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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Neural network emulation of spontaneous fission

Large-scale computations of fission properties are an important ingredient for nuclear reaction network calculations simulating rapid neutron-capture process (the 𝑟 process) nucleosynthesis. Due to the large number of fissioning nuclei potentially contributing to the 𝑟 process, a microscopic description of fission based on nuclear density functional theory (DFT) is computationally challenging. Here, we explore the use of neural networks (NNs) to construct DFT emulators capable of predicting potential energy surfaces and collective inertia tensors across the whole nuclear chart, starting from a minimal set of DFT calculations. We use constrained Hartree-Fock-Bogoliubov (HFB) calculations to predict the potential energy and collective inertia tensor in the axial quadrupole and octupole collective coordinates, for a set of nuclei in the 𝑟-process region. We then employ NNs to emulate the HFB energy and collective inertia tensor across the considered region of the nuclear chart. Least-action pathways characterizing spontaneous fission half-lives and fragment yields are then obtained by means of the nudged elastic band method. The potential energy predicted by NNs agrees with the DFT value to within a root-mean-square error of 500 keV, and the collective inertia components agree to within an order of magnitude. These results are largely independent of the NN architecture. The exit points on the outer turning line are found to be well emulated. For the spontaneous fission half-lives the NN emulation provides values that are found to agree with the DFT predictions within a factor of 10 3 across more than 70 orders of magnitude. Neural networks are able to emulate the potential energy and collective inertia well enough to reasonably predict physical observables. Future directions of study, such as the inclusion of additional collective degrees of freedom and active learning, will improve the predictive power of microscopic theory and further enable large-scale fission studies.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Bayesian mixture model approach to quantifying the empirical nuclear saturation point

The equation of state (EOS) in the limit of infinite symmetric nuclear matter exhibits an equilibrium density, $n_0 \approx 0.16 \, \mathrm{fm}^{-3}$, at which the pressure vanishes and the energy per particle attains its minimum, $E_0 \approx -16 \, \mathrm{MeV}$. Although not directly measurable, the nuclear saturation point $(n_0,E_0)$ can be extrapolated by density functional theory (DFT), providing tight constraints for microscopic interactions derived from chiral effective field theory (EFT). However, when considering several DFT predictions for $(n_0,E_0)$ from Skyrme and Relativistic Mean Field (RMF) models together, a discrepancy between these model classes emerges at high confidence levels that each model prediction's uncertainty cannot explain. How can we leverage these DFT constraints to rigorously benchmark nuclear saturation properties of chiral interactions? To address this question, we present a Bayesian mixture model that combines multiple DFT predictions for $(n_0,E_0)$ using an efficient conjugate prior approach. The inferred posterior distribution for the saturation point's mean and covariance matrix follows a Normal-inverse-Wishart class, resulting in posterior predictives in the form of correlated, bivariate $t$-distributions. The DFT uncertainty reports are then used to mix these posteriors using an ordinary Monte Carlo approach. At the 95\% credibility level, we estimate $n_0 \approx 0.157 \pm 0.010 \, \mathrm{fm}^{-3}$ and $E_0 \approx -15.97 \pm 0.40 \, \mathrm{MeV}$ for the marginal (univariate) $t$-distributions. Combined with chiral EFT calculations of the pure neutron matter EOS, we obtain bivariate normal distributions for the nuclear symmetry energy and its slope parameter evaluated at $n_0$: $S_v \approx 32.0 \pm 1.1 \, \mathrm{MeV}$ and $L\approx 52.6\pm 8.1 \, \mathrm{MeV}$ (95\%), respectively. Furthermore, our Bayesian framework is publicly available, so practitioners can readily use and extend our results.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Fast and Universal Kohn-Sham Density Functional Theory Algorithm for Warm Dense Matter to Hot Dense Plasma

