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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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Electronic Structure of Above-Room-Temperature van der Waals Ferromagnet Fe 3 GaTe 2

Fe 3 GaTe 2 , a recently discovered van der Waals ferromagnet, demonstrates intrinsic ferromagnetism above room temperature, necessitating a comprehensive investigation of the microscopic origins of its high Curie temperature ($T$ C ). In this study, we reveal the electronic structure of Fe 3 GaTe 2 in its ferromagnetic ground state using angle-resolved photoemission spectroscopy and density functional theory calculations. Our results establish a consistent correspondence between the measured band structure and theoretical calculations, underscoring the significant contributions of the Heisenberg exchange interaction ($J$ ex ) and magnetic anisotropy energy to the development of the high-$T$ C ferromagnetic ordering in Fe 3 GaTe 2 . Intriguingly, we observe substantial modifications to these crucial driving factors through doping, which we attribute to alterations in multiple spin-splitting bands near the Fermi level. In conclusion, these findings provide valuable insights into the underlying electronic structure and its correlation with the emergence of high-$T$ C ferromagnetic ordering in Fe 3 GaTe 2 .

36 MATERIALS SCIENCE↗
DOE OSTI2021

On the liquid–liquid phase transition of dense hydrogen

Until recently the consensus theory/computation interpretation of the challenging liquid liquid phase transition (LLPT) of high-pressure hydrogen was first order. Cheng et al. developed a machine-learnt potential (MLP) that, in larger molecular dynamics (MD) simulations, gives a continuous transition instead. Here, we show that the MLP does not reproduce our still larger MD density-functional theory (MD-DFT) calculations as it should. Since the MLP is not a faithful surrogate for the MD-DFT, the Ref. 6 prediction of a supercritical atomic liquid is unfounded.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Investigating 3,4-bis(3-nitrofurazan-4-yl)furoxan detonation with a rapidly tuned density functional tight binding model

In this work, we describe a machine learning approach to rapidly tune density functional tight binding models for the description of detonation chemistry in organic molecular materials. Resulting models enable simulations on the several 10s of ps scales characteristic to these processes, with “quantum-accuracy.” We use this approach to investigate early shock chemistry in 3,4-bis(3-nitrofurazan-4-yl)furoxan, a hydrogen-free energetic material known to form onion-like nanocarbon particulates following detonation. We find that the ensuing chemistry is significantly characterized by the formation of large C x N y O z species, which are likely precursors to the experimentally observed carbon condensates. Beyond utility as a means of investigating detonation chemistry, the present approach can be used to generate quantum-based reference data for the development of full machine-learned interatomic potentials capable of simulation on even greater time and length scales, i.e., for applications where characteristic time scales exceed the reach of methods including Kohn–Sham density functional theory, which are commonly used for reference data generation.

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

Improving the precision of forces in real-space pseudopotential density functional theory

The high-order finite difference real-space pseudopotential density functional theory (DFT) approach is a valuable method for large-scale, massively parallel DFT calculations. A significant challenge in the approach is the oscillating “egg-box” error introduced by aliasing associated with a coarse grid spacing. To address this issue while minimizing computational cost, we developed a finite difference interpolation (FDI) scheme [Roller et al., J. Chem. Theory Comput. 19, 3889 (2023)] as a means of exploiting the high resolution of the pseudopotential to reduce egg-box effects systematically. Here, we show an implementation of this method in the PARSEC code and examine the practical utility of the combination of FDI with additional methods for improving force precision and/or reducing its computational cost, including orbital-based forces, compensating charges (namely, adding and subtracting a judiciously chosen charge density such that the total density is unaltered), and a modified spatial domain in which the real-space grid is defined. Using selected small molecules, as well as metallic Li, as test cases, we show that a combination of all four aspects leads to a significant reduction in computational cost while retaining a high level of precision that supports accurate structures and vibrational spectra, as well as stable and accurate molecular dynamics runs.

