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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Combining artificial intelligence and physics-based modeling to directly assess atomic site stabilities: from sub-nanometer clusters to extended surfaces

The performance of functional materials is dictated by chemical and structural properties of individual atomic sites. In catalysts, for instance, the thermodynamic stability of constituting atomic sites is a key descriptor from which more complex properties, such as molecular adsorption energies and reaction rates, can be derived. In this study, we present a widely applicable machine learning (ML) approach to instantaneously compute the stability of individual atomic sites in structurally and electronically complex nano-materials. Conventionally, we determine such site stabilities using computationally intensive first-principles calculations. With our approach, we predict the stability of atomic sites in sub-nanometer metal clusters of 3–55 atoms with mean absolute errors in the range of 0.11–0.14 eV. To extract physical insights from the ML model, we introduce a genetic algorithm (GA) for feature selection. This algorithm distills the key structural and chemical properties governing the stability of atomic sites in size-selected nanoparticles, allowing for physical interpretability of the models and revealing structure–property relationships. The results of the GA are generally model and materials specific. In the limit of large nanoparticles, the GA identifies features consistent with physics-based models for metal–metal interactions. By combining the ML model with the physics-based model, we predict atomic site stabilities in real time for structures ranging from sub-nanometer metal clusters (3–55 atom) to larger nanoparticles (147 to 309 atoms) to extended surfaces using a physically interpretable framework. Finally, we present a proof of principle showcasing how our approach can determine stable and active nanocatalysts across a generic materials space of structure and composition.

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

Guiding Principles for the Rational Design of Hybrid Materials: Use of DFT Methodology for Evaluating Non‐Covalent Interactions in a Uranyl Tetrahalide Model System

Abstract Together with the synthesis and experimental characterization of 14 hybrid materials containing [UO 2 X 4 ] 2− (X=Cl − and Br − ) and organic cations, we report on novel methods for determining correlation trends in their formation enthalpy (ΔH f ) and observed vibrational signatures. Δ H f values were analyzed through isothermal acid calorimetry and a Density Functional Theory+Thermodynamics (DFT+T) approach with results showing good agreement between theory and experiment. Three factors (packing efficiency, cation protonation enthalpy, and hydrogen bonding energy [ ]) were assessed as descriptors for trends in Δ H f . Results demonstrated a strong correlation between and Δ H f , highlighting the importance of hydrogen bonding networks in determining the relative stability of solid‐state hybrid materials. Lastly, we investigate how hydrogen bonding networks affect the vibrational characteristics of uranyl solid‐state materials using experimental Raman and IR spectroscopy and theoretical bond orders and find that hydrogen bonding can red‐shift U≡O stretching modes. Overall, the tightly integrated experimental and theoretical studies presented here bridge the trends in macroscopic thermodynamic energies and spectroscopic features with molecular‐level details of the geometry and electronic structure. This modeling framework forms a basis for exploring 3D hydrogen bonding as a tunable design feature in the pursuit of supramolecular materials by rational design.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Gd-Ni-Sb-SnO 2 electrocatalysts for active and selective ozone production

Direct electrochemical production of dissolved ozone could potentially provide economic wastewater treatment and sanitation or a valuable chemical oxidant. Although Ni-Sb-SnO 2 electrocatalysts have the highest known faradaic efficiencies for electrochemical ozone production, the activity and selectivity are not yet sufficient for commercial implementation. This report finds that co-doping Ni and Gd increases the ozone selectivity by a factor of three over Ni alone. These findings are the first demonstration of an active dopant other than Ni in SnO 2 . Electrochemical and physical characterization show that trends in ozone activity are caused by chemical catalysis, not morphology effects, and that conduction band alignment is not a catalytic descriptor for the system. Selective radical quenching experiments and quantum chemistry calculations of thermodynamic energies suggest that the kinetic barriers to form solution-phase intermediates are important for understanding the role of dopants in electrochemical ozone production.

42 ENGINEERING↗
DOE OSTI2023

A review of in situ/operando studies of heterogeneous catalytic hydrogenation of CO 2 to methanol

Repurposing CO 2 into chemicals, one of the Carbon Dioxide Removal (CDR) strategies, still faces significant challenges in conversion and energy efficiency due to the lack of effective catalysts and processes. Fundamental understanding through in situ/operando investigations of the reaction mechanisms and catalyst structures is pivotal for developing the efficient catalysts. This paper reviews the past and recent in situ/operando studies of methanol synthesis from heterogeneous CO 2 hydrogenation over a few typical catalysts including Cu-based, oxide-based, and noble-metal-based catalysts. With the development of high-pressure reactors, in situ/operando IR, X-ray absorption spectroscopy (XAS), X-ray diffraction (XRD), neutron diffraction and imaging have been used to reveal the surface intermediates and structures of working catalysts under CO 2 hydrogenation conditions. On the one hand, the combined operando techniques shed light on working mechanisms for some catalytic systems. On the other hand, due to the complexity of selective CO 2 hydrogenation reaction and limited sensitivity of current accessible operando techniques to the surface structure of catalysts, it is still murky about the exact nature of the active sites at the surface/interface and how different sites promote different reaction paths. Furthermore, it is concluded that new methodologies for differentiating signals from different sites, and the development of surface-sensitive techniques for high pressure reactions are needed for providing structural descriptors of highly active and selective CO 2 conversion catalysts.

