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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Exploration of the Application of Machine Learning to the improvement of Interatomic Potentials

Current methods for atomistic simulations of material systems suffer from limitations which restrict the ability of the simulation to correctly characterize certain material behavior and physical phenomena. Small scale ab initio (AIMD) modeling is highly accurate but is computationally expensive. Classical molecular dynamics (CMD) simulations use interatomic potentials (IPs) to describe larger systems at a reduced computational cost, but with a reduced accuracy. Creating more robust IPs enable more precise CMD simulations. In this thesis, the application of a specific machine learning process,artifical neural networks (ANNs), to the improvement of IPs and MD simulation is discussed.

O'Connor, Sean↗
NASA NTRS1997

Atomistic Simulations of Ti Additions to NiAl

The development of more efficient engines and power plants for future supersonic transports depends on the advancement of new high-temperature materials with temperature capabilities exceeding those of Ni-based superalloys. Having theoretical modelling techniques to aid in the design of these alloys would greatly facilitate this development. The present paper discusses a successful attempt to correlate theoretical predictions of alloy properties with experimental confirmation for ternary NiAl-Ti alloys. The B.F.S. (Bozzolo-Ferrante-Smith) method for alloys is used to predict the solubility limit and site preference energies for Ti additions of 1 to 25 at.% to NiAl. The results show the solubility limit to be around 5% Ti, above which the formation of Heusler precipitates is favored. These results were confirmed by transmission electron microscopy performed on a series of NiAl-Ti alloys.

Bozzolo, Guillermo↗
DOE OSTI2025

Ab Initio Structures and Energetics of Hydrated Flat and Terrace-Step Surfaces of Forsterite (Mg 2 SiO 4 )

Forsterite (Mg 2 SiO 4 ), a model divalent metal silicate mineral, has been extensively studied in the context of mineral carbonation. Although dissolution is a key step in this process, the mechanisms by which forsterite dissolves under high CO 2 conditions remain poorly understood. Atomistic simulations could aid in exploring these mechanisms, but it is essential first to understand the structures and energetics of the relevant forsterite surfaces. We present an ab initio study of the structure and surface energy at 0 K of the flat $(010), (110), (001), (111), (021), (101)$ and $(120)$ faces of forsterite using the density functional PBE Hamiltonian and a plane-wave basis set. Dry surfaces became stabilized upon hydration through the formation of bonds between surface Mg and O from water, as well as by the formation of hydrogen bonds. According to surface energy values, the stability order of the hydrated forsterite faces was found to be $(120) < (101) < (021) < (111) < (001) < (110) < (010)$. We also investigated the energetics of the terrace-step $(0\bar{41})$ surface as a model site for forsterite dissolution. Among all the facets, the $(0\bar{41})$ surface is the least stable termination in water. Hydration of Mg atoms on the $(0\bar{41})$ surface increases their susceptibility to dissolution. The presence of a step and its hydration destabilizes the terraces, making step retreat more likely than a dissolution front advancing along the [010] direction. This research will support future simulations to investigate forsterite dissolution in water under CO 2 -rich conditions.

PBE Hamiltonian↗
DOE OSTI2026

Hierarchical Multi-agent Large Language Model Reasoning for Autonomous Heterogeneous Catalyst Discovery

Artificial intelligence is reshaping scientific exploration, but most methods automate procedural tasks without engaging in scientific reasoning, limiting autonomy in discovery. We demonstrate that hierarchical agentic large language model reasoning can efficiently drive simulation and scientific exploration. Across two chemical applications, CO adsorption on Cu surface transition metal adatoms and on M–N–C catalysts, reasoning-guided exploration reduces required atomistic simulations by up to 90% relative to heuristic or random selection. Comparisons across single-agent, multi-agent, and stochastic baselines show that hierarchical strategies yield more coherent and information-efficient search trajectories. Reasoning traces reveal chemically grounded decisions that cannot be explained by semantic bias or stochastic sampling. We realize these agentic reasoning strategies in Materials Agents for Simulation and Theory in Electronic-structure Reasoning (MASTER), a multimodal system that translates natural language into density functional theory workflows. Altogether, multi-agent collaboration accelerates heterogeneous catalyst discovery and marks a step toward more autonomous, reasoning-guided scientific exploration.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2025