Understanding many processes, e.g., fusion experiments, planetary interiors, and dwarf stars, depends strongly on microscopic physics modeling of warm dense matter and hot dense plasma. This complex state of matter consists of a transient mixture of degenerate and nearly free electrons, molecules, and ions. This regime challenges both experiment and analytical modeling, necessitating predictive ab initio atomistic computation, typically based on quantum mechanical Kohn-Sham density functional theory (KS-DFT). However, cubic computational scaling with temperature and system size prohibits the use of DFT through much of the warm dense matter regime. A recently developed stochastic approach to KS-DFT can be used at high temperatures, with the exact same accuracy as the deterministic approach, but the stochastic error can converge slowly and it remains expensive for intermediate temperatures (< 50 eV). Here we have developed a universal mixed stochastic-deterministic algorithm for DFT at any temperature. This approach leverages the physics of KS-DFT to seamlessly integrate the best aspects of these different approaches. We demonstrate that this method significantly accelerated self-consistent field calculations for temperatures from 3 to 50 eV, while producing stable molecular dynamics and accurate diffusion coefficients.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Effects of the surface termination and the oxygen vacancy position on LaNiO 3 ultra-thin films: First-principles study

In this study, while ultra-thin layers of the LaNiO 3 film exhibit a remarkable metal-insulator transition as the film thickness becomes smaller than a few unit cell (u.c.), the formation of possible oxygen vacancies and their effects on the correlated electronic structure have been rarely studied using first principles. Here, we investigate the effects of the surface termination and the oxygen vacancy position on the electronic properties and vacancy energetics of LaNiO 3 ultra-thin films under the compressive strain using density functional theory plus U (DFT + U). We find that oxygen vacancies can be easily formed in the Ni layers with the NiO 2 terminated surface (0.5 u.c. and 1.5 u.c. thickness) compared to the structures with the LaO terminated surface and the in-plane vacancy is energetically favored than the out-of-plane vacancy. When two vacancy sites are allowed, the Ni square plane geometry is energetically more stable in most cases as two oxygen vacancies tend to stay near a Ni ion. Strong anisotropy between the in-plane and out-of-plane vacancy formation as well as the layer and orbital dependent electronic structure occur due to strain, surface termination, charge reconstruction, and quantum confinement effects. The in-plane vacancy of the NiO 2 terminated structure is favored since the released charge due to the oxygen vacancy can be easily accommodated in the d x 2 -y 2 orbital, which is less occupied than the d z 2 orbital. Remarkably, the oxygen vacancy structure containing the Ni square-plane geometry becomes an insulating state in DFT + U with a sizable band gap of 1.2 eV because the large crystal field splitting between d z 2 and d x 2 -y 2 orbitals in the square-plane favors an insulating state and the Mott insulating state is induced in other Ni sites due to strong electronic correlations. In the thin-film structure without oxygen vacancies, the strong correlation effect in DFT + U drives a pseudogap ground state at the Fermi energy, similarly as the experimental photo-emission spectra; however, the variation of the DFT + U electronic structure depending on the surface termination becomes weaker compared to those obtained in DFT.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Rise and fall of Mott insulating gaps in YNiO 3 paramagnets as a reflection of symmetry breaking and remaking

