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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Featureless adaptive optimization accelerates functional electronic materials design

Electronic materials that exhibit phase transitions between metastable states (e.g., metal-insulator transition materials with abrupt electrical resistivity transformations) are challenging to decode. For these materials, conventional machine learning methods display limited predictive capability due to data scarcity and the absence of features that impede model training. In this article, we demonstrate a discovery strategy based on multi-objective Bayesian optimization to directly circumvent these bottlenecks by utilizing latent variable Gaussian processes combined with high-fidelity electronic structure calculations for validation in the chalcogenide lacunar spinel family. We directly and simultaneously learn phase stability and bandgap tunability from chemical composition alone to efficiently discover all superior compositions on the design Pareto front. Previously unidentified electronic transitions also emerge from our featureless adaptive optimization engine. Our methodology readily generalizes to optimization of multiple properties, enabling co-design of complex multifunctional materials, especially where prior data is sparse.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Electronic properties of corundum-like Ir 2 O 3 and Ir 2 O 3 -Ga 2 O 3 alloys

In the hexagonal, corundum-like structure, α-Ga 2 O 3 has a bandgap of ~5.1 eV, which, combined with its relatively small electron effective mass, high Baliga's figure of merit, and high breakdown field, makes it a promising candidate for power electronics. Ga 2 O 3 is easy to dope n-type, but impossible to dope p-type, impeding the realization of some electronic device designs. Developing a lattice-matched p-type material that forms a high-quality heterojunction with n-type Ga 2 O 3 would open new opportunities in electronics and perhaps optoelectronic devices. In this work, we studied Ir 2 O 3 as a candidate for that purpose. Using hybrid density functional theory calculations we predict the electronic band structure of α-Ir 2 O 3 and compare that to α-Ga 2 O 3 , and study the stability and electronic properties of α-(Ir x Ga 1–x ) 2 O 3 alloys. We discuss the band offset between the two materials and compare it with recently available experimental data. We find that the Ir d bands that compose the top of the valence band in α-Ir 2 O 3 are much higher in energy than O p bands in α-Ga 2 O 3 , possibly enabling effective p-type doping. Finally, our results provide an insight into using the Ir 2 O 3 or Ir 2 O 3 -Ga 2 O 3 alloys as p-type material lattice-matched to α-Ga 2 O 3 for the realization of p–n heterojunctions.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Development of neural network force fields for corrosion studies

To fully understand the chemistry and physics of corrosion, novel methods of simulation must be developed. One approach is designing machine learning (ML) algorithms integrated with density functional theory to develop adaptive force fields to gain insight into corrosion behavior namely at the surface of metal oxides. Current methods of modeling corrosion are slow due to the computational cost of resolving both reaction mechanics and mass transport processes. Machine learning methods can be implemented to obtain structure-activity relationships at both the molecular and bulk scale while still retaining the accuracy of density functional theory (DFT) and significantly decreasing the time needed for simulations of complex chemical processes in the various environments of corrosion. Multiscale models are needed for corrosion studies to fully understand its processes not only at the atomic length scale (chemical bonding, energies, and forces), but also at the nano and meso length scales (solid-state physics and material science processes). Current methods of study include DFT, molecular dynamics, and Monte Carlo. The limitation of DFT is that only a small number of atoms or molecules can be simulated at that level of theory. Density functional theory is used to study the electronic structure of atoms and molecules, and calculate the force component of each atom. However, these calculations are limited to about 1000 atoms. Custom periodic boundary conditions (PBC) can be used to describe the various environments and defects that affect the atomic forces to produce a large data set from which a training set can be derived. Machine learning can be utilized to overcome the barrier of modeling macroscopic and multi-scale processes from ab initio calculations through the development of adaptive force fields. Local environments determine the atomic forces of a given system, therefore adaptive force fields must be created to produce reliable quantum mechanical calculations. This can be achieved by developing a learning algorithm that uses the mapped atomic forces or fingerprint as an input to produce energies and magnetic moments as output. A systematic approach was used to begin to build a data set in order to accurately describe the atomic forces in various environments. In Figure 4 below, a simple PBC cell of Fe{sub 2}O{sub 3} was first optimized. A surface optimization was performed next, followed by a hydroxylated surface optimization. Once this calculation has converged, the adsorption of halide species to the hydroxylated surface will be investigated. TensorFlow is an open source platform for machine learning developed by Google. Using a high level application program interface (API) such as Keras allows for building and training ML models easily in a number of different environments and languages. For this project, a neural network was developed within Anaconda in Python. Future Work: Further development of reference data set; Refining neural network and learning algorithm; Fingerprinting atomic environment to enable mapping of atomic force components; Choosing appropriate training set from reference data; Learning from training set and enabling non-linear mapping of training set fingerprints and the atomic forces; Estimation of uncertainty to identify ranges of outside applicability; Testing and analysis of molecular dynamic simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2024

