Implemented advance Surface-Hopping functional in time dependent Density Functional Theory (TDDFT) simulator package for modeling of nonlinear X-ray spectroscopy in complex molecular materials
Abstract not provided.
74 ATOMIC AND MOLECULAR PHYSICS↗
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Tuning the Optical and Photophysical Properties of Boron Aza-Dipyridylmethene dyes for Optoelectronics Application
A series of substituted boron aza-dipyridylmethene (aD) compounds are explored for their attractive photophysical and electrochemical properties as fluorescent emitters for blue OLEDs. Previous work in our lab have demonstrated the synthetic ease of dipyridylmethene monomers and their interesting photophysical properties as homoleptic meso-linked dimers. Using Density Functional Theory (DFT) modeling, we combine our theoretical and experimental efforts to synthesize a library of aD compounds by replacing the meso carbon with nitrogen to destabilize the HOMO energy. The synthesized aD derivatives emit blue light with λ max between 400-460 nm and internal quantum efficiencies above 85 % . From TD-DFT calculations, estimation of the singlet and triplet energies that are deduced from fluorescence and phosphorescence emission, respectively, closely matches the experimental data of ΔE ST ≤ 0.4 eV. Aza-dipyridylmethene show promising potential as blue emitting fluorophores in OLEDs with EQE max = 4.5 %, approaching the maximum theoretical limit of fluorescent (fl) OLED of 5.5 %. Boron aza-dipyridylmethene are synthetically tunable compounds which show interesting photophysical properties for applications in organic electronics.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
High Performance Circuit Pastes for Solid Oxide Fuel Cell Applications: Final Technical Report
Using a combination of in-plane electrical conductivity measurements, electrical contact resistance measurements, tensile fracture tests, double shear lap fracture tests, rapid thermal cycling adhesion tests, in situ wetting angle measurements, 3D X-ray tomography, reduction-oxidation (RedOx) cycling, scanning electron microscopy (SEM), energy dispersive x-ray spectroscopy (EDAX), Density Functional Theory (DFT) modeling, Molecular Dynamics (MD) modeling, and Phase Field modeling, this project determined that a new brazing technique developed by the PIs (specifically the use of nickel particles to direct the wetting and spreading of molten silver) could be used to produce well-adhered, >97% dense, electrically-conductive circuits, current collectors, and/or electrical contacts on a variety of ceramic and ceramic-coated substrates (specifically lanthanum strontium manganite, yttria stabilized zirconia, sapphire, Cr2O3-passivated stainless steel, and Al2O3-passivated stainless steel). Compared to other silver-based alternatives, Ag:Ni led to less manufacturing defects and was more tolerant of the extreme environments (isothermal high-temperature aging) and occasional abuse (rapid thermal cycling and RedOx cycling) encountered during Solid Oxide Fuel Cell and/or Solid Oxide Electrolysis Cell operation. Further, in some situations (such as when to produce Ag:Ni electrical contacts to Al2O3-protected stainless steel) the Ni particles also improved operation by acting as chemical getters that removed electrically-insulating, surface-segregating substrate impurities (i.e. by lowering the contact resistance with the underlying substrate).
36 MATERIALS SCIENCE↗
Modeling Ni Coarsening and Migration in the Hydrogen Electrode of Solid oxide Cells under Operating Conditions
Ni coarsening and migration is the most important degradation in the hydrogen electrode of solid oxide cells. This presentation reviews our works on Ni coarsening and migration in NETL over the past years. We used simulation techniques including phase-field modeling and density function theory to investigate possible mechanisms of Ni coarsening and migration, including self-diffusion of Ni, diffusion of gaseous Ni(OH)2, surface diffusion of Ni(OH)x and Ni-YSZ wettability change, and examined the effect of operating conditions on these mechanisms. So far, none of the mechanisms can fully explain the experiments. Remaining questions and possible paths forward are summarized and discussed.