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

Machine learning predictions of high-Curie-temperature materials

Technologies that function at room temperature often require magnets with a high Curie temperature, $T$ C , and can be improved with better materials. Discovering magnetic materials with a substantial $T$ C is challenging because of the large number of candidates and the cost of fabricating and testing them. Using the two largest known datasets of experimental Curie temperatures, we develop machine-learning models to make rapid $T$ C predictions solely based on the chemical composition of a material. We train a random-forest model and a k -NN one and predict on an initial dataset of over 2500 materials and then validate the model on a new dataset containing over 3000 entries. The accuracy is compared for multiple compounds' representations (“descriptors”) and regression approaches. A random-forest model provides the most accurate predictions and is not improved by dimensionality reduction or by using more complex descriptors based on atomic properties. Further, a random-forest model trained on a combination of both datasets shows that cobalt-rich and iron-rich materials have the highest Curie temperatures for all binary and ternary compounds. An analysis of the model reveals systematic error that causes the model to over-predict low-$T$ C materials and under-predict high-$T$ C materials. For exhaustive searches to find new high-$T$ C materials, analysis of the learning rate suggests either that much more data is needed or that more efficient descriptors are necessary.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Hierarchical reconstruction of 3D well-connected porous media from 2D exemplars using statistics-informed neural network

The relationships between porous microstructures and transport properties are of fundamental importance in various scientific and engineering applications. Due to the intricacy, stochasticity and heterogeneity of porous media, reliable characterization and modeling of transport properties often require a complete dataset of internal microstructure samples. However, it is often an unbearable cost to acquire sufficient 3D digital microstructures by purely using microscopic imaging systems. Herein this paper presents a machine learning-based technique to hierarchically reconstruct 3D well-connected porous microstructures from one isotropic or several anisotropic low-cost 2D exemplar(s). To compactly characterize the large-scale microstructural features, a Gaussian image pyramid is built for each 2D exemplar. Local morphology patterns are collected from the Gaussian image pyramids, and then they serve as the training data to embed the 2D morphological statistics into feed-forward neural networks at multiple length levels. By using a specially-developed morphology integration scheme, the 3D morphological statistics at different levels can be inferred from the statistics-informed neural networks. Gibbs sampling is adopted to hierarchically reconstruct 3D microstructures by using multi-level 3D morphological statistics, where the large-scale, regional and local morphological patterns are statistically generated and successively added to the same 3D random field. The proposed method is tested on a series of porous media with distinct morphologies, and the statistical equivalence between the reconstructed and the real microstructures is systematically evaluated by comparing morphological descriptors and transport properties. The results demonstrate that the proposed 2D-to-3D microstructure reconstruction method is a universal and efficient approach to generating morphologically and physically realistic samples of porous media.

42 ENGINEERING↗
DOE OSTI2022

Molecular Simulations of CH 4 and CO 2 Diffusion in Rigid Nanoporous Amorphous Materials

Molecular diffusion in nanoporous materials is important in determining the rate of equilibration of various adsorption processes and plays a pivotal role in kinetic separations and membrane-based separations. Because generating realistic structures of amorphous nanoporous materials is difficult, far less is known about diffusion in amorphous nanoporous materials than in their crystalline counterparts. Here, we present molecular dynamics simulations assessing the room-temperature self-diffusion of CH 4 and CO 2 in a wide range of rigid amorphous nanoporous materials, including porous carbons, kerogens, polymers of intrinsic microporosity, and hyper-cross-linked polymers. Our results are the largest collection of molecular diffusivities in amorphous nanoporous materials to date. In each material, the diffusivity increases with the adsorbate concentration at low and moderate adsorbate concentrations, reaching a maximum before decreasing due to steric effects at higher concentrations. The observed diffusivities are much slower than that would be expected based on standard descriptions of Knudsen diffusivity. Here we show that the observed diffusivities are not correlated in a simple way with scalar descriptors of the pore structures such as the pore limiting diameter.