The YNiO 3 nickelate is a paradigm d-electron oxide that manifests the intriguing temperature-mediated sequence of three phases transitions from (i) magnetically ordered insulator to (ii) paramagnetic (PM) insulator and then to (iii) PM metal. Such phenomena raised the question of the nature of the association of magnetism and structural symmetry breaking with the appearance in (i) and (ii) and disappearance in (iii) of insulating band gaps. It is demonstrated here that first-principles mean-field–like density-functional theory (DFT), driven by molecular dynamics temperature evolution, can describe not only the origin of the magnetically long-range ordered insulating phase (i), but also the creation of an insulating paramagnet (ii) that lacks spin- long-range order, and of a metallic paramagnet (iii) as temperature rises. This approach provides the patterns of structural and magnetic symmetry breaking at different temperatures, in parallel with band gaps obtained when the evolving geometries are used as input to DFT electronic band-structure calculations. This disentangles the complex interplay among spin, charge, and orbital degrees of freedom. Analysis shows that the success in describing the rise and fall of the insulating band gaps along the phase transition sequence is enabled by allowing sufficient flexibility in describing diverse local structural and magnetic motifs as input to DFT. This entails the use of sufficiently large supercells that allow expressing structural disproportionation of octahedra, as well as a description of PM phases as a distribution of local magnetic moments (rather than using a single averaged moment). It appears that the historic dismissal of mean-field–like DFT as being unable to describe such Mott-like transitions was premature, as it was based on consideration of averaged crystallographic unit cells, a description that washes out local symmetry-breaking motifs. The magnetically ordered insulating YNiO 3 phase (i) and the PM insulating phase (ii) result in DFT from allowing symmetry breaking, evident already by considering the athermal internal energy. In contrast, the PM metallic phase (iii) is formed thermally by smearing out thus weakening symmetry breaking. Analysis of snapshots of the different forms of structural vs magnetic symmetry breaking shows that only the loss of the polymorphous distribution of magnetic moments existing in (ii) causes the fall of the band gap, resulting in the metallic state in (iii). Finally, the interesting conclusion is that such a description of the rise [in phases (i) and (ii)] and fall [in phase (iii)] of the insulating gap does not rely on the traditional Mott-like strong correlation understanding, but on breaking and remaking of magnetic and structural symmetries reflected in energy lowering.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Dendrite Growth Morphology Modeling in Liquid and Solid Electrolytes

The main goal of this project is to develop a multi-scale modeling approach that connects micron-scale phase-field models and atomic-scale density functional theory (DFT)-based simulations via parameter- and relationship-passing in order to predict Li-metal dendrite morphology evolution, in both liquid and solid electrolytes. The key hypothesis of the DFT-informed phase-field multiscale modeling approach is that it can capture the electrochemical-mechanical driving forces and incorporate the roles of nano-meter-thin solid electrolyte interphase (SEI) in liquid electrolytes as well as of the microstructures of micro-meter-thick solid electrolytes (SEs) for all-solid-state batteries. In this project, we have formulated and implemented phase-field models to incorporate the electrochemical driving forces in liquid electrolytes and then incorporate mechanical driving forces to simulate dendrite growth in solid electrolytes with resolved microstructures. We have implemented two treatments for the SEI: an explicit model to include the microstructure of the SE or SEI in the phase field model and an implicit model to simulate the impact of nano-meter thick SEI in liquid electrolytes by varying the electrode/electrolyte interfacial properties. The key interfacial properties, including the electronic and ionic transport properties, the charge transfer reaction kinetics, and mechanical properties, were computed by DFT-based calculations. At the DFT-based model, one key advancement is to directly predict the charge transfer reaction kinetics at a complex Li/SEI/electrolyte interface by linking DFT with density functional tight binding (DFTB) calculations. As the main accomplishments, we have demonstrated two successful predictions in both solid electrolyte and liquid electrolyte based on this multiscale approach. The predicted intergranular Li dendrite growth in LLZO revealed the importance of trapped electrons at internal interfaces in the microstructure of LLZO. The predicted electroplating morphology of mossy Li and faceted Mg agreed well with experiments. The insights provided by the multiscale model and the model enabled electrolyte and SEI design will accelerate the development of Li-metal electrode for high energy density batteries, that meet DOE’s target on cell density (>350 Wh/kg) and cost below $100/kWhuse for EV applications.

