1.2 GPa and molecular dynamics simulation for cross-linked structures of comparable density. We also observed a first-order mechanical transition near 0.36 GPa shock stress and estimated a spall strength of 0.102 GPa and Hugoniot elastic limit of 1–2 GPa. The shock stress is found to vary up to 24% among phenolics made with different resin and/or cure processes. Finally, molecular dynamics simulations are used to identify a reactive regime at shock pressures ≳20 GPa that is characterized by chemically driven, rate-dependent relaxation processes, including dehydrogenation and dehydration reactions that promote the formation of a dense, highly cross-linked carbonaceous solid and the release of light volatiles.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

High-pressure phase transition in 3-D printed nanolamellar high-entropy alloy by imaging and simulation insights

Abstract We report on the high-resolution imaging and molecular dynamics simulations of a 3D-printed eutectic high-entropy alloy (EHEA) Ni 40 Co 20 Fe 10 Cr 10 Al 18 W 2 consisting of nanolamellar BCC and FCC phases. The direct lattice imaging of 3D-printed samples shows the Kurdjumov–Sachs (K–S) orientation relation {111} FCC parallel to {110} BCC planes in the dual-phase lamellae. Unlike traditional iron and steels, this alloy shows an irreversible BCC-to-FCC phase transformation under high pressures. The nanolamellar morphology is maintained after pressure cycling to 30 GPa, and nano-diffraction studies show both layers to be in the FCC phase. The chemical compositions of the dual-phase lamellae after pressure recovery remain unchanged, suggesting a diffusion-less BCC–FCC transformation in this EHEA. The lattice imaging of the pressure-recovered sample does not show any specific orientation relation between the two resulting FCC phases, indicating that many grain orientations are produced during the BCC–FCC phase transformation. Molecular dynamics simulations on phase transformation in a nanolamellar BCC/FCC in K–S orientation show that phase transformation from BCC to FCC is completed under high pressures, and the FCC phase is retained on decompression aided by the stable interfaces. Our work elucidates the irreversible phase transformation under static compression, providing an understanding of the orientation relationships in 3-D printed EHEA under high pressures.

3-D Printing↗
DOE OSTI2025

Developing reliable machine learning interatomic potential for Fe–Cr–Ni austenitic alloys

Gaining atomistic understanding of mechanical behavior of heat-resistant structural materials such as Fe–Cr–Ni-based alloys requires an approach with an accuracy close to density functional theory (DFT) that considers the intrinsic properties of the bulk lattice and important defects such as stacking faults, grain boundaries, and surfaces. This work aims to develop reliable machine learning interatomic potential (MLIAP) at cross-scale for Fe–Cr–Ni ternary alloys with a focus on the face-centered-cubic (fcc) solid solution structure. Leveraging the advantages of moment tensor potentials, which typically necessitate a relatively small training dataset and enable rapid calculations using the large-scale atomic/molecular massively parallel simulator package, we ensure the stability and accuracy of the trained potentials. Important defects such as stacking faults, grain boundaries, and surfaces for wide-range compositions are investigated. Structural, thermal, elastic, and defect properties are determined from molecular dynamics simulations comprising several thousand atoms, generated via canonical Monte Carlo simulations guided by the trained potential. The trained potential allows efficient atomic simulations of structural, thermal, and mechanical properties of fcc Fe–Cr–Ni solid solution alloys as a function of composition and temperature. Therefore, the MLIAP approach represents a major advancement from DFT calculations that are limited to small simulation sizes and traditional molecular dynamics simulations using relatively low accuracy potentials. Furthermore, this work outlines a practical foundation for further investigating the structural evolution and mechanical behavior of austenitic stainless steel and nickel-based alloys in a wide array of applications in extreme environments.

