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DOE OSTI2024

Fundamental Studies of the Vibrational, Electronic, and Photophysical Properties of Tetrapyrrolic Architectures

The ability to capture and utilize light in the near-ultraviolet (NUV), visible and near-infrared (NIR-I and NIR-II) spectral regions (i.e., 320–400, 400–700, 700–1000, 1000–1700 nm) is essential for any solar-energy conversion scheme. Nature employs chlorophylls and bacteriochlorophylls in light-harvesting architectures to absorb light in the blue and red/NIR regions. Accessory pigments (carotenoids, bilins) augment absorption of the (bacterio)chlorophylls in the green region. The harvested energy is funneled to a reaction center protein, where charge separation occurs. Subsequent migration of the electron and the hole stabilizes and stores the energy from light via redox chemistry. The long-term objective of the Bocian/Holten&Kirmaier/Lindsey research program under this DOE grant has been to develop tetrapyrrole-based molecular architectures that absorb sunlight, funnel energy and separate charge with high efficiency. Integral to the program has been iterative cycles of design, synthesis and characterization that provided deep insights into the relationships between chemical composition, electronic structure, and key static and dynamic properties (vibrational, redox, photophysical, energy/charge transfer) of tetrapyrrolic systems. Such architectures included monomers, dyads, larger arrays, and complexes with accessory components. The objective was to develop molecular designs and guiding principles to enhance current and future energy-conversion schemes. Molecular arrays targeted to address one or more fundamental questions concerning light harvesting and energy/charge transfer were constructed from analogues of the naturally occurring hemes, chlorophylls and bacteriochlorophylls. Diverse, tunable synthetic building blocks were prepared that spanned the three respective tetrapyrrole families, which are the porphyrins, chlorins and bacteriochlorins. Thus, the research focused on porphyrins as well as synthetic surrogates for chlorophylls (chlorins, 13 1 -oxophorbines and chlorin-imides) and bacteriochlorophylls (bacteriochlorins, bacterio-13 1 -oxophorbines and bacteriochlorin-imides), generically termed hydroporphyrins. Although the three tetrapyrrole classes (porphyrins, chlorins and bacteriochlorins) absorb light strongly in the violet-blue spectral region, the long-wavelength absorption band typically lies in the green-orange, red, and NIR regions, respectively, with increasing intensity. Understanding the spectra, electronic structure, and energy/charge-transfer properties of such tetrapyrrolic macrocycles is of central importance for the rational design of molecular architectures for solar-energy conversion. Our integrated program of molecular design and synthesis coupled with a variety of spectroscopic, electrochemical, and computational studies have probed from first principles how structural and electronic properties of tetrapyrrolic macrocycles dictate spectral properties as well as the rates of ground-state hole/electron transfer and excited-state energy flow in multicomponent architectures. Individual molecules and multicomponent architectures were designed to test ideas of fundamental importance, often requiring the development of new synthetic methodology. The members of the collaborative team had almost daily discussions by phone and/or e-mail concerning design of molecules, flow of compounds between the labs, planning of physical characterization studies, discussing results and analysis and integrating into design of next generation architectures, and the preparation of manuscripts. Furthermore, students and postdocs in the different labs routinely communicated with one another to facilitate the advancement of the research activities. In short, a highly integrated and collaborative research program was well established among the groups. The research effort involved molecular design and synthesis of synthetic molecular architectures by the Lindsey group integrated with physicochemical and photophysical characterization by the Bocian group and the Holten&Kirmaier group (Figure 2). The Bocian group carried out electrochemical, electron paramagnetic resonance (EPR), resonance Raman (RR), and Fourier-transform infrared (FT-IR) studies, as well as density functional theory (DFT) calculations and the time-dependent extension (TDDFT) to gain insight into excited-state properties. The Holten&Kirmaier group carried out static and time-resolved absorption and fluorescence spectroscopy studies and simulated absorption spectra using molecular orbital (MO) energies from DFT as input to the four-orbital model to complement the TDDFT calculations. The combined measurements provided understanding of the vibrational/electronic properties of the individual molecules and the changes that occur upon incorporation into multicomponent architectures. This information underpinned elucidating the mechanisms and timescales of ground-state hole/electron transfer and excited-state energy and charge transfer.