Chemistry↗
DOE OSTI2023

Predicting electronic structures at any length scale with machine learning

The properties of electrons in matter are of fundamental importance. They give rise to virtually all material properties and determine the physics at play in objects ranging from semiconductor devices to the interior of giant gas planets. Modeling and simulation of such diverse applications rely primarily on density functional theory (DFT), which has become the principal method for predicting the electronic structure of matter. While DFT calculations have proven to be very useful, their computational scaling limits them to small systems. We have developed a machine learning framework for predicting the electronic structure on any length scale. It shows up to three orders of magnitude speedup on systems where DFT is tractable and, more importantly, enables predictions on scales where DFT calculations are infeasible. Our work demonstrates how machine learning circumvents a long-standing computational bottleneck and advances materials science to frontiers intractable with any current solutions.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Dielectric screening and vacancy formation for large neutral and charged Si n H m ( n > 1500 ) nanocrystals using real-space pseudopotentials

A commonly used procedure for computing the properties of defects in crystalline materials is to consider a large supercell that includes the defect of interest. This is a straightforward technique as standard energy band codes can be used for such computations. For neutral defects, the only impediment of such an approach is to avoid defect-defect interactions between adjoining cells. However, this procedure can be complex if the defect of interest is charged as the system at large contains Coulombic divergences. Moreover, some have recently argued that the conventional definition of formation energies for charged defects cannot be reconciled with statistical mechanics. Here, we focus on an alternative approach. We consider large nanocrystals wherein a charged defect can be placed. Since the system is confined, a charged defect within the nanocrystal does not result in a Coulombic divergence. The chief impediment is computational, i.e., while no defect-defect or Coulombic divergences are present, the nanocrystal must be sufficiently large to allow the system to properly replicate a bulklike configuration. With the development of new algorithms and hardware advances, computations for systems of sufficient size to address this issue are feasible. In particular, we solve the Kohn-Sham equation in real space using pseudopotential-density-functional theory for large silicon nanocrystals, which contain thousands of atoms. Further, we focus on (i) the screening of a point charge and (ii) the formation of a charged vacancy in hydrogen-terminated silicon nanocrystals. This approach allows us to examine the role of quantum confinement in addition to exploring the bulk limit. Comparisons to other methods confirm the viability of this approach.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Machine Learned Force Field Modeling of Metal Organic Frameworks for CO2 Direct Air Capture

Metal organic frameworks (MOFs) are a large class of porous materials and have garnered significant interest due to their large surface areas and their tunable physical and chemical properties. Numerous prior studies have been performed to screen large databases of this material class for promising DAC sorbent materials. These studies have often relied on classical model potentials. While density functional theory (DFT) calculations have been shown to be very accurate for modeling the interaction of CO2 with MOFs, such calculations are too computationally demanding for statistically significant adsorption predictions. To overcome this barrier, we developed methods for training models to achieve DFT-level accuracy for the forces and energies associated with MOF flexibility and CO2 adsorption using machine learned force fields (MLFFs). These methods were parametrized based on DFT calculations of CO2 in a flexible MOF and used to predict MOF structural properties as well as CO2 adsorption in several MOFs.

Findley, John↗
DOE OSTI2022

Path Integral Monte Carlo Simulations of Iron Plasmas (Final Technical Report)

