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DOE OSTI2023

Twisted Diindeno‐Fused Dibenzo[ a , h ]anthracene Derivatives and their Dianions

Abstract We report a facile synthesis of diindeno‐fused dibenzo[a,h]anthracene derivatives ( DIDBA‐2Cl , DIDBA‐2Ph , and DIDBA‐2H) with different degrees of non‐planarity using three substituents (chloro, phenyl, and hydrogen) of various sizes. The planarization of their cores, as evidenced by the decreased end‐to‐end torsional angles, was confirmed by X‐ray crystallography. Their enhanced energy gaps with twisting were investigated by a combination of spectroscopic and electrochemical methods with density functional theory, which showed a transition from singlet open‐shell to closed‐shell configuration. Moreover, their doubly reduced states, DIDBA‐2Ph 2− and DIDBA‐2H 2− , were achieved by chemical reduction. The structures of dianions were identified by X‐ray crystallographic analysis, which elucidated that the electron charging further distorted the backbones. The electronic structure of the dianions was demonstrated by experimental and theoretical approaches, suggesting decreased energy gaps with larger non‐planarity, different from the neutral species.

Gu, Yanwei↗

DOE OSTI2023

Twisted Diindeno‐Fused Dibenzo[ a , h ]anthracene Derivatives and their Dianions

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Structural, Electronic, and Photophysical Insights into a Few Atom Copper-Sulfur Cluster in the Solid and Solution States

Coinage-metal chalcogenide clusters are widely studied for their attractive photoluminescence properties. Copper chalcogenides are especially promising, but are often confined to solid-state investigations due to their limited solution stability and the difficulty of synthesizing stable, well-defined clusters. Here, we investigate copper–sulfur clusters incorporating a small number of Cu atoms to elucidate fundamental atomic interactions, ground- and excited-state characteristics, and photophysical behavior in both solid and solution. We have synthesized the Cu6(4,6-dimethyl-2-mercaptopyrimidine)6 cluster in both neutral and charged states, Cu6 and Cu6-2H2+, respectively, by selective ligand protonation. The molecular structures are determined using single-crystal X-ray diffraction, while Cu K-edge X-ray absorption spectroscopy is used to probe Cu electronic structure differences arising from the ligand modification. Steady-state and pump-probe optical spectroscopy is used to investigate photophysical properties, interpreted using density functional theory methods. Both clusters exhibit good stability in the solid state and in solution and show characteristic near-infrared emission with microsecond lifetimes. Overall, the Cu6S6 clusters display favorable charge–transfer characteristics and show potential for further use in driving photochemical transformations.

Copper-sulfur clusters↗

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

“Self-trapping” in solar cell hybrid inorganic-organic perovskite absorbers

In the simplest picture, a “self-trapped” polaron forms when an excess electron or hole deforms a crystal lattice, creating a potential well with bound states. Properties of self-trapped polarons in methylammonium lead iodide perovskite (MAPbI 3 ), which is widely used as solar cell absorber, are of great interest, and are a subject of ongoing investigations and debates concerning the existence of large polarons with the co-presence of metastable self-trapping. Herein, we employ a self-interaction-free density functional theory method to investigate the stability of small polarons in tetragonal MAPbI 3 phase. The electron small polaron is found to be unstable, while the hole small polaron is found to be metastable at realistic operation temperatures of solar cells. Further, the hole polaron is found to have a hole band close to the conduction band, which in conjunction with its metastability suggests that small polarons will have an appreciable effect on charge-carrier recombinations in MAPbI 3 . Further, in this study, we posit that the existence of the metastable polarons in addition to the large polarons may explain the experimentally observed non-monotonic temperature dependence of bimolecular charge-carrier recombination rate in tetragonal MAPbI 3 phase.

14 SOLAR ENERGY↗

DOE OSTI2025

Effects of iron carbide crystal phases and dopants on the conversions of CO 2 into ethylene

The density functional theory method was used to investigate the conversions of CO 2 to ethylene formation on two common iron carbide surfaces: Fe 3 C(0 1 0) and Fe 5 C 2 (1 1 1). Based on the structure relaxation of reaction intermediates and the elementary reaction transition states. We deduced the most competitive reaction pathways for ethylene production. The main CO 2 -to-ethylene routes and the competition of side products, CO and CH 4 , are discussed. Our analyses showed that CO 2 conversion is surface structure sensitive, whereas CH 4 and C2+ hydrocarbon formations depend on the reactivity of native C atoms in the carbides. To modify the intrinsic catalyst performance, mixing dopants in Fe catalysts is an effective strategy. Furthermore, we demonstrate that doping Zn and Zr can alter the local electronic structure and enhance CO 2 adsorption on the catalyst surface.