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

DuctGPT: A Generative Transformer for Forward Screening of Ductile Refractory Multi-Principal Element Alloys

Designing ductile materials for extreme environments such as fusion reactors requires a deep understanding of the complex interplay between electronic structure, mechanical stability, and wide compositional space. Here, in this work, we introduce DuctGPT, a physics-informed, GPT-powered machine learning platform that enables rapid and accurate prediction of ductility across a wide range of refractory multi-principal element alloys (MPEAs). Trained on both experimental and high-fidelity computational data, DuctGPT integrates descriptors such as density of states at the Fermi level, elastic constants, and valence electron concentration to capture the fundamental mechanisms governing ductile versus brittle behavior. Using this framework, we screen over 1000 compositions in of body-centered cubic (BCC) MPEAs, including two new alloy classes, i.e., NbTa-rich (NbTa $>$ 50 at.%) NbTa-Ti-V and W-rich ($>$ 50 at.%) W-Ti-V MPEAs, to rapidly identify promising alloy compositions with enhanced ductility. Validation against experimental data confirms the model's ability to predict ductility with high fidelity and low uncertainty. By leveraging conversational AI and robust physical modeling, DuctGPT provides a blueprint for the next generation of alloy design assistants, enabling human-AI collaboration in the accelerated discovery of ductile, high-performance materials for fusion, aerospace, and advanced manufacturing.

AI/ML↗
DOE OSTI2026

Physics-informed machine learning exploration of Na storage mechanisms in disordered carbon

Sodium-ion batteries are a cost-effective, sustainable alternative to lithium-ion systems for large-scale energy storage. However, optimizing sodium storage in carbon-based anodes with microstructural complexity and atomic disorder remains a major challenge. The intrinsic inhomogeneity of these materials produces diverse local environments, making it difficult for conventional methods to predict and control ion dynamics. Hard carbon (HC) anodes, composed of ranges of ordered-to-disordered graphitic and amorphous nanodomains, offer tunable ion storage and rate capacity, yet rationale design remains a challenge due to poorly understood correlation between local atomic feature and ion transport mechanism. Here, to address this challenge, we introduce a data-driven framework that integrates validated machine-learned interatomic potentials, large-scale molecular dynamics simulations, and machine learning to elucidate sodium transport mechanisms as a function of carbon and sodium loading densities. By computing per-ion structural descriptors and applying unsupervised learning, we identify distinct diffusion modes governed by microscopic features. Supervised analysis and correlation mapping then establish quantitative links between these transport regimes and processing variables such as bulk carbon density and sodium content. This physics-informed approach establishes quantitative structure–transport relationships and offers actionable design principles for engineering high-performance HC anodes.

Data-driven framework↗
DOE OSTI2022

Understanding Metal–Organic Framework Nucleation from a Solution with Evolving Graphs

A mechanistic understanding of metal–organic framework (MOF) synthesis and scale-up remains underexplored due to the complex nature of the interactions of their building blocks. In this work, we investigate the collective assembly of building units at the early stages of MOF nucleation, using MIL-101(Cr) as a prototypical example. Using large-scale molecular dynamics simulations, we observe that the choice of solvent (water and N,N-dimethylformamide), the introduction of ions (Na+ and F–) and the relative populations of MIL-101(Cr) half-secondary building unit (half-SBU) isomers have a strong influence on the cluster formation process. Additionally, the shape, size, nucleation and growth rates, crystallinity, and short and long-range order largely vary depending on the synthesis conditions. We evaluate these properties as they naturally emerge when interpreting the self-assembly of MOF nuclei as the time evolution of an undirected graph. Solution-induced conformational complexity and ionic concentration have a dramatic effect on the morphology of clusters emerging during assembly. While pure solvents lead to the rapid formation of a small number of large clusters, the presence of ions in aqueous solutions results in smaller clusters and slower nucleation. This diversity is captured by the key features of the graph representation. Principle component analysis on graph properties reveals that only a small number of molecular descriptors is needed to deconvolute MOF self-assembly. Furthermore, descriptors such as the average coordination number between half-SBUs and fractal dimension are of particular interest as they can be can be followed experimentally by techniques like by time-resolved spectroscopy. Ultimately, graph theory emerges as an approach that can be used to understand complex processes revealing molecular descriptors accessible by both simulation and experiment.