Proton radiation effects in indium oxide using cascade molecular dynamics simulations

Metal oxide (MO) semiconductors, characterized by their wide band gaps and notable charge transport properties, are promising candidates for electronic applications in extreme environments, including near-Earth space. However, atomistic simulations of radiation–matter interactions in MOs remain challenging due to the limitations of existing interatomic potentials, which often fail to capture both the short-range repulsive forces essential for radiation damage modeling and the long-range electrostatic effects governing defect evolution. In this work, we develop a customized interatomic potential tailored for radiation damage simulations in indium oxide (In 2 O 3 ) as a model system, a representative MO material. Our potential integrates the Ziegler-Biersack-Littmark potential to accurately describe short-range interactions with Buckingham and Coulombic potentials to account for long-range forces. We perform molecular dynamics simulations of low-energy proton irradiation using this custom potential. We employ the primary knock-on atom (PKA) cascade method to study atomic displacements and primary defect formation. Simulations were conducted for 1 keV proton irradiation in four randomly chosen directions, and PKA-driven defect analyses at 5, 10, and 15 keV to examine the effects of direction and energy level on damage generation. Our results provide insight into the impact of irradiation direction and energy level on the cascade evolution and defect formation mechanisms.

Atomistic simulations↗
DOE OSTI2024

Screening Cu-Zeolites for Methane Activation Using Curriculum-Based Training

Machine learning (ML), when used synergistically with atomistic simulations, has recently emerged as a powerful tool for accelerated catalyst discovery. However, the application of these techniques has been limited by the lack of interpretable and transferable ML models. In this work, we propose a curriculum-based training (CBT) philosophy to systematically develop reactive machine learning potentials (rMLPs) for high-throughput screening of zeolite catalysts. Our CBT approach combines several different types of calculations to gradually teach the ML model about the relevant regions of the reactive potential energy surface. The resulting rMLPs are accurate, transferable, and interpretable. We further demonstrate the effectiveness of this approach by exhaustively screening thousands of [CuOCu] 2+ sites across hundreds of Cu-zeolites for the industrially relevant methane activation reaction. Specifically, this large-scale analysis of the entire International Zeolite Association (IZA) database identifies a set of previously unexplored zeolites (i.e., MEI, ATN, EWO, and CAS) that show the highest ensemble-averaged rates for [CuOCu] 2+ -catalyzed methane activation. We believe that this CBT philosophy can be generally applied to other zeolite-catalyzed reactions and, subsequently, to other types of heterogeneous catalysts. Thus, this represents an important step toward overcoming the long-standing barriers within the computational heterogeneous catalysis community.

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

Polariton-assisted incoherent to coherent excitation energy transfer between colloidal nanocrystal quantum dots

Here, we explore the dynamics of energy transfer between two nanocrystal quantum dots placed within an optical microcavity. By adjusting the coupling strength between the cavity photon mode and the quantum dots, we have the capacity to fine-tune the effective coupling between the donor and acceptor. Introducing a non-adiabatic parameter, γ, governed by the coupling to the cavity mode, we demonstrate the system’s capability to shift from the overdamped Förster regime (γ $\ll$ 1) to an underdamped coherent regime (γ $\gg$ 1). In the latter regime, characterized by swift energy transfer rates, the dynamics are influenced by decoherence time. To illustrate this, we study the exciton energy transfer dynamics between two closely positioned CdSe/CdS core/shell quantum dots with sizes and separations relevant to experimental conditions. Employing an atomistic approach, we calculate the excitonic level arrangement, exciton–phonon interactions, and transition dipole moments of the quantum dots within the microcavity. These parameters are then utilized to define a model Hamiltonian. Subsequently, we apply a generalized non-Markovian quantum Redfield equation to delineate the dynamics within the polaritonic framework.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Combining machine-learned and empirical force fields with the parareal algorithm: application to the diffusion of atomistic defects