MnRhBi 3 : A Cleavable Antiferromagnetic Metal

Cleavable metallic antiferromagnets may be of use for low-dissipation spintronic devices; however, few are currently known. Here, in this study, we present orthorhombic MnRhBi 3 as one such compound and present a thorough study of its physical properties. Exfoliation is demonstrated experimentally, and the cleavage energy and electronic structure are examined by density functional theory calculations. It is concluded that MnRhBi 3 is a van der Waals-layered material that cleaves easily between neighboring Bi layers and that the Bi atoms have lone pairs extending into the van der Waals gaps. A series of four phase transitions are observed below room temperature, and neutron diffraction shows that at least two of the transitions involve the formation of antiferromagnetic order. Anomalous thermal expansion points to a crystallographic phase transition and/or strong magnetoelastic coupling. This work reveals a complex phase evolution in MnRhBi 3 and establishes this cleavable antiferromagnetic metal as an interesting material for studying the interplay of structure, magnetism, and transport in the bulk and ultrathin limits, as well as the role of lone pair electrons in interface chemistry and proximity effects in van der Waals heterostructures.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Machine learning for accuracy in density functional approximations

Machine learning techniques have found their way into computational chemistry as indispensable tools to accelerate atomistic simulations and materials design. In addition, machine learning approaches hold the potential to boost the predictive power of computationally efficient electronic structure methods, such as density functional theory, to chemical accuracy and to correct for fundamental errors in density functional approaches. In this paper, recent progress in applying machine learning to improve the accuracy of density functional and related approximations is reviewed. Promises and challenges in devising machine learning models transferable between different chemistries and materials classes are discussed with the help of examples applying promising models to systems far outside their training sets.

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

From Oxo to Oxyl to Biradical: Systematic Multireference Calculations of Methane Activation at MOF Nodes

Methane C–H activation at transition-metal sites often involves electronic structures that challenge conventional single-reference electronic structure descriptions. Although Kohn–Sham density functional theory (DFT) is widely used to study catalytic trends, its reliability for reactions involving strongly correlated species remains uncertain. Here we present a systematic multireference investigation of methane activation at metal–organic framework (MOF) node catalysts across the 3d transition-metal series. We introduce an automated workflow for active space selection to enable consistent application of multireference methods, including multiconfiguration pair-density functional theory and n-electron valence state perturbation theory, to these catalytic systems. These calculations show substantial static correlation in the C–H activation reaction step and predict activation barriers that differ from DFT by 30–70 kJ mol–1, with DFT often qualitatively disagreeing in barrier height trends across transition metals. Analysis of multireference wave functions shows that reactivity is governed by the electronic structure of the M–O moiety along a continuum from metal–oxo to oxyl radical and O biradical character. Increased oxygen-centered spin density and weakened M–O bonding are identified as descriptors of catalytic activity which correlate with lower activation barriers.