25 ENERGY STORAGE↗
DOE OSTI2020

The Novel Hybrid Ab Initio Model of High-Performance Structural Alloys Design for Fossil Energy Power Plants

The current project developed a novel HT-CALPHAD/DFT approach, which can quickly design new high-performance structural alloys for the application of FE power plants. The PI will mainly take charge of high-throughput DFT simulations and computational thermodynamics of the selected multicomponent alloy systems for the FE power plant applications. At the end of the project, a novel hybrid model based on high throughput CALPHAD/DFT simulations and computational thermodynamics will be developed to provide guidance on how to identify multi-component new high-performance structural alloys with much less computational effort needed. It will address the extensive computation time needed for DFT on the new alloys design. In addition, it will also address the well-known headache of DFT, i.e. how to make the accurate prediction of the high-temperature equilibria. The novel hybrid model the PI proposed will not only be applied to the design of high-performance structural alloys in FE power plants but in many different applications, such as nuclear reactors. This hybrid modeling approach includes four sections: 1) HT-CALPHAD modeling of Al-Co-Cr-Ni-Fe system with FCC and BCC phase. In this section, 3561 non-equiatomic compositions were randomly generated in order to investigate the phase stability of single FCC and BCC phases. Meanwhile, we proposed a data screening procedure to screen out the good candidates within these compositions, considering the temperature range, average density, and melting temperature, etc. 2) Investigation of FCC-Cr lattice stability in Fe-Cr system. We systematically assessed the reliability of FCC-Cr lattice stability derived by DFT and CALPHAD approaches. Meanwhile, the Fe-Cr binary system was chosen as a case study to verify the Cr lattice stability obtained by both approaches. 3) High-throughput DFT modeling on elastic properties of Al-Co-Cr-Ni-Fe systems. We predicted and established the FCC quinary elastic constant database of the Al-Co-Cr-Fe-Ni systems at 0K by using special quasi-random structure (SQS) approach. The predictions will start with pure elements of Al-Co-Cr-Fe-Ni system and will be continued with binaries, ternaries, quaternaries, and finally the quinary compositions. 3) Modeling of temperature-dependent elastic properties in Al-Co-Cr-Ni-Fe systems. In this part, we predict the thermal expansion coefficient and elastic stiffness coefficient as a function of temperature by applying quasiharmonic approximation. With this approach, the elastic properties of HEAs at elevate temperature can be estimated.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2020

Using density functional theory to construct multiphase equations of state: tin as an example

We present a general methodology for using density functional theory (DFT) calculations as a basis for constructing multiphase equations of state (EOS). Focusing on tin as an example, we discuss the full process of generating a tabular EOS, starting from first-principles calculations and arriving at a full EOS in agreement with existing experimental data. We begin by describing DFT calculations for the five solid phases and liquid phase of tin, including cold curve, phonon, and DFT-based molecular dynamics calculations. We then discuss methods for incorporating DFT results into materials models used in OpenSesame, the program used to create a full tabular multiphase EOS. Next, we outline a general strategy for adjusting model parameters in OpenSesame to fit to a variety of experimental data, including isobaric data, isothermal data, shock data, solid-solid phase boundary measurements, and measurements of the melt curve. We end with a discussion of the advantages of using DFT data as a foundation for EOS generation and discuss potential strategies for automated EOS generation based on the fitting process highlighted in this work.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2020

Computational study of TATB

We have performed a computational study of TATB. The study is composed of two parts, one where we perform static T = 0 K calculations with several different DFT functionals, to investigate structural properties and obtain a cold curve. Even though the functionals used in this study give poor results at ambient pressure, they perform much better at compression, when the dominance of the van der Waals’ forces in the binding is replaced by more normal interactions. In particular AM05 gives good results, mainly because it doesn’t include any “faulty” van der Waals’. Indeed the van der Waals’ corrected functional (PBE+D2) results shows the worst performance on structural properties. For the cold curve the PBE+D2 functional gives the best results compared to experiments (room temperature) but since the structural properties were bad with this functional we cannot for sure say that this is in any way a validation. It is shown that scaling the AM05 results with a common factor reproduces the experimental data. We leave the question on why to a follow up study but speculate that maybe the experiment and our calculations do not represent the same system set-up. From the structural properties we identify a site in the TATB lattice that contains a high concentration of oxygen and hydrogen. The consequences of this finding, if any, are left to a future project. In the second part of the study we perform extensive high-quality DFT-MD (molecular dynamics with DFT forces) calculations to obtain points on the Hugoniot. This part of the study did not get finished within this project and a more extensive summary of this part will be given in a follow up report at a future date. However, we show that obtaining calculated data is essential for equations of state development since the existing experimental Hugoniot data barely deviates from room temperature isotherm data and thus give no information of the high temperature dependency of materials properties. Our conclusion so far in the project is that being able to calculate materials properties is essential for high explosives since experiments are unable to give information in the high temperature part of phase space. While blind use of DFT cannot (yet) give unambiguous answers, we can still explore qualitatively and use such information to guide us in EOS development. However, the main conclusion is that we need better functionals in DFT to easily extract needed properties from calculations.