Lei, Yinkai↗
Fluorescent Detection of Carbon Disulfide by a Highly Emissive and Robust Isoreticular Series of Zr-Based Luminescent Metal Organic Frameworks (LMOFs)
Carbon disulfide (CS 2 ) is a highly volatile neurotoxic species. It is known to cause atherosclerosis and coronary artery disease and contributes significantly to sulfur-based pollutants. Therefore, effective detection and capture of carbon disulfide represents an important aspect of research efforts for the protection of human and environmental health. In this study, we report the synthesis and characterization of two strongly luminescent and robust isoreticular metal organic frameworks (MOFs) Zr 6 (µ 3- O) 4 (OH) 8 (tcbpe) 2 (H 2 O) 4 (here termed 1) and Zr 6 (µ 3- O) 4 (OH) 8 (tcbpe-f) 2 (H 2 O) 4 (here termed 2) and their use as fluorescent sensors for the detection of carbon disulfide. Both MOFs demonstrate a calorimetric bathochromic shift in the optical bandgap and strong luminescence quenching upon exposure to carbon disulfide. The interactions between carbon disulfide and the frameworks are analyzed by in-situ infrared spectroscopy and computational modelling by density functional theory. These results reveal that both the Zr metal node and organic ligand act as the preferential binding sites and interact strongly with carbon disulfide.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Local Coordination Environment of 3d and 4d Transition Metal Ions in LiCl-KCl Eutectic Mixture
In this study, the structure and coordination environment of two 3d transition elements (Ni and Cr) is investigated in a molten chloride salt system. Electronic absorption spectroscopy was employed to elucidate their coordination environment in 3LiCl-2KCl eutectic salt, as a function of temperature. Density functional theory (DFT) modeling was used to determine the coordination environment of the transition metal species in the eutectic composition as well as the optical spectra computationally. The Ni 2+ and Cr 3+ exist in a tetrahedral and octahedral coordination environment, respectively, in eutectic salt. The spectra thus obtained were compared with the experimental data; a reasonable qualitative agreement was obtained between experimental and computational Ni 2+ and Cr 3+ spectra, and the coordination of both elements in the eutectic composition were in excellent agreement with the experimentally determined results. Computational results were also obtained for two 4d elements, Mo 3+ and Nb 3+ , with both quantum molecular dynamics (QMD) and hybrid functional optical spectra indicating octahedral coordination.
36 MATERIALS SCIENCE↗
Theoretical Modeling of Formic Acid (HCOOH), Formate (HCOO(-)), and Ammonia (NH(4)) Vibrational Spectra in Astrophysical Ices
Ions embedded in icy grain mantles are thought to account for various observed infrared spectroscopic features, particularly in certain young stellar objects. The dissociation of formic acid (HCOOH) in astrophysical ices to form the formate ion (HCOO(-)) was modeled with density functional theory cluster calculations. Like isocyanic acid (HOCN), HCOOH was found to spontaneously deprotonate when sufficient water is present to stabilize charge transfer complexes. Both ammonia and water can serve as proton acceptors, yielding ammonium (NH4(+)) and hydronium (H3O(+)) counterions. Computed frequencies of weak infrared features produced by stretching and bending modes in both HCOO(-) and HCOOH were compared with experimental and astronomical data. Our results confirm laboratory assignments that a band at 1381 cm(exp -1) can be attributed to the CH bend in either HCOO(-) or HCOOH, but a band at 1349 cm(exp -1) corresponds to CO stretching in HCOO(-). Another feature at 1710 cm(exp -1) (5.85 m) can possibly be assigned to a CO stretching mode in HCOOH, as suggested by experiment, but the agreement is less satisfactory. In addition, we examine and analyze spectroscopic features associated with NH+4, both as a counterion to HCOO(-) or OCN(-) and in isolation, in order to compare with experimental and astronomical data in the 7 m region.
Park, Jin-Young↗
Accessible Open-Source Software Framework for Multi-Scale Modeling
Explore the source record for details and available documents.
36 MATERIALS SCIENCE↗
Development of an ab initio learned model of electron deposition range in deuterium-tritium plasmas through time-dependent density functional theory calculations and machine learning
Accurate hydrodynamic modeling for laser-direct-drive (LDD) inertial-confinement-fusion (ICF) relies on precise calculations of the electron thermal conduction in all target materials. The nonlocal stopping range of electrons in ICF plasmas directly influences thermal conduction; yet, no first principles model exists for the electron mean free path in the conduction-zone regime. This work utilized time-dependent stochastic density-functional theory (TD-sDFT) to calculate the electron stopping power in deuterium-tritium (DT) plasmas at (ρ, T) conditions relevant to the conduction zone and the compressed shell in ICF. Using a combination of our TD-sDFT data and already established analytical models, we developed and trained an artificial neural network to create a global model for the nonlocal electron deposition range, λ E . We compared our machine-learning (ML) based model for λ E to the currently-used modified-Lee-More model in LDD radiation-hydrodynamic codes, such as lilac, and saw an overall decrease in the deposition range. To understand the effects of λ E on LDD ICF implosion dynamics, we implemented the ML-based model into lilac; specifically, we looked at designs consistent with a current experiment on the OMEGA laser and for a newly designed LDD-ICF target for the future OMEGA-Next facility. In both cases, we saw an overall drop in predicted ablation pressure, peak areal density, and neutron yield due to the reduced thermal conduction (smaller λ E ) in DT plasmas. Comparisons with the experiment on OMEGA are also made.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Toward first principles-based simulations of dense hydrogen