Crystal structure↗
DOE OSTI2026

Custom-trained Machine-learning Interatomic Potentials: ZnCl2 Aqueous Solution

This dataset was generated using an iterative active-learning strategy implemented in the ArcaNN software package (https://github.com/arcann-chem/arcann_training) to train machine-learning interatomic potentials for aqueous ZnCl2 solutions. Each active-learning cycle consisted of three stages: training, exploration, and labeling. The initial training set combined configurations generated in this work from enhanced-sampling ab initio molecular dynamics simulations with configurations from a previously reported neural-network-potential study of aqueous ZnCl2. The enhanced-sampling ab initio molecular dynamics simulations involved Zn–Cl separation and the chloride coordination number around Zn²? as collective variables. These configurations served as the seed dataset. Subsequent active-learning cycles expanded the training set by identifying and labeling configurations that were poorly represented by the current models, thereby improving coverage of ion-association states and changes in local coordination and charge-state environments relevant to the solution free-energy landscape. For all selected configurations, single-point calculations of the total energies and atomic forces were performed within density functional theory using the CP2K Quickstep module. Reference calculations employed the revPBE-D3 and r2SCAN exchange-correlation functionals. Motivated by recent work on aqueous Zn²?, the main revPBE calculations omitted D3 dispersion contributions involving Zn²?, while retaining the D3 correction for water and chloride. For comparison, fully dispersion-corrected revPBE-D3 reference calculations were also performed, with D3 applied to all species, including Zn²?. Valence electrons were treated explicitly, while core electrons were represented using norm-conserving Goedecker–Teter–Hutter pseudopotentials. The wave functions were expanded using the mixed Gaussian-and-plane-wave scheme with TZV2P-MOLOPT basis sets for all elements and a 600 Ry auxiliary plane-wave cutoff for the electron density. Self-consistent-field convergence was accelerated using the orbital-transformation and Direct Inversion in the Iterative Subspace algorithms, with a convergence threshold of 10?6. All single-point calculations were performed in periodic orthorhombic cells. The CELL_REF keyword in CP2K was used to define a fixed reference cell with a box length of 25 Å. This treatment ensured a consistent reference for configurations extracted from NpT trajectories with fluctuating cell dimensions. The resulting DFT energies and atomic forces constitute the ground-truth labels used to train the MLIPs. The resulting MLIP was trained for aqueous ZnCl2 solutions spanning concentrations from 0 to 30 molal and a broad pH range, from strongly acidic to strongly basic conditions. Representative examples of configurations included in the MLIP training dataset are provided below. These include 1) Representative configurations from the dataset labeled at the revPBE-D3 level, with D3 dispersion interactions involving Zn2+ excluded (revPBE-wo-D3). 2) Representative configurations from the dataset labeled at the fully dispersion-corrected revPBE-D3 level, with D3 interactions applied to all species, including Zn2+ (revPBE-D3). 3) Representative configurations from the dataset labeled at the r2SCAN level of theory (r2SCAN).

Dinpajooh, Mohammadhasan [Pacific Northwest Nation↗
DOE OSTI2025

Accurate and efficient parameterization of an atomic cluster expansion (ACE) potential for ammonia under extreme conditions

We present a machine learning interatomic potential for ammonia designed to capture its complex multiphase behavior, including both molecular and superionic phases. The potential is based on the atomic cluster expansion (ACE) formulation and has been parameterized to facilitate high-fidelity molecular dynamics simulations of ammonia under extreme conditions, for pressures up to 100 GPa and for temperatures above 500 K and up to 6000 K. A diverse range of configurations was generated through high-quality ab initio molecular dynamics simulations, covering insulating and superionic ice phases, liquid ammonia, molecular nitrogen (N 2 ) and hydrogen (H 2 ), and metastable compounds that form upon dissociation, including $NH^{+}_{4}$, $H^{+}_{3}$, N 2 H 4 , and N 3 H. We demonstrate that the ammonia ACE potential accurately reproduces experimental and density functional theory predicted isotherms and Hugoniots. Crucially, the potential is able to capture the intricate phase behavior of ammonia, including the transition from insulating molecular fluid to the superionic phase. This work provides a robust interatomic potential that can be used for large-scale, accurate simulations of ammonia under extreme thermodynamic conditions, offering a powerful tool for investigating its behavior in various phases and applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Structure and Dynamics of CO 2 at the Air–Water Interface from Classical and Neural Network Potentials