14 SOLAR ENERGY↗

DOE OSTI2025

Computational Determination of the Refractive Index of TATB, LLM-105, β-HMX, & α-RDX

In this study, we use Materials Studio CASTEP, an ab initio quantum mechanical (QM) program employing density functional theory (DFT), to compute the refractive index (RI) of solid energetic materials, specifically, PETN (tetragonal phase), TATB, LLM-105, β-HMX, and α-RDX. The use of quantum mechanical DFT methods affords the investigation of the optical mate rial properties of a system without the need for any experimental input. The RI values of PETN and TATB have been experimentally determined previously and are compared here to the QM derived RI values as validation of the theoretical approach in determining material optical proper ties. To date, experimental measurements to determine the RI values for LLM-105 have yet to be determined. This fact obviates the need to determine LLM-105 RI values using quantum mechanical means.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Computational methods based on density functional theory for reactions and processes involving electronic spin (Final Technical Report)

This award supports one post-doctoral researcher for 1.5 years. Publications that acknowledge this grant: Refs. 1–14. Refs. 3,10,12 assess current methodology for the evaluation of magnetic exchange couplings in transition metal complexes. In particular, Ref. 10 validates the use of an approximate (non-iterative) Green’s function approach for the calculation of magnetic exchange couplings and will be the foundation for Thrust 2 in this proposal. Refs. 3 and 12 focus on widely used density functional approaches based on the standard energy differences methodology for the particular case of oxo-bridged Fe(III) complexes. Refs. 2,4–7,11 apply current methodologies to problems of practical interest in molecular magnetism. Ref. 13 presents a methodology to explicitly simulate the dynamics of open quantum systems within density functional theory (DFT) calculations based on the Liouville-von Neumann equation of motion for quantum systems driven out-of-equilibrium. Ref. 8 uses non-collinear spin DFT to explain the mechanical behavior of magnetic mono-atomic Pt wires produced in break-junction experiments in the presence of a magnetic field.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Accelerated Design of Cost-Effective Thermal/Environmental Barrier Coatings based on High-Entropy Rare Earth Disilicates: A First-Principles Study

This project aims to design cost-effective thermal/environmental barrier coatings (TEBC) based on high entropy rare earth disilicates to protect SiC-based ceramic matrix composites from chemical and thermal attack for better performance of components in the hot section of gas turbine engines. To accelerate the alloy design, we utilize first-principles density functional theory (DFT) together with combinatorial chemistry methodology to predict key properties including phase stability, apparent bulk coefficient of thermal expansion (ABCTE), intrinsic lattice thermal conductivity, and temperature-dependent elastic constants. Specifically, this project focuses on β-RE2Si2O7 (RE=Yb, Y, Er, Lu, La, Ce,) with β-Yb2Si2O7 and β-Y2Si2O7 as the benchmark. Our DFT calculations predict that Er1/4Lu1/4Y3/4Yb3/4Si2O7 and Er1/2Lu1/2Y1/2Yb1/2Si2O7 have ultralow lattice thermal conductivity < 0.23 W/m/K at 1500 K and a good match of average ABCTE (5.1 - 5.2×10-6 K-1) with SiC. Owing to the low cost and abundant supply of Ce and La, the A- and G-La2Si2O7/Ce2Si2O7 disilicates are also studied. Our study shows that G-phase Ce2Si2O7 has an ultralow thermal conductivity (0.26 W/m/K at 1500 K) and the apparent bulk ABCTE (≈6.9×10-6 K-1) slightly higher than SiC, demonstrating great potential as low-cost high-performance T/EBC. However, La2Si2O7 and Ce2Si2O7 undergo an A-phase to G-phase polymorphic transition at around 1470 K.

environmental barrier coatings↗

DOE OSTI2024

Rapid Design of Cost-Effective Refractory High Entropy Alloys Strengthened by Precipitation