Accurate knowledge of the properties of hydrogen at high compression is crucial for astrophysics (e.g., planetary and stellar interiors, brown dwarfs, atmosphere of compact stars) and laboratory experiments, including inertial confinement fusion. There exists experimental data for the equation of state, conductivity, and Thomson scattering spectra. However, the analysis of the measurements at extreme pressures and temperatures typically involves additional model assumptions, which makes it difficult to assess the accuracy of the experimental data rigorously. On the other hand, theory and modeling have produced extensive collections of data. They originate from a very large variety of models and simulations including path integral Monte Carlo (PIMC) simulations, density functional theory (DFT), chemical models, machine-learned models, and combinations thereof. At the same time, each of these methods has fundamental limitations (fermion sign problem in PIMC, approximate exchange–correlation functionals of DFT, inconsistent interaction energy contributions in chemical models, etc.), so for some parameter ranges accurate predictions are difficult. Recently, a number of breakthroughs in first principles PIMC as well as in DFT simulations were achieved which are discussed in this review. Here we use these results to benchmark different simulation methods. We present an update of the hydrogen phase diagram at high pressures, the expected phase transitions, and thermodynamic properties including the equation of state and momentum distribution. Furthermore, we discuss available dynamic results for warm dense hydrogen, including the conductivity, dynamic structure factor, plasmon dispersion, imaginary-time structure, and density response functions. We conclude by outlining strategies to combine different simulations to achieve accurate theoretical predictions that are based on first principles.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Machine Learned Hückel Theory: Interfacing Physics and Deep Neural Networks
The Hückel Hamiltonian is an incredibly simple tight-binding model known for its ability to capture qualitative physics phenomena arising from electron interactions in molecules and materials. Part of its simplicity arises from using only two types of empirically fit physics-motivated parameters: the first describes the orbital energies on each atom and the second describes electronic interactions and bonding between atoms. By replacing these empirical parameters with machine-learned dynamic values, we vastly increase the accuracy of the extended Hückel model. The dynamic values are generated with a deep neural network, which is trained to reproduce orbital energies and densities derived from density functional theory. The resulting model retains interpretability, while the deep neural network parameterization is smooth and accurate and reproduces insightful features of the original empirical parameterization. Altogether, this work shows the promise of utilizing machine learning to formulate simple, accurate, and dynamically parameterized physics models.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Combining Computational Modeling with Reaction Kinetics Experiments for Elucidating the In Situ Nature of the Active Site in Catalysis
Microkinetic modeling based on density functional theory (DFT) derived energetics is important for addressing fundamental questions in catalysis. The quantitative fidelity of microkinetic models (MKMs), however, is often insufficient to conclusively infer the mechanistic details of a specific catalytic system. Here, this can be attributed to a number of factors such as an incorrect model of the active site for which DFT calculations are performed, deficiencies in the hypothesized reaction mechanism, inadequate consideration of the surface environment under reaction conditions, and intrinsic errors in the DFT exchange-correlation functional. Despite these limitations, we aim at developing a rigorous understanding of the reaction mechanism and of the nature of the active site for heterogeneous catalytic chemistries under reaction conditions. By achieving parity between experimental and modeling outcomes through robust parameter estimation and by ensuring coverage-consistency between DFT calculations and MKM predictions, it is possible to systematically refine the mechanistic model and, thereby, our understanding of the catalytic active site in situ.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Computational Investigation of the Role of Active Site Heterogeneity for aSupported Organovanadium(III) Hydrogenation Catalyst
A crucial consideration for supported heterogeneous catalysts is the nonuniformity of the active sites, particularly for supported organometallic catalysts. Standard spectroscopic techniques, such as X-ray absorption spectroscopy, reflect the nature of the most populated sites, which are often intrinsically structurally distinct from the most active catalytic sites. Additionally, with computational models, often, only a few representative structures are used to depict catalytic active sites on a surface, even though there are numerous observable factors of surface heterogeneity that contribute to the kinetically favorable active species. A previously reported study on the mechanism of a surface organovanadium-(III) catalyst [(SiO 2 )V III (Mes)(THF)] for styrene hydrogenation yielded two possible mechanisms: heterolytic cleavage and redox cycling. These two mechanistic scenarios are challenging to differentiate experimentally since the kinetic readouts of the catalyst are identical. To showcase the importance of modeling surface heterogeneity and its effect on catalytic activity, density functional theory (DFT) computational models of a series of potential active sites of [(SiO 2 )V III (Mes)(THF)] for the reaction pathways are applied in combination with kinetic Monte Carlo (kMC) simulations. Computed results were then compared to the previously reported experimental kinetic study: (1) DFT free-energy reaction pathways indicated the likely active site and pathway for styrene hydrogenation, a heterolytic cleavage pathway requiring a bare tripodal vanadium site. (2) From the kMC simulations, a mixture of different bond lengths from the support oxygen to the metal center was required to qualitatively describe the experimentally observed kinetic aspects of a supported organovanadium(III) catalyst for olefin hydrogenation. This work underscores the importance of modeling surface heterogeneity in computational catalysis.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Using projection operators with maximum overlap methods to simplify challenging self-consistent field optimization