This documents is the final technical report for our grant entitled "Path Integral Monte Carlo Simulations of Iron Plasmas" that focused on developing path integral Monte Carlo (PIMC) computer simulations. This techniques will be developed to study plasmas composed of heavier elements including iron and other third row elements. Equations of state (EOS) and transport properties will be derived in the regime of warm dense matter (WDM) and dense plasmas where existing first-principles methods cannot be applied. While standard density functional theory (DFT) has been used to accurately predict the structure of many solids and liquids up to temperatures on the order of 100,000 K, this method is not applicable at much higher temperature because the number of partially occupied electronic orbitals reaches intractably large numbers or the use of finite-temperature free energy functionals in orbital-free DFT introduces an uncontrolled approximation. Here we focus on PIMC methods that become more and more efficient with increasing temperatures and still include all electronic correlation effects. In this approach, electronic excitations increase the efficiency rather than reduce it. While it had commonly been assumed this method could only be applied to elements without core electrons, we showed that PIMC with free-particle nodes works well for first-row elements (PRL 108 (2012) 115502). Most recently, we extended the applicability range of all-electron PIMC to second-row elements by adopting localized nodal surfaces (PRL 115 (2015) 176403). To simulate third-row elements efficiently under WDM conditions, we propose a new method to remove core electrons by introducing pseudo-nodes. We explain our approach step by step and present preliminary results. We focus our method development on getting PIMC simulations of iron to work because of its fundamental importance for WDM and astrophysics. Then we move on to krypton and copper-doped beryllium, a ICF ablator material. We plan to continue working on key second-row material such as Na, Mg, MgO, Al, silica, and silicon-doped plastic ablators. Our collaborators at LLNL, will use our PIMC EOS data both as comparisons to existing semi-empirical, EOS-generating schemes, and as input for continuum radiation hydrodynamics simulations. We will establish an efficient pipeline from PIMC to macroscopic continuum studies of materials response. An emphasis will be placed on benchmarking such methods for plasmas of heavy elements at the very high temperatures (~100 eV) and low densities that are generated when Hohlraum radiation heats the ablator material in indirect drive laser experiments. Results from changes to the EOS will be of immeasurable importance to the designers at the National Ignition Facility (NIF) and at other facilities. Starting with our EOS of Cu-doped Be, our second collaborator at LLE, will perform real-time simulations of laser fusion experiments at the Omega laser and at the NIF to determine how sensitive the compression path depends on the ablator EOS. Since our collaborator also has experience in performing orbital-free DFT calculations, we propose to compare predictions from this method with PIMC results. In joint publications, we plan to analyze the accuracy of different free-energy functionals in order to understand why existing orbital-free DFT calculations do not predict compression peaks along the shock Hugoniot curve that we see with PIMC. The peaks are caused by the ionization of various electron shells. Their accurate characterization is important to compare with experimental results. We will break new ground by developing PIMC techniques that can simulate iron and all other third row elements in the plasma and WDM regimes. We introduce the concept of pseudo-nodes for the efficient treatment of the core-electrons. The EOS and transport properties will be derived and published online in the form of a new WDM database. Our PIMC EOS calculations will benchmark and possibly replace semi-analytical EOS tables like QEOS or SESAME, which will impact the hydrocode simulation community and will affect the design of NIF targets. Our PIMC results will help to improve the accuracy of orbital-free DFT simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Atomate2: modular workflows for materials science

High-throughput density functional theory (DFT) calculations have become a vital element of computational materials science, enabling materials screening, property database generation, and training of “universal” machine learning models. While several software frameworks have emerged to support these computational efforts, new developments such as machine learned force fields have increased demands for more flexible and programmable workflow solutions. This manuscript introduces atomate2, a comprehensive evolution of our original atomate framework, designed to address existing limitations in computational materials research infrastructure. Key features include the support for multiple electronic structure packages and interoperability between them, along with generalizable workflows that can be written in an abstract form irrespective of the DFT package or machine learning force field used within them. Our hope is that atomate2's improved usability and extensibility can reduce technical barriers for high-throughput research workflows and facilitate the rapid adoption of emerging methods in computational material science.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Composition-property correlations in NiTi-based shape memory alloys from the first principles

Development of shape memory alloys (SMA) has been traditionally accomplished by means of extensive empirical efforts, with a very limited support from phenomenological models and related tools. We present a theoretical framework based on the Hamiltonian formalism, which connects important thermodynamic properties directly to the relative energies (the eigenvalues of the Hamiltonian), computed from the first principles using density functional theory. The estimates based on this formalism allow to establish correlations between composition and physical properties, such as relative energies, phase transition temperatures, hysteresis, thermal expansion, and other thermodynamic information. Importantly, estimates and correlations not only provide a fast answer, but also elucidate underpinning of thermodynamic features in terms of the electronic structure. Using this framework, we consider phase transformations and correlations between the selected properties and composition in the NiTi-based ternary and multicomponent shape memory alloys. Our theoretical guidance facilitates design and development of future alloys.

shape memory alloys↗
DOE OSTI2021

What's Left for a Computational Chemist To Do in the Age of Machine Learning?

Machine learning (ML) has become a central focus of the computational chemistry community. In this paper, I will first discuss my personal history in the field. Then I will provide a broader view of how this resurgence in ML interest echoes and advances upon earlier efforts. Although numerous changes have brought about this latest wave, one of the most significant is the increased accuracy and efficiency of low-cost methods (e.g., density functional theory or DFT) that have made it possible to generate large data sets for ML models. ML has also been used to bypass, guide, or improve DFT. The field of computational chemistry thus finds itself at a crossroads as ML both augments and supersedes traditional efforts. I will present what I believe the role of the computational chemist will be in this evolving landscape, with specific focus on my experience in the development of autonomous workflows in computational materials discovery for open-shell transition-metal chemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Computational synthesis of 2D materials: A high-throughput approach to materials design