We numerically investigate an adaptive version of the parareal algorithm in the context of molecular dynamics. This adaptive variant has been originally introduced in [1]. We focus here on test cases of physical interest where the dynamics of the system is modelled by the Langevin equation and is simulated using the molecular dynamics software LAMMPS. In this work, the parareal algorithm uses a family of machine-learning spectral neighbor analysis potentials (SNAP) as fine, reference, potentials and embedded-atom method potentials (EAM) as coarse potentials. We consider a self-interstitial atom in a tungsten lattice and compute the average residence time of the system in metastable states. Our numerical results demonstrate significant computational gains using the adaptive parareal algorithm in comparison to a sequential integration of the Langevin dynamics. We also identify a large regime of numerical parameters for which statistical accuracy is reached without being a consequence of trajectorial accuracy.

36 MATERIALS SCIENCE↗
DOE OSTI2020

On-the-fly active learning of interpretable Bayesian force fields for atomistic rare events

Abstract Machine learned force fields typically require manual construction of training sets consisting of thousands of first principles calculations, which can result in low training efficiency and unpredictable errors when applied to structures not represented in the training set of the model. This severely limits the practical application of these models in systems with dynamics governed by important rare events, such as chemical reactions and diffusion. We present an adaptive Bayesian inference method for automating the training of interpretable, low-dimensional, and multi-element interatomic force fields using structures drawn on the fly from molecular dynamics simulations. Within an active learning framework, the internal uncertainty of a Gaussian process regression model is used to decide whether to accept the model prediction or to perform a first principles calculation to augment the training set of the model. The method is applied to a range of single- and multi-element systems and shown to achieve a favorable balance of accuracy and computational efficiency, while requiring a minimal amount of ab initio training data. We provide a fully open-source implementation of our method, as well as a procedure to map trained models to computationally efficient tabulated force fields.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Irradiation-induced chemical disordering in ceramics: The case of SiC

Chemical disordering is one feature of damage that occurs in irradiation environments and is common in ceramic compounds. This paper reviews irradiation-induced chemical disordering in SiC-based materials at elevated temperatures. Here the results were obtained using advanced analytical tools to characterize atomistic defects and to evaluate the chemistry of defect clusters and homonuclear bond structures. Finally, this paper demonstrates that chemical disordering is crucial to understanding microstructural stability and establishing a mechanistic modeling of microstructural evolution of irradiated SiC.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Evaluating the Structural Response of Amphiphilic Monolayers to Environmental Stimuli

Amphiphilic monolayers composed of end groups with distinct polar and nonpolar functional groups offer rapid and reversible interfacial adaptation in response to environmental stimuli such as a change in interfacial medium polarity. We have synthesized and characterized a suite of monolayers with functional groups of competing polarity designed to reconfigure their interfacial chemical composition in response to solvent polarity. In these films, the end group is designed to be able to reorient and expose the functional groups that minimize the interfacial free energy between the film and the environment. Using a combination of spectroscopic, computational, and wettability studies, we have investigated the responsive interfacial behavior of different end groups upon exposure to environments with varying polarities. Contact angle measurements across a series of polar and dispersive probe liquids reveal trends that reflect the underlying molecular flexibility and composition. Vibrational sum frequency generation (SFG) spectroscopy and atomistic molecular dynamics (MD) simulations confirm solvent-driven reorientation of the end groups, with restructuring observed at the interface. To quantify these effects, we have developed a surface energy calculation model that incorporates solvent-induced surface rearrangements into the estimations. Our findings reveal a strong dependence of surface energy and switching behavior on the length and flexibility of the functionalities in the end group, which affects the exposure of certain interfacial compositions under different solvents. These results offer new insights into the design of adaptive monolayers and provide a framework for evaluating solvent-responsive surfaces.

functional groups↗
NASA NTRS2012

Development and Application of Interatomic Potentials for Ultra High Temperature Ceramics (UHTC): ZrB2 and HfB2

Ultra high temperature ceramics (UHTC) including ZrB2 and HfB2 are characterized by high melting point, good strength, and reasonable oxidation resistance. These materials are of interest for use as sharp leading edges for hypersonic vehicles among other applications. Progress in computational modeling of UHTCs has been limited in part due to the absence of suitable interatomic potentials. We present a Tersoff style parameterization of such potentials for ZrB2 and HfB2 appropriate for atomistic simulations. Parameters are fit to data generated from ab initio calculations. The accuracy of the potentials is assessed against further ab initio data. As a first non ]trivial application, molecular dynamics simulations are performed to evaluate the thermal conductivity of single crystals and the thermal resistance of high symmetry grain boundaries.