36 MATERIALS SCIENCE↗
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 OSTI2024

Theoretical Prediction of Thermal Expansion Anisotropy for Y 2 Si 2 O 7 Environmental Barrier Coatings Using a Deep Neural Network Potential and Comparison to Experiment

Environmental barrier coatings (EBCs) are an enabling technology for silicon carbide (SiC)-based ceramic matrix composites (CMCs) in extreme environments such as gas turbine engines. However, the development of new coating systems is hindered by the large design space and difficulty in predicting the properties for these materials. Density Functional Theory (DFT) has successfully been used to model and predict some thermodynamic and thermo-mechanical properties of high-temperature ceramics for EBCs, although these calculations are challenging due to their high computational costs. In this work, we use machine learning to train a deep neural network potential (DNP) for Y 2 Si 2 O 7 , which is then applied to calculate the thermodynamic and thermo-mechanical properties at near-DFT accuracy much faster and using less computational resources than DFT. We use this DNP to predict the phonon-based thermodynamic properties of Y 2 Si 2 O 7 with good agreement to DFT and experiments. We also utilize the DNP to calculate the anisotropic, lattice direction-dependent coefficients of thermal expansion (CTEs) for Y 2 Si 2 O 7 . Molecular dynamics trajectories using the DNP correctly demonstrate the accurate prediction of the anisotropy of the CTE in good agreement with the diffraction experiments. In the future, this DNP could be applied to accelerate additional property calculations for Y 2 Si 2 O 7 compared to DFT or experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Data-Driven and Multiscale Modeling of DNA-Templated Dye Aggregates

Dye aggregates are of interest for excitonic applications, including biomedical imaging, organic photovoltaics, and quantum information systems. Dyes with large transition dipole moments (μ) are necessary to optimize coupling within dye aggregates. Extinction coefficients (ε) can be used to determine the μ of dyes, and so dyes with a large ε (>150,000 M –1 cm –1 ) should be engineered or identified. However, dye properties leading to a large ε are not fully understood, and low-throughput methods of dye screening, such as experimental measurements or density functional theory (DFT) calculations, can be time-consuming. In order to screen large datasets of molecules for desirable properties (i.e., large ε and μ), a computational workflow was established using machine learning (ML), DFT, time-dependent (TD-) DFT, and molecular dynamics (MD). ML models were developed through training and validation on a dataset of 8802 dyes using structural features. A Classifier was developed with an accuracy of 97% and a Regressor was constructed with an R2 of above 0.9, comparing between experiment and ML prediction. Using the Regressor, the ε values of over 18,000 dyes were predicted. The top 100 dyes were further screened using DFT and TD-DFT to identify 15 dyes with a μ relative to a reference dye, pentamethine indocyanine dye Cy5. Two benchmark MD simulations were performed on Cy5 and Cy5.5 dimers, and it was found that MD could accurately capture experimental results. The results of this study exhibit that our computational workflow for identifying dyes with a large μ for excitonic applications is effective and can be used as a tool to develop new dyes for excitonic applications.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
NASA NTRS1997

Application of Density Functional Theory to Systems Containing Metal Atoms

The accuracy of density functional theory (DFT) for problems involving metal atoms is considered. The DFT results are compared with experiment as well as results obtained using the coupled cluster approach. The comparisons include geometries, frequencies, and bond energies. The systems considered include MO2, M(OH)+(sub n), MNO+, and MCO+(sub 2). The DFT works well for frequencies and geometries, even in cases with symmetry breaking; however, some examples have been found where the symmetry breaking is quite severe and the DFT methods do not work well. The calculation of bond energies is more difficult and examples of the successes as well as failures of DFT will be given.