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

DeePMD-kit v2: A software package for deep potential models

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features, such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, DP-range correction, DP long range, graphics processing unit support for customized operators, model compression, non-von Neumann molecular dynamics, and improved usability, including documentation, compiled binary packages, graphical user interfaces, and application programming interfaces. This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, this article presents a comprehensive procedure for conducting molecular dynamics as a representative application, benchmarks the accuracy and efficiency of different models, and discusses ongoing developments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Inferring the Energetics of CO 2 –Aniline Adduct Formation from Vibrational Spectroscopy

Control of atmospheric CO 2 is an important contemporary scientific and engineering challenge. Towards this goal, the reaction of CO 2 with amines to form carbamate bonds is an established method for CO 2 capture. However, controllable reversal of this reaction remains difficult and requires tuning the energetics of the carbamate bond. Through IR spectroscopy, we show that a characteristic frequency observed upon carbamate formation varies as a function of the substituent’s Hammett parameter for a family of para- substituted anilines. We present computational evidence that the vibrational frequency of the adducted CO 2 serves as a predictor of the energy of formation of the carbamate. Electron donating groups typically enhance the driving force of carbamate formation by transferring more charge to the adducted CO 2 and thus increasing the occupancy of the anti-bonding orbital in the carbon-oxygen bonds. Increased occupancy of the anti-bonding orbital within adducted CO 2 indicates a weaker bond, leading to a red shift in the characteristic carbamate frequency. As a result, our work serves the large field of CO 2 capture research where spectroscopic observables, such as IR frequencies, are more easily obtainable and can stand in as a descriptor of driving forces.

Amines↗
DOE OSTI2024

First-principles investigation of near-field energy transfer between localized quantum emitters in solids

We present a predictive and general approach to investigate near-field energy transfer processes between localized defects in semiconductors, which couples first-principles electronic structure calculations and a nonrelativistic quantum electrodynamics description of photons in the weak-coupling regime. The approach is general and can be readily applied to investigate broad classes of defects in solids. We apply our approach to investigate an exemplar point defect in an oxide, the F center in MgO, and we show that the energy transfer from a magnetic source, e.g., a rare-earth impurity, to the vacancy can lead to spin nonconserving long-lived excitations that are dominant processes in the near field, at distances relevant to the design of photonic devices and ultrahigh dense memories. We also define a descriptor for coherent energy transfer to predict geometrical configurations of emitters to enable long-lived excitations, that are useful to design optical memories in semiconductor and insulators. Published by the American Physical Society 2024

Chattaraj, Swarnabha (ORCID:0000000329333581)↗
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 OSTI2022

Rheo-Structural Spectroscopy: Fingerprinting the In Situ Response of Fluids to Arbitrary Flow Fields

The objectives of this project were to develop new sample environments, measurement methodologies and associated modeling tools for characterizing the structural response to arbitrarily complex processing flows using small angle scattering, and to apply these new tools for understanding the fundamental physics governing the structuring of anisotropic particulate and polymeric materials under flow histories and conditions relevant to industrial processing flows. The research resulted in the development and implementation of a new sample environment, the fluidic four roll mill (FFoRM), for in situ small angle neutron and X-ray scattering (SANS/SAXS) measurements. These measurements are capable of generating large data sets that “fingerprint” how a complex fluid responds to a wide range of flow histories involving time variations in deformation type and rate. New modeling tools were developed to extract detailed microstructural information from such data sets, including orientation distribution functions and interparticle correlation functions, as well as reduced-order parametric descriptors of these high-dimensional functions that can be used to readily map, visualize and interpret a fluid’s structural response to its flow history. These new tools were applied to a range of model materials involving elongated particle suspensions in order to provide new insights into the physics of how flow couples with orientational and structural order in complex flows, particularly under non-dilute conditions for which no accurate theories currently exist. Using these investigations, we elucidated a number of new insights into the fundamental phenomena driving such process-structure-property relationships. These findings provide guidance for the further development of rheological models, and ultimately can inform the rational and model-based design of flow processes to achieve optimized orientational ordering that is key to the properties and function of a wide range of energy-relevant materials.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Machine learning approach for vibronically renormalized electronic band structures

Here, we present a machine learning (ML) method for efficient computation of vibrational thermal expectation values of physical properties from first principles. Our approach is based on the nonperturbative frozen phonon formulation in which stochastic Monte Carlo algorithm is employed to sample configurations of nuclei in a supercell at finite temperatures based on a first-principles phonon model. A deep-learning neural network is trained to accurately predict physical properties associated with sampled phonon configurations, thus bypassing the time-consuming ab initio calculations. To incorporate the point-group symmetry of the electronic system into the ML model, group-theoretical methods are used to develop a symmetry-invariant descriptor for phonon configurations in the supercell. We apply our ML approach to compute the temperature dependent electronic energy gap of silicon based on density functional theory (DFT). We show that, with less than a hundred DFT calculations for training the neural network model, an order of magnitude larger number of sampling can be achieved for the computation of the vibrational thermal expectation values. Our work highlights the promising potential of ML techniques for finite temperature first-principles electronic structure methods.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