Wardzala, Jacob↗
DOE OSTI2024

Hybrid programming-model strategies for GPU offloading of electronic structure calculation kernels

To address the challenge of performance portability and facilitate the implementation of electronic structure solvers, we developed the basic matrix library (BML) and Parallel, Rapid O(N), and Graph-based Recursive Electronic Structure Solver (PROGRESS) library. The BML implements linear algebra operations necessary for electronic structure kernels using a unified user interface for various matrix formats (dense and sparse) and architectures (CPUs and GPUs). Focusing on density functional theory and tight-binding models, PROGRESS implements several solvers for computing the single-particle density matrix and relies on BML. In this paper, we describe the general strategies used for these implementations on various computer architectures, using OpenMP target functionalities on GPUs, in conjunction with third-party libraries to handle performance critical numerical kernels. In this study, we demonstrate the portability of this approach and its performance in benchmark problems.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Lattice vacancy migration barriers in Fe-Ni alloys, and an indication as to why Ni atoms diffuse slowly: A first-principles study

Lattice vacancy migration barriers in ferromagnetic Fe 𝑥 ⁢Ni 1−𝑥 alloys (0.4 ≤ 𝑥 ≤ 0.6) are accurately quantified within the framework of ab initio electronic structure calculations using the nudged elastic band (NEB) method. Both the atomically disordered (A1) fcc phase, as well as the atomically ordered, tetragonal L⁢1 0 phase—which is under consideration as a material for a rare-earth-free gap magnet for advanced engineering applications—are investigated. Across an ensemble of NEB calculations performed on supercell configurations spanning a range of compositions and containing disordered, partially ordered, and fully ordered structures, we find that Ni-vacancy interchanges encounter significantly higher energetic barriers than do Fe-vacancy interchanges. We contend that this aspect is a key factor in determining the differences in mobility between Fe and Ni atoms in this ferromagnetic alloy. Moreover, we are able to interpret these findings in terms of the ferromagnetic alloy's underlying spin-polarized electronic structure. Specifically, we report a coupling between the size of local lattice distortions and the magnitude of the local electronic spin polarization around vacancies. This causes Fe atoms to relax into lattice vacancies, while Ni atoms remain rigidly fixed to their original lattice positions. These results give atomic-scale insight into the longstanding experimental observation that Ni exhibits remarkably slow atomic diffusion in Fe-Ni alloys.

density functional theory↗
DOE OSTI2023

Study of the structure, structural transition, interface model, and magnetic moments of CrN grown on MgO(001) by molecular beam epitaxy

Structural phase transition is studied in high quality CrN thin films grown by molecular beam epitaxy on MgO(001) substrates. Cross-sectional transmission electron microscopy and x-ray diffraction reveal that the epitaxial relationship between CrN film and MgO substrate is [100] CrN /[100] MgO , [110] CrN /[110] MgO , and [001] CrN /[001] MgO . The films show tensile strain/compression at the CrN/MgO(001) interface, which relaxes gradually with the film growth. Temperature dependent x-ray diffraction measurements show a first-order structural phase transition. In addition to the experimental measurements, first-principles theoretical calculations have been carried out for finding a stable model for the CrN/MgO interface. Furthermore, these calculations determine two possible models for the interface, where a monolayer of chromium oxide is formed between the CrN and MgO layers.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Reimagining the $e_g$ 1 Electronic State in Oxygen Evolution Catalysis: Oxidation-State-Modulated Superlattices as a New Type of Heterostructure for Maximizing Catalysis

We report that the discovery of solid-phase, inexpensive transition-metal-based water oxidation catalysts is a central goal for renewable energy, and has led to a general consensus that a partially populated metal e g d-electronic state is desirable, leading to favorable catalysis for certain elements in specific oxidation states. In manganese systems, the key species is manganese(III), whose high-spin d 4 electronic configuration places an unpaired electron in the e g orbital, which is postulated to contribute to electronic and structural features that support catalysis. Based on density functional theory calculations, it is predicted that electron transfer would be facilitated by a catalyst with alternating low- and high-Mn III -content sheets, which positions neighboring band edges in closer energetic proximity. The preparation of such catalysts is demonstrated for the first time and it is shown that the catalytic activity is maximized in these systems over more uniform, but more Mn III -rich systems. The best catalyst possesses alternating high-and low-average oxidation state sheets with interlayer Cs + ions, and has an overpotential of 450 mV at 10 mA, which represents an improvement of 250 mV over the best unmodified synthetic potassium birnessites. Using scanning tunneling spectroscopy, bandgap modulations consistent with the theoretically predicted band edge shifts are detected.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Molecular Modifications of Crystalline Poly(triazine imide) for Advancing Its Structure–Property Relationships in Light-Driven Catalysis