Bauschlicher, Charles W., Jr.↗
NASA NTRS2006

Application of Density Functional Theory to Systems Containing Metal Atoms

The accuracy of density functional theory (DFT) for problems involving metal atoms is considered. The DFT results are compared with experiment as well as results obtained using the coupled cluster approach. The comparisons include geometries, frequencies, and bond energies. The systems considered include MO2, M(OH)+n, MNO+, and MCO+2. The DFT works well for frequencies and geometries, even in case with symmetry breaking; however, some examples have been found where the symmetry breaking is quite severe and the DFT methods do not work well. The calculation of bond energies is more difficult and examples of successes as well as failures of DFT will be given.

Bauschlicher, Charles W., Jr.↗
NASA NTRS2024

Theoretical Prediction of Thermal Expansion Anisotropy for Y 2 Si 2 O 7 Environmental Barrier Coatings Using a Deep Neural Network Potential and Comparison to Experiment

Environmental barrier coatings (EBCs) are an enabling technology for silicon carbide (SiC)-based ceramic matrix composites (CMCs) in extreme environments such as gas turbine engines. However, development of new coating systems is hindered by the large design space and difficulty in predicting properties for these materials. Density Functional Theory (DFT) has successfully been used to model and predict some thermodynamic and thermo-mechanical properties of high-temperature ceramics for EBCs, although these calculations are challenging due to their high computational costs. In this work, we use machine learning to train a deep neural network potential (DNP) for Y 2 Si 2 O 7 , which is then applied to calculate thermodynamic and thermo-mechanical properties at near-DFT accuracy much faster and using less computational resources than DFT. We use this DNP to predict phonon-based thermodynamic properties of Y 2 Si 2 O 7 with good agreement to DFT and experiments. We also utilize the DNP to calculate the anisotropic, lattice direction-dependent coefficients of thermal expansion (CTEs) for Y 2 Si 2 O 7 . Molecular dynamics trajectories using the DNP correctly demonstrate accurate prediction of the anisotropy of the CTE in good agreement with diffraction experiments. In the future, this DNP could be applied to accelerate additional property calculations for Y 2 Si 2 O 7 compared to DFT or experiments.

rare earth silicates↗
DOE OSTI2023

Single and dual metal atom catalysts for enhanced singlet oxygen generation and oxygen reduction reaction

We demonstrate rational design of graphene-supported single and dual metal atom catalysts (SACs and DACs) for photocatalytic applications, such as singlet oxygen ( 1 O 2 ) sensitization and H 2 O 2 production. Here we combine density functional theory (DFT) and time-dependent DFT (TD-DFT) calculations with experimental verifications. We found a synergistic effect between triplet sensitization and triplet–triplet (Dexter) energy transfer; both play a role in the photocatalytic activity through the volcano plot of 3d transition metal SACs. More specifically, FeN 4 -SAC exhibits a low ISC energy gap (ΔE ISC ) of 0.039 eV, compared with 0.108 eV for FeNiN8-DACs, both possessing a high Bader charge transfer of 0.366 e- and 0.405 e-, respectively. Guided by these computational results, we synthesized a series of SACs and a DAC and confirmed their structures with scanning transmission electron microscopy (STEM) along with the X-ray absorption near-edge structure (XANES) and extended X-ray absorption fine structure (EXAFS). We then confirm their band structures with low-energy inverse photoemission spectroscopy (LEIPS) and UV-vis-NIR. Subsequently, we synthesized the catalysts for the photooxygenation of anthracene and two-electron oxygen reduction reaction (ORR) to measure their photocatalytic activity. We found that H 2 O 2 production through the two-electron ORR competes with the 1 O 2 generation through Dexter energy transfer. FeN 4 -SAC demonstrates a high photooxygenation conversion of 86% and a high 1 O 2 quantum yield of 1.04, obtained from electron spin resonance (ESR) spectroscopy, with low H 2 O 2 production. In contrast, NiN 4 -SAC exhibits a low 1 O 2 generation and a high H 2 O 2 production mainly because of the high Gibbs free energy of the OOH* intermediate. In conclusion, this work proposes an effective DFT-guided strategy for designing SACs and DACs for various photocatalytic applications.