Lawson, John W.↗
DOE OSTI2022

Molecular-based analysis of nanoparticle solvation: Classical density functional approach

Proper statistical mechanics understanding of nanoparticle solvation processes requires an accurate description of the molecular structure of the solvent. Achieving this goal with standard molecular dynamics (MD) simulation methods is challenging due to large length scales. An alternative approach to this problem can be formulated using classical density functional theory (cDFT), where a full configurational description of the positions of all the atoms is replaced by collective atomic site densities in the molecule. Using an example of the negatively charged silica-like system in an aqueous polar environment represented by a two-site water model, we demonstrate here that cDFT can reproduce MD data at a fraction of the computational cost. An important implication of this result is the ability to understand how the solvent molecular features may affect the system’s properties at the macroscopic scale. A concrete example highlighted in this work is the analysis of nanoparticle interactions with sizes of up to 100 nm in diameter.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
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 OSTI2021

Atomistic Mechanics of Torn Back Folded Edges of Triangular Voids in Monolayer WS 2

Triangular nanovoids in 2D materials transition metal dichalcogenides have vertex points that cause stress concentration and lead to sharp crack propagation and failure. Here, the atomistic mechanics of back folding around triangular nanovoids in monolayer WS 2 sheets is examined. Combining atomic-resolution images from annular dark-field scanning transmission electron microscopy with reactive molecular modelling, it is revealed that the folding edge formation has statistical preferences under geometric conditions based on the orientation mismatch. It is further investigated how loading directions and strong interlayer friction, interplay with WS 2 lattice's crack preference, govern the deformation and fracture pattern around folding edges. Finally, these results provide fundamental insights into the combination of fracture and folding in flexible monolayer crystals and the resultant Moiré lattices.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Direct Observation of Electron Beam-Induced Phase Transition in MgCrMnO 4

Irreversible structural transformation in intercalation-type cathode materials, which has been frequently observed, has been perceived as a principal cause of capacity fading and voltage decay in (uni) multivalent batteries. Herein, we explored the electron beam-induced spinel to defective rocksalt phase transitions in MgCrMnO 4 , a potential multivalent cation intercalation cathode, using atomic-resolution imaging and spectroscopy in an aberration-corrected scanning transmission electron microscope. This dynamic electron beam irradiation study of specific structural transformations provides an atomistic understanding of the structural evolution observed in transition-metal oxide spinels during electrochemical cycling using multivalent cations, such as Mg 2+ . By combining an imaging study with first-principles modeling, we demonstrate that the mechanism of the spinel to defective rocksalt transformation in MgCrMnO 4 nanostructures is enabled by the presence of oxygen vacancies and is, therefore, very similar to that observed in transition-metal oxide spinels upon Li intercalation.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Fast predictions of liquid-phase acid-catalyzed reaction rates using molecular dynamics simulations and convolutional neural networks

The rates of liquid-phase, acid-catalyzed reactions relevant to the upgrading of biomass into high-value chemicals are highly sensitive to solvent composition and identifying suitable solvent mixtures is theoretically and experimentally challenging. We show that the complex atomistic configurations of reactant–solvent environments generated by classical molecular dynamics simulations can be exploited by 3D convolutional neural networks to enable accurate predictions of Brønsted acid-catalyzed reaction rates for model biomass compounds. We develop a 3D convolutional neural network, which we call SolventNet, and train it to predict acid-catalyzed reaction rates using experimental reaction data and corresponding molecular dynamics simulation data for seven biomass-derived oxygenates in water–cosolvent mixtures. We show that SolventNet can predict reaction rates for additional reactants and solvent systems an order of magnitude faster than prior simulation methods. This combination of machine learning with molecular dynamics enables the rapid, high-throughput screening of solvent systems and identification of improved biomass conversion conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