The accurate description of the structure and dynamics of CO 2 at the instantaneous air–water interface, along with the effects of surface fluctuations on the CO 2 -transport processes, is essential for the development of negative emission technologies aimed at minimizing climate change. In this study, we performed molecular dynamics simulations of CO 2 at the air–water interface using neural network potentials (NNPs) trained on ab initio data generated through density-functional-theory-based molecular dynamics simulations. We compared these results with classical force fields to assess their performance in modeling interfacial CO 2 behavior. Our findings revealed that the asymmetric interactions, coupled with thermal surface fluctuations at the air–water interface, significantly influence CO 2 transport into the aqueous phase. The simulations demonstrate that classical force fields underestimate both the free energy of CO 2 transport and the strength of its interactions at the interface compared with the neural network potentials. In conclusion, the free energy and the interfacial dynamics of CO 2 are primarily influenced by the distribution of water within the instantaneous interfacial water layer, responsible for creating an asymmetric intermolecular interaction environment within the interfacial region.

Ab initio molecular dynamics↗
DOE OSTI2024

Release dynamics of nanodiamonds created by laser-driven shock-compression of polyethylene terephthalate

Laser-driven dynamic compression experiments of plastic materials have found surprisingly fast formation of nanodiamonds (ND) via X-ray probing. This mechanism is relevant for planetary models, but could also open efficient synthesis routes for tailored NDs. We investigate the release mechanics of compressed NDs by molecular dynamics simulation of the isotropic expansion of finite size diamond from different P-T states. Analysing the structural integrity along different release paths via molecular dynamic simulations, we found substantial disintegration rates upon shock release, increasing with the on-Hugnoiot shock temperature. We also find that recrystallization can occur after the expansion and hence during the release, depending on subsequent cooling mechanisms. Our study suggests higher ND recovery rates from off-Hugoniot states, e.g., via double-shocks, due to faster cooling. Laser-driven shock compression experiments of polyethylene terephthalate (PET) samples with in situ X-ray probing at the simulated conditions found diamond signal that persists up to 11 ns after breakout. In the diffraction pattern, we observed peak shifts, which we attribute to thermal expansion of the NDs and thus a total release of pressure, which indicates the stability of the released NDs.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Molecular origin of negative lithium transference in electrolytes with star-shaped multivalent anions

Large-scale molecular dynamics simulations illustrate that highly correlated cation–anion motion leads to negative t 0+ on the order of 1 in lithium electrolytes with star-shaped multivalent anions. Large multivalent anions have gained increasing attention for their potential to improve lithium transference in electrolytes. We employ large-scale molecular dynamics simulations based on the Onsager transport framework to investigate ion transport in a lithium electrolyte with star-shaped multivalent anions. The simulations show that t 0+, the cation transference number with respect to solvent velocity, is negative over a wide range of concentration. This is consistent with experimental data reported previously. The simulation-based Onsager transport coefficients reveal that the magnitudes of the cation–cation, anion–anion, and cation–anion correlations are comparable, a signature of highly correlated motion in the electrolyte. Examination of the cation solvation environment indicates the presence of strong cation–anion association across the entire concentration range, which leads to negative t 0+ on the order of −1. Both simulation and experiment also show that the maximum value of t 0+ reaches 0 when the cation concentration is c + = 0.4 M. This is the concentration at which the anions begin to spatially overlap, and lithium ions serve as dynamic linkers to balance cation–cation and cation–anion correlations. Our results provide molecular-level insights into the origin of transference in multivalent electrolytes.

Fang, Chao↗
DOE OSTI2025

Keeping LAMMPS cutting edge

Since its inception 30 years ago, LAMMPS has grown to be a world-class molecular dynamics code and a cornerstone of computational materials science research. This project aimed to keep LAMMPS at the forefront of molecular dynamics simulations by adapting LAMMPS to the latest developments in machine learning technology and hardware. Initially, the project set out to provide a unified implementation of active learning for efficient training data generation in LAMMPS, but the research trajectory pivoted to address more immediate and impactful opportunities. On the hardware side, recent record-breaking molecular dynamics simulations were developed on the Cerebras wafer-scale AI chip, and this project has developed an interface between LAMMPS and the hardware-specific molecular dynamics code to accelerate and simplify development and user adoption. On the software side, PyTorch’s Ahead-of-Time (AOT) compilation features promised increased performance for state-of-the-art equivariant neural network potentials, and this project laid the groundwork for their adoption in LAMMPS, resulting in a nearly 20x acceleration in extreme cases. Combined with a comprehensive benchmark study of LAMMPS across all current exascale systems, this project has reinforced LAMMPS’s role as a versatile, high-performance tool for current and future materials science applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Iodine recombination in xenon solvent: Clusters in the gas to liquid-like state transition