36 MATERIALS SCIENCE↗
DOE OSTI2022

CALPHAD modeling of uranium nitride (UN) fabrication routes enabled by first-principles calculations

The thermochemical details of fabricating uranium nitride (UN) by ammonolysis of uranium tetraflouride (UF 4 ) were determined using density functional theory (DFT) and CALculation of PHAse Diagrams (CALPHAD) computational methods. The thermochemical data of all binary, ternary, and quaternary U-H-N-F phases were computed using DFT, and the data for the phases that have not been measured experimentally, including UN 2 and NH 4 F(g), were combined with existing experimentally-determined data for CALPHAD modeling. The DFT data were benchmarked using experimental Gibbs energy of reaction and experimental thermochemical data for individual species. Phase diagrams relevant to the ammonolysis reaction are depicted, showing regions of stability for solid U-N, U-F and U-N-F phases. An unidentified phase produced in a previous experiment was identified as UN 0.95 F 1.2 (UNF) by comparing its X-ray diffraction spectrum to the experimental spectrum, and its formation during the fabrication of UN from UF 4 is supported by the simulated phase diagram. Here, it is calculated that UN 2 can be produced by the ammonolysis of UF 4 , but requires elevated temperatures, high NH 3 (g) partial pressure, and large amounts of flowing NH 3 (g) to avoid solid flu oride impurities in the uranium nitride. Likewise, U 2 N 3 can be produced instead at temperatures greater than 980 K. The use of silane (SiH 4 ) gas was investigated as a potential additive in the ammonolysis fabrication route to speed removal of fluorine. The addition of SiH 4 (g) offers little advantage to the removal of fluorine, and adds the complication of Si 3 N 4 formation. The use of DFT to fill in missing data to perform CALPHAD calculations demonstrated here allows for the determination of more comprehensive and trustworthy phase diagrams than the use of existing experimental data alone.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Dependence of predicted bulk properties of hexagonal hydroxyapatite on exchange-correlation functional

Hydroxyapatite (HA) is the main component of human bones and teeth. HA has also been widely applied in various technological fields due to its unique properties. Reliable computational simulations for this critical material often require input parameters obtained from the first-principles density functional theory (DFT) calculations with appropriate exchange–correlation functionals. Previous DFT calculations are insufficient to verify the reliabilities of various functionals, particularly as they fail to assess predictions for multiple properties of the material. In this paper, we first select 18 different functionals to calculate geometric, elastic, electronic, and thermodynamic properties of hexagonal HA bulk crystal. We find that the results from optB86b-vdW and optB88-vdW functionals with dispersion corrections have the overall best agreement with available experimental data. Then, we choose optB88-vdW functional, as well as PBE functional without dispersion corrections as a comparison, to perform extensive first-principles DFT phonon calculations under the quasiharmonic approximation. Various thermodynamic properties (including phonon contributions to internal energy, entropy, and Helmholtz free energy) and thermal parameters (including thermal expansions of volume, thermal expansion coefficients, heat capacities, isothermal bulk moduli, etc.) versus temperature are consequently obtained. By comparing these quantities, we report that the results from optB88-vdW functional have significantly better agreement with available experimental data than those from PBE functional although the latter has been widely used in previous DFT calculations for HA-based material systems.

36 MATERIALS SCIENCE↗