Funded by the ARPA-E ULTIMATE program, this project aims to develop cost-effective refractory high entropy alloys (RHEAs) for gas turbine blades applications at 1300 degree Celsius and above. The alloy design focuses on carbides precipitation strengthening in a strong and ductile solid solution BCC matrix. The goal is to achieve balanced mechanical properties including room temperature ductility & fracture toughness and high temperature strength and creep resistance, while maintaining comparable oxidation resistance and densities to Ni-base superalloys. To accelerate alloy design, multiscale modeling including CALPHAD and first-principles density functional theory (DFT) calculations and machine learning are performed. Specifically, high throughput phase diagram calculations and screening are carried out using CALPHAD; DFT calculations are performed to predict intrinsic ductility, grain boundary strengthening potential, coefficient of thermal expansion (CTE) and temperature-dependent elastic constants. Temperature dependent yield stress, creep strain and CTE are also predicted using machine learning. Down-selected alloys are synthesized in small buttons of about 250g using arc melting for rapid evaluation on microstructure and mechanical properties before employing plasma arc melting and additive manufacturing for producing large ingots. Preliminary computational and experimental results of this project will be presented and future direction in RHEA development will be discussed.

high temperature materials↗

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↗

DOE OSTI2026

Interpretable Deep Learning for Advancing Field-Enhanced Catalysis

This DOE Early Career project developed a physics-informed, interpretable AI-and-modeling framework to understand and exploit electric-field effects in heterogeneous catalysis, with ammonia cracking and synthesis as a representative pathway. The team built and validated methods to map local electric fields on metal surfaces and nanoparticles, showing that low-coordination features (tips/edges/corners) can concentrate fields by several-fold relative to flat facets. Using DFT-generated datasets, the project created physics-guided machine learning models that rapidly predict local electric fields and field-dependent adsorption energetics with near-DFT accuracy while reducing computational cost by orders of magnitude. These predictions were integrated with microkinetic modeling to quantify how field-dipole interactions reshape reaction energetics and mechanisms, enabling large increases in predicted catalytic rates and substantial reductions in operating temperature under favorable field conditions. To accelerate discovery of earth-abundant catalysts, the project combined interpretable ML screening (with electronic-structure descriptors identified as key drivers) with a generative inverse-design workflow based on diffusion models and physics constraints. The resulting closed-loop approach, linking simulation, mechanistic modeling, and AI, provides reusable tools and datasets for designing catalysts and operating conditions in field-enhanced catalysis, with broad relevance to electrostatic catalysis, plasma catalysis, electrocatalysis, and other energy-related chemical transformations.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2026

Schiff Moment of 129 Xe and 199 Hg [Slides]

Using Chicoma in 2025, we advanced DFT-based calculations needed to connect fundamental T violating interactions to measurable Schiff moments in heavy nuclei. We implemented DFT for polarized odd nuclear systems, a first step toward treating the time-reversal-breaking mean fields required for Schiff moment calculations. As a validation test, we computed polarizabilities using a perturbation with the dipole operator (see figure to the right) for the odd systems shown in Table 1; while no odd-system effect is expected for polarizabilities, this capability is essential for Schiff moments. Initial mean-field solutions for 129 Xe Schiff moment calculations were also generated.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2026

Augmenting Molecular Graphs with Geometries via Machine Learning Interatomic Potentials

Accurate molecular property predictions require 3D geometries, which are typically obtained using expensive methods such as density functional theory (DFT). Here, we attempt to obtain molecular geometries by relying solely on machine learning interatomic potential (MLIP) models. To this end, we first curate a large-scale molecular relaxation dataset comprising 3.5 million molecules and 300 million snapshots. Then MLIP pre-trained models are trained with supervised learning to predict energy and forces given 3D molecular structures. Once trained, we show that the pre-trained models can be used in different ways to obtain geometries either explicitly or implicitly. First, it can be used to obtain approximate low-energy 3D geometries via geometry optimization. While these geometries do not consistently reach DFT-level chemical accuracy or convergence, they can still improve downstream performance compared to non-relaxed structures. To mitigate potential biases and enhance downstream predictions, we introduce geometry fine-tuning based on the relaxed 3D geometries. Second, the pre-trained models can be directly fine-tuned for property prediction when ground truth 3D geometries are available. Our results demonstrate that MLIP pre-trained models trained on relaxation data can learn transferable molecular representations to improve downstream molecular property prediction and can provide practically valuable but approximate molecular geometries that benefit property predictions. Our code is publicly available at: https://github.com/divelab/AIRS/.