Carbon-nitride materials represent light-absorbing structures composed of earth-abundant elements capable of being leveraged for semiconductor photocatalysis at their surfaces. This study systematically investigates the addition of molecular modifiers to the synthesis of crystalline carbon nitrides to assess their effects on the materials’ structure, optical bandgap, and photocatalytic activity for hydrogen (H 2 ) and oxygen (O 2 ) evolution under ultraviolet and visible-light irradiation. Melamine and five pyrimidine-centered analogs were employed as building blocks to modify various heteroatoms within the polymeric framework. The modified materials were characterized with attention to the differences introduced by the monomeric modifiers and their influence on the resulting structures and compositions. The findings indicate that these changes significantly broaden the visible-light absorption range, albeit with the gradual loss of the bulk crystalline structure. As the loading of modifiers increased beyond 50%, a predominantly amorphous form of carbon nitride emerged. XPS, 13 C solid-state NMR, and SEM analyses corroborated the changes, which were attributed to modifications of the elemental composition and a reduced amount of Li cations and charge-balancing Cl anions owing to fewer binding sites in the intralayer cavities. In photocatalytic measurements under an ultraviolet 390 nm LED, and aided by photodeposited nanoparticle cocatalysts, the unmodified PTI-LiCl framework demonstrated the highest H 2 evolution rate (HER; 3.44 mmol·g –1 ·h –1 ) with an apparent quantum yield of 5.4%, along with total water splitting at rates of 163 μmol of H 2 ·g –1 ·h –1 and 75.6 μmol·O 2 g –1 ·h –1 . While PTI-LiCl showed trace activity under a visible-light 440 nm LED, all modified materials exhibited enhanced reactivity with as low as 5% molecular modifiers. The photocatalytic rates peaked at a 15% modification level when using 2,4,6-triaminopyrimidine, with rates of 33 μmol·g –1 ·h –1 for HER, along with 19.7 μmol of H 2 ·g –1 ·h –1 and 8.7 μmol of O 2 ·g –1 ·h –1 for total water splitting. Density functional theory calculations were used to probe electronic structure changes resulting from the modifications. Furthermore, these results elucidate the structural, optical, and electronic changes arising from the five selected molecular modifiers and their impact on the semiconductors’ photocatalytic properties.

Electrical conductivity↗
DOE OSTI2023

Spin and orbital metallic magnetism in rhombohedral trilayer graphene

We provide a theoretical interpretation of the metallic broken spin-valley (flavor) symmetry states recently discovered in hole-doped rhombohedral trilayer (ABC) graphene in large electric displacement fields. Our conclusions about the phase diagram and phase transitions combine insights from ABC graphene electronic structure models and mean-field theory, and are guided by the precise magneto-oscillation Fermi-surface-area measurements of recent experiments. We find that the principle of momentum-space condensation plays a key role in determining Fermi-surface reconstructions enabled by broken flavor symmetries when the single-particle bands imply thin annular Fermi seas. The reconstructed Fermi sea consists of one large outer Fermi-surface-enclosed majority-flavor states in reciprocal-space area A maj and one or more small inner holelike Fermi-surface-enclosed minority-flavor states in A min that are primarily responsible for nematic order. The competing ground states (valley-Ising, valley-XY, and spin-polarized state) have different A maj /A min and exchange energy maximizes this ratio and selects valley-XY nematic metal as the lowest-energy state. Here, we discuss how the nematic pockets explain the observed fractionalization of quantum oscillation frequencies, and propose anisotropic transport and the nonlinear Hall effect as additional observables.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Phonon spectrum in the spin-Peierls phase of CuGeO 3