Supercritical fluids (SCFs) have attracted significant attention as solvents for chemical reactions due to their unique properties, such as high diffusivity, low viscosity, and tunable solvation properties. These properties profoundly influence reaction kinetics and are often attributed to the formation of molecular clusters within SCFs. To study the effect of supercritical solvent on chemical reactivity and dynamics of reactions, one needs to understand the dynamics of clusters in supercritical fluid. Extensive experiments on the photodissociation and recombination of iodine in supercritical fluids served as a model system for understanding these effects. Experimental studies have been complemented by theoretical and computational investigations, which mostly employ Monte Carlo or empirical molecular dynamics simulations. However, computational studies using non-reactive force fields and ab initio approaches present challenges in capturing reactive processes at larger scales within supercritical fluids. Here, in this work, we developed the ReaxFF parameters by training against quantum mechanics data. ReaxFF reactive force field based molecular dynamics simulations were performed, studying the dynamics of a xenon solvent and cage effect at different thermodynamic conditions for the iodine recombination reaction. We show that the conditions near the critical point are the optimal conditions to study the cage effect. We show that the average lifetime of xenon clusters ranging between 5 and 11 ps is comparable to iodine geminate recombination. Our simulation results of iodine recombination in xenon solvent demonstrate the higher probability of iodine molecule formation in the presence of xenon clusters. Finally, we show that the supercritical condition exhibits the highest recombination rate for iodine atoms.

Cage effect↗
DOE OSTI2025

Development and transferability of neural-network models for plasma-surface interactions

Plasma-surface interactions are increasingly critical to modern technologies; yet, accurate molecular dynamics simulations remain limited by the capabilities of interatomic potentials. Deep Potentials (DPs) promise to revolutionize the field by providing a systematic method for producing accurate interatomic potentials. The primary challenge of DP development is selecting a dataset, which efficiently spans the set of atomic environments one expects to encounter in the subsequent molecular dynamics simulations. The computational cost of density functional theory calculations, which are the typical basis for DP development, makes it impossible to directly verify the quality of a given DP. To address this challenge, we explore the development of a deep-learned interatomic potential, “DeepREBO,” trained to reproduce the behavior of the REBO2 empirical potential, enabling direct validation of training methodology and transferability. Using an active learning framework, we begin with a minimal dataset and iteratively expand it to train a Deep Potential-Smooth Edition model that faithfully reproduces REBO2 results for 25 eV hydrogen bombardment of diamond (001), a particularly challenging case. We show that small, carefully curated datasets can outperform large, unguided ones, with effective models requiring fewer than 15 000 snapshots. Subsequent transferability tests demonstrate that while DeepREBO generalizes well to diamond (111) surfaces, performance degrades for amorphous carbon or higher-energy impacts, highlighting the need for use-case-specific training data. We also evaluate methods to improve short-range repulsion. This study outlines best practices for training robust deep potentials and underscores the importance of dataset design for predictive plasma simulations.

Ab-initio molecular dynamics↗
DOE OSTI2025

Long timescale solvation dynamics and confinement: The case of non-ionic deep eutectic solvents of lauric acid and N-methylacetamide