Fu, Cong [Texas A & M Univ., College Station, TX (↗

DOE OSTI2021

Density Functional Theory Solute-Dislocation Interactions

DFT-optimized structures of dislocations with a solute at different sites. The dataset include the VASP input files (except POTCAR) and output files OSZICAR and OUTCAR. There are also text files containing the DFT-computed interaction energies. The README file contains expressions that can be used to compute the interaction energies and chemical misfit energies using the volumetric size misfit for any solute species in Mg.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Hydrated Metal and Metal-Nitrate Complexes in Water: Full Lanthanide(III) Series plus Miscellaneous Metal Ions

This is a dataset of hydrated metal complexes and metal–nitrate hydrated complexes intended for public use, reproducibility, and downstream structural analysis. A key feature is coverage across the full lanthanide(III) series (La–Lu), enabling systematic comparisons of coordination motifs and bonding trends across the entire lanthanide sequence. In addition to the lanthanides, the dataset also includes other metal ions such as UO2(VI), Fe(II), and Fe(III). The dataset provides optimized geometries for hydrated and nitrate-containing hydrated complexes, together with representative ab initio molecular dynamics (AIMD) trajectories saved in standard XYZ formats. The accompanying NWChem input decks enable reproduction of the reported calculations and provide a starting point for extending the simulations to related coordination environments. Computationally, DFT calculations employ the B3LYP functional with DFT-D3BJ dispersion corrections and a COSMO continuum solvent model (dielectric constant 78.4) to represent solvation beyond the explicitly treated first hydration shell. AIMD simulations are performed with the NWChem qmd module at 298 K, integrating nuclear motion with the velocity-Verlet algorithm and controlling temperature using a Nosé–Hoover thermostat. Trajectories are approximately 4.8 ps in length and are used primarily to assess short-time stability of candidate coordination motifs, including (for lanthanides) differences between 8- versus 9-water coordination and comparisons between nitrate-bound and nitrate-free hydrated complexes.

Dinpajooh, Mohammadhasan [Pacific Northwest Nation↗

DOE OSTI2021

Density Functional Theory Solute-Dislocation Interactions

Trinkle, Dallas↗

DOE OSTI2021

Density Functional Theory Solute-Dislocation Interactions

Trinkle, Dallas↗

DOE OSTI2021

Density Functional Theory Solute-Dislocation Interactions

Trinkle, Dallas↗

DOE OSTI2020

Crystal Structure Prediction of Binary Alloys via Deep Potential

Predicting crystal structure has been a challenging problem in physics and materials science for a long time. A reliable energy calculation engine combined with an efficient global search algorithm, such as particle swarm optimization algorithm or genetic algorithm, is needed to conduct crystal structure prediction. In recent years, machine learning-based interatomic potential energy surface models have been proposed, potentially allowing us to perform crystal structure prediction for systems with the accuracy of density functional theory (DFT) and the speed of empirical force fields. In this paper, we employ a previously developed Deep Potential model to predict the intermetallic compound of the aluminum–magnesium system, and find six meta-stable phases with negative or nearly zero formation energy. In particular, Mg 12 Al 8 shows excellent ductility and Mg 5 Al 27 has a high Young's modulus. Based on our benchmark results, we propose a relatively robust structure screening criterion that selects potentially stable structures from the Deep Potential-based convex hull and performs DFT refinement. By using this criterion, the computational cost needed to construct the convex hull with ab initio accuracy can be dramatically reduced.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Unexpected features in the optical vibrational spectra of δ-UO 3