2D materials find promising applications in next-generation devices, however, large-scale, low-defect, and reproducible synthesis of 2D materials remains a challenging task. Here, to assist in the selection of suitable substrates for the synthesis of as-yet hypothetical 2D materials, we have developed an open-source high-throughput workflow package, Hetero2d, that searches for low-lattice mismatched substrate surfaces for any 2D material and determines the stability of these 2D-substrate heterostructures using density functional theory (DFT) simulations. Hetero2d automates the generation of 2D-substrate heterostructures, the creation of DFT input files, the submission and monitoring of computational jobs on supercomputing facilities, and the storage of relevant parameters alongside the post-processed results in a MongoDB database. We demonstrate the capability of Hetero2d in identifying stable 2D-substrate heterostructures for four 2D materials, namely 2H-MoS 2 , 1T- and 2H-NbO 2 , and hexagonal-ZnTe, considering 50 cubic elemental substrates. We find Cu, Hf, Mn, Nd, Ni, Pd, Re, Rh, Sc, Ta, Ti, V, W, Y, and Zr substrates sufficiently stabilize the formation energies of these 2D materials, with binding energies in the range of ~0.1–0.6 eV/atom. Upon examining the z-separation, the charge transfer, and the electronic density of states at the 2D-substrate interface, we find a covalent type bonding at the interface which suggests that these substrates can be used as contact materials for the 2D materials. Hetero2d is available on GitHub as an open-source package under the GNU license.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Atomic Energy Accuracy of Neural Network Potentials: Harnessing Pretraining and Transfer Learning

Machine learning-based interatomic potentials (MLIPs) have transformed the prediction of potential energy surfaces (PESs), achieving accuracy comparable to ab initio calculations. However, atomic energy predictions, often assumed to lack physical meaning, remain underexplored. In this study, we demonstrate that inaccuracies in atomic energy predictions reduce the robustness and transferability of Neural Network Potentials (NNPs) and atomic energy error can be masked in total energy predictions due to error cancellation. Here, we validate this finding using challenging configurations involving deformation and failure under tensile loading. By pretraining atomic energy predictions using empirical potentials and applying transfer learning with density functional theory (DFT) data, we achieve notable improvements in the accuracy of total energy, forces, and stress predictions. Furthermore, this approach enhances the robustness and transferability of NNPs, emphasizing the importance of atomic energy predictions in developing high-quality and reliable MLIPs.

Active Learning↗
DOE OSTI2025

In Silico Design of Methyl-Driven Overhauser Dynamic Nuclear Polarization Agents

Overhauser effect (OE) dynamic nuclear polarization (DNP) has drawn attention owing to its enhanced performance at ultrahigh magnetic fields. The lack of design principles has, nevertheless, limited the development of OE polarizing agents when compared to those used for cross-effect DNP. Here, we measured the 19 F OE DNP performance of a series of CF 3 -functionalized Blatter-type radicals. Using density functional theory calculations, we accurately predict the methyl-driven OE performance, paving the way for computer-aided design of optimized OE DNP polarizing agents.

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

A reactive molecular dynamics model for uranium/hydrogen containing systems

Uranium-based materials are valuable assets in the energy, medical, and military industries. However, understanding their sensitivity to hydrogen embrittlement is particularly challenging due to the toxicity of uranium and the computationally expensive nature of quantum-based methods generally required to study such processes. In this regard, we have developed a Chebyshev Interaction Model for Efficient Simulation (ChIMES) that can be employed to compute energies and forces of U and UH3 bulk structures with vacancies and hydrogen interstitials with accuracy similar to that of Density Functional Theory (DFT) while yielding linear scaling and orders of magnitude improvement in computational efficiency. Here, we show that the bulk structural parameters, uranium and hydrogen vacancy formation energies, and diffusion barriers predicted by the ChIMES potential are in strong agreement with the reference DFT data. We then use ChIMES to conduct molecular dynamics simulations of the temperature-dependent diffusion of a hydrogen interstitial and determine the corresponding diffusion activation energy. Our model has particular significance in studies of actinides and other high-Z materials, where there is a strong need for computationally efficient methods to bridge length and time scales between experiments and quantum theory.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Single-Walled Carbon Nanotubes with Confined Chalcogens as the Catalysts and Electrodes for Oxygen Reduction Reaction in Fuel Cells