CuGeO 3 has long been studied as a prototypical example of the spin-Peierls transition in a 𝑆 = 1/2 Heisenberg chain. Despite intensive investigation of this quasi-one-dimensional material, systematic measurements and calculations of the phonon excitations in the dimerized phase have not to date been possible, leaving certain aspects of the spin-Peierls phenomenon unresolved. We perform state-of-the-art density functional theory (DFT) calculations to compute the electronic structure and phonon dynamics in the low-temperature dimerized phase. We also perform high-resolution neutron spectroscopy to measure the full phonon spectrum over multiple Brillouin zones. We find excellent agreement between our numerical and experimental results that extend to all measurement temperatures. Notable features of our phonon spectra include a number of steeply dispersive modes, nonmonotonic dispersion features, and specific phonon anticrossings, which we relate to the mode eigenvectors. By calculating the magnetic interactions within DFT and studying the effects of different phonon modes on the superexchange paths, we discuss the possibility of observing spin-phonon hybridization effects in experiments performed both in and out of equilibrium.

density functional theory↗
DOE OSTI2023

Theory and Simulation of Ultrafast Multidimensional Nonlinear X-ray Spectroscopy of Molecules (Final Report)

Emerging X-ray free electron laser (XFEL) beam sources offer new types of probes of matter with unprecedented spatial and temporal resolutions. These experimental advances must be met by robust theoretical and computational tools that provide predictive modeling capacity of the underlining electronic and structural dynamics. The latter will be essential for the design of sophisticated multi-pulse experiments and for their interpretation. The proposed research effort will focus on developing cutting- edge simulation tools for nonlinear multidimensional X-ray/optical spectroscopies and aims to address key questions in Priority Research Opportunities 1 (Probing and controlling electron motion within a molecule) and 3 (Capturing rare events and intermediate states in the transformation of matter) as mentioned in the BES Roundtable Report “Opportunities for Basic Research at the Frontiers of XFEL Ultrafast Science”. XFEL multidimensional nonlinear techniques, which combine sequences of X-ray and possibly optical pulses, provide a unique experimental toolbox for probing the dynamics of core and valence electronic excitations, as well as material structure. Predictive modeling of these dynamical processes requires the combination of analytical theory for nonlinear interactions of light and matter, robust quantum-chemical methodologies for the accurate description of electronic structure of various materials, and multiscale ab initio electron and nuclear dynamics techniques operating beyond Born-Oppenheimer approximation. These challenges will be addressed with three research thrusts (i) Develop and implement theoretical apparatus for modeling a broad range of multidimensional spectroscopic techniques enabled by present and upcoming XFEL facilities. This thrust also includes the incorporation of a computational module in the DOE supported open-source NWChem computational chemistry package as well as the development of other open-source codes ready for dissemination across a broad user base; (ii) Propose and design new multi-pulse experiments that make use of the capabilities of the incoming LCLS-II facility; (iii) Perform selected applications to specific molecular systems that can be carried out at LCLS-II and demonstrate how these X-ray sources may be used to study nonadiabatic dynamics through conical interactions, electronic correlations in multi-core excitons, and charge transfer/energy transfer processes. The proposed research will be carried out by a multi-disciplinary four-institution research team which combines academia and national laboratories and spans the broad and necessary expertise in theoretical spectroscopy, nonlinear optics, quantum chemistry, molecular non-adiabatic dynamics and code development. The work will be performed in a highly interactive team environment with junior researchers shared between institutions thus cementing cross- disciplinary interactions. The developed simulation tools will be immediately deployed for XFEL facility users, both experimentalists and theorists, via freely distributed codes and databases. Altogether, this project will facilitate establishing XFEL-based multidimensional spectroscopies as a novel diagnostic tool for monitoring electronic and structural dynamics in molecular materials.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