Uranium trioxide displays a complex chemical phase space, with at least six structurally distinct polymorphs accessible via different synthetic routes. Remarkably, despite its technological importance, full structural and electronic characterization of these polymorphs remains an open area of study. δ-UO 3 in particular has attracted significant theoretical attention due to its high point group and space group symmetries, having U (VI) in octahedral coordination with polyhedra interconnected through corner-sharing to build a 3-D cubic lattice with space group symmetry Pm-3m and Z = 1. Critical experimental information, such as its optical vibrational spectra, are not known. Here, we study the Raman and infrared (IR) spectra of δ-UO 3 together with the support of density functional theory (DFT) calculations for spectral interpretation. A symmetry analysis of the DFT-predicted phonon eigenmodes indicates that δ-UO 3 should have two IR active modes and no Raman active modes. Experimental results, however, indicate significant Raman scattering from δ-UO 3 . We therefore propose four potential explanations for this apparent contradiction: a possible tetragonal distortion to the cubic cell, the existence of a surface impurity layer, vacancy scattering, and structural activation of Raman signal. We use powder X-ray diffraction and confocal Raman spectroscopy with depth profiling to investigate these possibilities and suggest future experiments to explore this phenomenon in more detail. Understanding the lattice dynamics of δ-UO 3 is important for identification of technogenic U phases via Raman and infrared spectroscopy and our results indicate that the simple understanding of δ-UO 3 as a high-symmetry cubic structure should be reconsidered.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2022

Thermodynamics Modeling for Actinide Monocarbides and Mononitrides from First Principles

The high-temperature thermodynamical properties for the actinide monocarbides and mononitrides ThC, ThN, UC, UN, PuC, and PuN are calculated from first-principles electronic-structure theory. The electronic structure is modeled with density-functional theory (DFT) and is fully relativistic, including the spin-orbit interaction. Furthermore, the DFT is extended to account for orbital–orbital interactions, by means of a parameter-free orbital-polarization (OP) technique, that has proven to be essential for the 5f electrons in plutonium. Strong anharmonicity and the temperature dependence of the lattice vibrations are captured with the self-consistent ab initio lattice dynamics (SCAILD) method. The calculated free energies and heat capacities are compared to published results from quasi-harmonic (QH) theory, and experiments, where available. For the uranium and plutonium compounds, we make use of CALPHAD assessments to help evaluate the theory. Generally, our anharmonic relativistic approach compares well with both CALPHAD and experiments. For the thorium compounds, our theory is in good accord with QH modeling of the free energy at lower temperatures but for the heat capacity the comparison is less favorable.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2026

An Ab Initio Molecular Dynamics Study of Key Thermodynamic Input Parameters for Computer Simulation of U-6Nb Solidification

The key to metallic fuel development is the fabrication of uranium metal and alloys into fuel forms. U-Nb alloys are one of the best candidates for a metallic fuel alloy with high-temperature strength sufficient to support the core, acceptable nuclear properties, good fabricability, and compatibility with usable coolant media. Melt processing has been a key component of the metallic fuel cycle, and process models require thermophysical parameters at elevated temperatures, particularly above the melting temperatures, regarding which experimental data are scarce, for accurate simulations and process development. By means of ab initio density-functional theory (DFT) quantum molecular dynamics (QMD), we have calculated the main thermophysical parameters—the density, thermal expansion coefficient, specific heat, thermal conductivity, melting temperature, latent heat of fusion, and viscosity—used in the modeling of the U-6 wt.% Nb alloy casting. The melting temperature of the U-6 wt.% Nb alloy at ambient pressure is obtained by means of QMD simulations using the Z-method. The ambient volume change and latent heat of melting of U-6 wt.% Nb are also derived from QMD simulations in conjunction with analytical fitting for the energy and pressure. The thermal conductivity for the solid U-Nb alloy is calculated from the semi-classical Boltzmann transport equation combined with an estimate of the electron relaxation time obtained from DFT simulations.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