Maximum overlap methods are effective tools for optimizing challenging ground- and excited-state wave functions using self-consistent field models such as Hartree-Fock and Kohn-Sham density functional theory. Nevertheless, such models have shown significant sensitivity to the user-defined initial guess of the target wave function. Here in this work, a projection operator framework is defined and used to provide a metric for non-aufbau orbital selection in maximum-overlap-methods. The resulting algorithms, termed the Projection-based Maximum Overlap Method (PMOM) and Projection-based Initial Maximum Overlap Method (PIMOM), are shown to perform exceptionally well when using simple user-defined target solutions based on occupied/virtual molecular orbital permutations. This work also presents a new metric that provides a simple and conceptually convenient measure of agreement between the desired target and the current or final SCF results during a calculation employing a maximum-overlap method.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Modified Energy Span Analysis Reveals Heterogeneous Catalytic Kinetics
Mechanistic modeling is a cornerstone of catalyst development generally conducted with microkinetic models or density functional theory-based energy profiles. We extend the energy span model of homogeneous catalysis to heterogeneous systems by introducing the modified energy span analysis (MESA) model by implementing collision theory and gas-phase concentration effects. We determine analytically turnover frequencies, coverages, rate-determining steps, apparent activation energies, and reaction orders in agreement with microkinetic and kinetic Monte Carlo simulations. The model applies to discrete catalyst state systems, including single-atom catalysts, homogeneous systems, and microporous materials. A generalizable rate expression is derived for reactor modeling or mechanistic insights from solely experimentally measured reaction orders. Here, we illustrate MESA on three published mechanisms and reveal unexpected phenomena absent in traditional energy span profiles.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Accelerated Cu2O Reduction by Single Pt Atoms at the Metal-Oxide Interface§
The reducibility of metal oxides, when serving as the catalyst support or the active sites themselves, plays an important role in heterogeneous catalytic reactions. Here we present an integrated experimental and theoretical study that reveals how the addition of small amounts of atomically dispersed Pt at the metal/oxide interface dramatically enhances the reducibility of a Cu2O thin film by H2. X-ray photoelectron spectroscopy (XPS) and temperature-programmed desorption (TPD) results reveal that upon oxidation, a PtCu single-atom alloy (SAA) surface is covered by a thin Cu2O film and is therefore unable to dissociate H2. Despite this, in situ studies using ambient pressure (AP) XPS reveal that the presence of a small amount of Pt under the oxide layer can, at the single-atom limit, promote the reduction of Cu2O by H2 at room temperature. We build two density functional theory based surface models to better understand these experimental findings: A Cu2O/Cu(111)-like surface oxide layer, known as the ‘29’ oxide, in which Pt is alloyed into the Cu(111) surface as well as a PtCu SAA. Our calculations suggest that the increased activity is due to the presence of atomically dispersed Pt under the surface oxide layer, which weakens the Cu-O bonds in its immediate vicinity, thus making the interface between subsurface Pt and the surface oxide a nucleation site for the formation of metallic Cu. This initial step in the reduction process results in the presence of surface Pt atoms surrounded by metallic Cu patches, and the Pt atoms become active in H2 dissociation, which consequently accelerates the reduction of the oxide layer. This work demonstrates how isolated Pt atoms at the metal/oxide interface of a Cu-based catalyst accelerate the reduction of the oxide and, therefore, helps maintain the active, reduced state of the catalyst under reaction conditions, as is required for the synthesis of methanol from CO2 hydrogenation.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
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↗
El Agente: An autonomous agent for quantum chemistry
Computational chemistry tools are widely used to study the behavior of chemical phenomena. Yet, the complexity of these tools can make them inaccessible to non-specialists and challenging even for experts. In this work, we introduce El Agente Q, an LLM-based multi-agent system that dynamically generates and executes quantum chemistry workflows from natural language user prompts. The system is built on a novel cognitive architecture featuring a hierarchical memory framework that enables flexible task decomposition, adaptive tool selection, post-analysis, and autonomous file handling and submission. El Agente Q is benchmarked on six university-level course exercises and two case studies, demonstrating robust problem-solving performance (averaging >87% task success) and adaptive error handling through in situ debugging. It also supports longer-term, multi-step task execution for more complex workflows, while maintaining transparency through detailed action trace logs. Together, these capabilities lay the foundation for increasingly autonomous and accessible quantum chemistry.
agentic systems↗