Tailoring MoS 2 for Small-Molecule Electroreduction: The Role of Metal Doping and Heterostructures

The electrification of chemical transformations central to sustainable fuel production and waste valorization, such as overall water splitting (OWS), hydrogen evolution reaction (HER), and electrochemical reduction of CO 2 (CO 2 R), presents a powerful opportunity to advance carbon-neutral energy technologies. Transition metal dichalcogenides (TMDs), particularly MoS 2 , have emerged as promising electrocatalyst candidates, owing to their abundance, tunable active sites, and defect-rich structures. This review highlights recent progress in leveraging metal doping and heterostructure engineering of MoS 2 to enhance the electrocatalytic activity and selectivity. By compiling insights from experimental studies and density functional theory (DFT) predictions, we examine how defect creation, electronic structure modification, and interface design contribute to improved charge transport and catalytic efficiency. Particular emphasis is placed on rational design principles, synthetic strategies, and operando characterization methods that provide a pathway to understanding and optimizing MoS 2 -based materials. We also discuss the challenges of stability, mechanistic ambiguity, and scaling while outlining opportunities to bridge theory and experiment. Collectively, this review underscores how defect and heterostructure engineering of MoS 2 can accelerate the development of efficient, sustainable electrocatalysts for both fuel generation and waste-to-value generation.

CO2 reduction↗
DOE OSTI2025

GPU acceleration of hybrid functional calculations in the SPARC electronic structure code

We present a Graphics Processing Unit (GPU)-accelerated version of the real-space SPARC electronic structure code for performing hybrid functional calculations in generalized Kohn–Sham density functional theory. In particular, we develop a batch variant of the recently formulated Kronecker product-based linear solver for the simultaneous solution of multiple linear systems. We then develop a modular, math kernel based implementation for hybrid functionals on NVIDIA architectures, where computationally intensive operations are offloaded to the GPUs, while the remaining workload is handled by the central processing units (CPUs). Considering bulk and slab examples, we demonstrate that GPUs enable up to 8× speedup in node-hours and 80× in core-hours compared to CPU-only execution, reducing the time to solution on V100 GPUs to around 300 s for a metallic system with over 6000 electrons, and significantly reducing the computational resources required for a given wall time.

Kohn-Sham density functional theory↗
DOE OSTI2025

Green’s Function Perspective on the Nonlinear Density Response of Quantum Many-Body Systems

We derive equations of motion for higher order density response functions using the theory of thermodynamic Green’s functions. We also derive expressions for the higher order generalized dielectric functions and polarization functions. Moreover, we relate higher order response functions and higher order collision integrals within the Martin–Schwinger hierarchy. We expect our results to be highly relevant to the study of a variety of quantum many-body systems such as matter under extreme temperatures, densities, and pressures.

Density Functional Theory↗
DOE OSTI2020

Scalable implementation of polynomial filtering for density functional theory calculation in PARSEC

In this work, we present an efficient implementation of polynomial filtering methods in PARSEC, a real-space pseudopotential based Kohn–Sham density functional theory solver. The implementation described here improves upon a Chebyshev-filtered subspace iteration algorithm used in the previous version of PARSEC. We present a hybrid polynomial filtering scheme that combines Chebyshev-filtered subspace iteration and a spectrum slicing method that partitions the spectrum into several spectral slices and uses bandpass-filtered subspace iteration to compute approximate eigenpairs within each interior slice simultaneously. We describe a procedure to partition a spectrum and construct polynomial filters. We also discuss a number of practical issues such as the use of appropriate data layouts for carrying out the computation on a two-dimensional process grid and how to achieve good load balance by allocating an appropriate number of process groups to each spectral slice. Numerical examples are presented to demonstrate the effectiveness of the hybrid polynomial filtering method as well as the superior parallel scalability of spectrum slicing in comparison to that of Chebyshev-filtered subspace iteration.

97 MATHEMATICS AND COMPUTING↗