The goal of this project is to synthesize and characterize a new non-metal electro-catalyst for oxygen reduction reaction (ORR) for fuel cell applications. The intended catalyst is a composite material composed of sulfur chains encapsulated in narrow diameter single-walled carbon nanotubes (S@SWNTs). S@SWNTs were successfully synthesized through sulfur vapor infusion method, and validated with Raman spectroscopy. However, the electrochemical analysis on the ORR catalytic activity of S@SWNTs indicated that it had low ORR catalytic activity. Our theoretical study based on density functional theory (DFT) revealed that the poor oxygen adsorption (low binding energy) on the surface of S@SWNTs was the bottleneck of the entire catalytic reaction. The focus of the project was subsequently pivoted to the development of a new non-noble metal ORR catalyst that could provide durability in acidic electrolyte. The hypothesis was to encapsulate small iron (Fe) clusters in SWNTs (Fe@SWNTs) can provide ORR electro-catalytic activity and long durability in acidic environment. DFT-based computational studies were carried out to explore the feasibility of the Fe@SWNTs catalyst. The theoretical study indicated that Fe@SWNTs indeed could catalyze the ORR with lower theoretical overpotential than platinum (Pt). However, its weaker bonding energy with oxygen was the bottleneck of the overall reaction. On the other hand, Fe clusters (composed of 7 Fe atoms) encapsulated in nitrogen-doped SWNTs (Fe 7 @N 4 WSNTs) showed proper oxygen adsorption by the Fe cluster and low theoretical overpotential of ORR. Comparing to the Fe single atom catalyst on N-doped SWNTs (Fe-N 4 SWNTs), which is one the best non-noble metal ORR catalysts reported in the literature, the Fe 7 @N 4 WSNTs showed lower overpotential and better resistance to acidic environment. Fe encapsulated N-doped SWNTs were synthesized with ferrocene as the Fe precursors through vapor infusion method, and experimental validate is underway. This study theoretically demonstrated the feasibility of a new type of non-noble metal electro-catalyst for ORR that could have high catalytic activity and long durability.

08 HYDROGEN↗
NASA NTRS2023

An Ensemble Neural Network Model for Predicting Rare-Earth Oxide and Silicate Heat Capacities at High Temperature

In this work, a neural network model was developed to predict the constant pressure heat capacity for materials in the rare-earth oxide—silica material space. Several model architectures were trained and tested on heat capacity data generated from first-principles density functional theory calculations. Hyperparameter optimization was performed, and the optimal model was selected for heat capacity predictions. The optimal model architecture was found to have a root-mean-squared error of 5.12 ± 3.37 J/mol-K. The optimal model architecture was then used in a bagging ensemble model trained using the leave-one-group-out method to provide error estimates for model predictions. The out-of-bag score for the ensemble model was 0.997. The predicted heat capacities agree well with the DFT and experimental results and were computed orders of magnitude faster than DFT simulations. Machine learning shows the potential to provide a suitable surrogate model for thermochemical property predictions for candidate environmental barrier coating materials but refining of input material features and model architectures could further improve accuracy for these models.

environmental barrier coatings↗
DOE OSTI2024

Time-dependent density functional theory with the orthogonal projector augmented wave method

The projector augmented wave (PAW) method of Blöchl linearly maps smooth pseudo wavefunctions to the highly oscillatory all-electron DFT orbitals. Compared to norm-conserving pseudopotentials (NCPP), PAW has the advantage of lower kinetic energy cutoffs and larger grid spacing at the cost of having to solve for non-orthogonal wavefunctions. We earlier developed orthogonal PAW (OPAW) to allow the use of PAW when orthogonal wavefunctions are required. In OPAW, the pseudo wavefunctions are transformed through the efficient application of powers of the PAW overlap operator with essentially no extra cost compared to NCPP methods. Previously, we applied OPAW to DFT. Here, we take the first step to make OPAW viable for post-DFT methods by implementing it in real-time time-dependent (TD) DFT. Using fourth-order Runge–Kutta for the time-propagation, we compare calculations of absorption spectra for various organic and biological molecules and show that very large grid spacings are sufficient, 0.6–0.7 bohr in OPAW-TDDFT rather than the 0.4–0.5 bohr used in traditional NCPP-TDDFT calculations. This reduces the memory and propagation costs by around a factor of 3. Our method would be directly applicable to any post-DFT methods that require time-dependent propagations such as the GW approximation and the Bethe–Salpeter equation.

Chemistry↗