Microscopic segregation and molecular heterogeneities in complex liquids are the result of the interplay between different intermolecular forces, all of which contribute to the energy landscape of the system. A consequence of the intricate energy landscape is the nontrivial effect on the solvation dynamics. Here, the impact of molecular heterogeneities on the solvation dynamics is studied using infrared spectroscopies and molecular dynamics simulations. In particular, this study focuses on the dynamical effect of nanoscopic heterogeneities present in deep eutectic solvents (DESs) composed of lauric acid (LA) and N-methylacetamide (NMA). To this end, a molecular probe containing a carbon triple bond is used as an infrared reporter. The results show that the vibrational probe is likely to be located in the NMA polar domains. Furthermore, the probe solvation dynamics derived from the 2DIR spectra presents a slowdown of its timescale with increasing LA concentration in the DES. Kubo modeling of the probe solvation dynamics shows a correlation between the amplitude of its long time component and the presence of molecular heterogeneities in the sample. Semiclassical modeling of the vibrational line shape of the triple bond stretch demonstrates that the heterogeneities affect the whole solvation dynamics of the system through the amplitudes of the frequency fluctuations. Molecular dynamics simulations confirm the experimental results and their interpretation by showing a slowdown of the solvation dynamics when the LA heterogeneities are present. Overall, the study presents a molecular framework to explain the effect of confinement created by nanoscopic LA heterogeneities on the solvation dynamics of the system.

Chemical compounds↗
DOE OSTI2026

Towards the Ultimate Strength of Medium‐Entropy Alloys Through Pulsed Lasers

The tensile strength of metals at extreme strain rates is a key predictor of their performance in ballistic and structural impact applications. An important experimental method to reach these extreme strain rates is the use of high-amplitude, short-duration pulsed lasers. The Jupiter Laser Facility at the Lawrence Livermore National Laboratory enabled probing for the first time the mechanical response of several promising High Entropy Alloys at times on the order of nanoseconds (strain rates of ∼10 7 and ∼10 9 s −1 ). The measured strength is in the range of 6 to 10 GPa, ten times the quasistatic value. The mechanisms of plastic deformation and failure were identified and quantified through analysis and molecular dynamics simulation. The reflected wave amplitudes, obtained by VISAR, were used to determine the tensile (spall) stress. The high tensile strength obtained is due to two factors: the strain-rate dependence of plastic flow and the kinetics of void nucleation, growth, and coalescence. The experimental results are compared with an analytical prediction considering both grain-interior and grain-boundary void initiation. Molecular dynamics simulations, conducted at strain rates of 10 8 and 10 9 s −1 , rationalize the experimental results. In conclusion, they provide valuable information about the process of failure evolution, and reveal that grain boundary separation plays a pivotal role in spalling.

Materials science↗
DOE OSTI2026

Dehydration-Driven Ion Aggregation and the Onset of Gelation in ZnCl2 Solution

A minimal model of ionic aggregation in concentrated ZnCl2 is developed, guided by molecular dynamics simulations with a machine-learned potential. It explicitly incorporates solvent-site depletion, correlated chloride binding, and allows for loops within Zn–Cl clusters. Dehydration drives two coordination-controlled structural changes: a crossover at Z=2 from predominantly isolated Zn-containing species to Cl-bridged clusters and the onset of gelation near Z≈3. The model reproduces the concentration-dependent trends observed in the molecular dynamics simulations with two fitted parameters, while the cluster-size distribution at the highest concentration is consistent with three-dimensional percolation scaling over the accessible range of cluster sizes.

25 ENERGY STORAGE↗
DOE OSTI2025

Spatial description of dislocation nucleation in the shock response of single-crystal aluminum

Nonequilibrium molecular dynamics simulations of shock loaded single-crystal Al in the $\langle$100$\rangle$, $\langle$110$\rangle$, $\langle$111$\rangle$, and $\langle$123$\rangle$ orientations are conducted to study elastic and plastic shockwave formation and details associated with dislocation activity. A computer vision-based approach is implemented to capture the presence of dislocations and describe their spatial characteristics in the zone of nucleation behind the propagating shockwave. The methodology developed relies on the sequences of images extracted during shock loading that show dislocation activity within a cross section of the sample. Results reveal that the spacing between activated slip systems is orientation dependent and exhibits a modest reduction for the $\langle$100$\rangle$ and $\langle$111$\rangle$ orientations as shock pressure increases. Comparisons are made to existing theoretical models. Such relationships between shock pressure and dislocation activity, extracted from molecular dynamics simulations, can be used to inform higher length scale simulations or modeling of dislocation-based plasticity during shock.

36 MATERIALS SCIENCE↗