CO2 hydrogenation↗

DOE OSTI2021

Modeling of aqueous species interaction energies prior to nucleation in cement-based gel systems

Arguably the most ubiquitous construction material in modern civilization, concrete is enabling the development of megacities around the globe together with increasing living standards in developing nations. However, the exact formation mechanisms of the strength-giving calcium-rich gels remain a topic of debate. Using density functional modeling, we simulate the fundamental solution-based building blocks of cement hydrates (calcium ions and silicate and aluminate monomers) and their propensity to form pair-wise complexes with bonding environments characteristic of those found in calcium-silicate-hydrate, calcium-alumino-silicate-hydrate and sodium-containing calcium-alumino-silicate-hydrate gels, as assessed from Gibbs free energies of chemical reactions. By accurately simulating the high pH pore solution chemistry in Portland cements and related systems, along with discrete solvation of the species, we hypothesize potential early age formation routes of the gels and discuss limitations and future work associated with this approach.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Intrinsic reactivity and dissolution characteristics of tetracalcium aluminoferrite

Tetracalcium aluminoferrite is one of the main phases of Portland cement, the dissolution characteristics of which however have not been fully revealed. This work adopted first-principles calculations and Mössbauer spectroscopy as well as X-ray diffraction analysis to clarify the intrinsic reactivity of different species of tetracalcium aluminoferrite crystals. Ca ions are the most reactive species of tetracalcium aluminoferrite and Al ions are slightly more reactive than Fe ions. Moreover, the tetrahedral Al and Fe ions are more reactive than the octahedral Al and Fe ions. These indicate dissolution characteristics of tetracalcium aluminoferrite crystals, namely, Ca ions dissolve first and the tetrahedral species dissolve faster than the octahedral species. After the initial dissolution, the dissolved ions nucleate and grow on the defective surface of C{sub 4}AF and the C{sub 4}AF residues are fully hydroxylated. The further hydration thereafter becomes the complex dissolution with the nucleation and growth of hydration products.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Machine learning assisted modeling of mixing timescale for LES/PDF of high-Karlovitz turbulent premixed combustion

Accurate modeling of mixing in the transported probability density function (PDF) method remains a great challenge, especially for turbulent premixed combustion under extreme conditions such as high Karlovitz number Ka. Recently, a power-law based mixing timescale model was developed for the large-eddy simulations (LES)/PDF modeling of high-Ka number turbulent premixed flames. It is found in this work that the power-law mixing timescale model is highly sensitive to the model parameters. It is thus critically needed to develop accurate calibration of these model parameters. The empirical specification of the model parameters developed in Zhang et. al. is found to be inadequate for accurate modeling of the mixing timescale. Here, machine learning is introduced as an attractive alternative in this work for the specification of the model parameters. A high-Ka number DNS jet flame is used as the training and validation of the machine learning models. The choices of the input parameters are discussed and compared for the machine learning models. The effect of differential molecular diffusion on mixing is examined by including the effect of the Lewis number in the training of the machine learning models. The performance of different machine learning algorithms is compared for the specification of the mixing model parameters. Overall, excellent performance of the machine learning models is observed for assisting the mixing modeling. The feasibility, interpretability, applicability, generality, and portability of using machine learning are discussed in general to provide a perspective on applying data-driven machine learning for turbulent combustion modeling studies.

42 ENGINEERING↗

DOE OSTI2022

High temperature stability and transport characteristics of hydrogen in alumina via multiscale computation

Here, the impact of hydrogen charge states on the stability and transport characteristics of hydrogen interstitials in alumina polymorphs is evaluated by multiscale computational methods including density functional theory (DFT), ab initio molecular dynamics (AIMD) and machine learned force fields. Thermodynamic calculations show that the protonic H i +1 interstitial is the most stable defect species for most values of the electronic bandgap in both and amorphous alumina (Al 2 O 3 ). Further, active learned Gaussian approximation potentials (GAP) were developed using AIMD data to study temperature dependent long time proton diffusion in alumina. Diffusivity calculations from GAP-MD simulations are found to be comparable with of the AIMD data, while being ~340 times faster and scalable to larger systems. Comparisons with diffusivity values for other interstitial charge states (H i 0 and H i -1 ) and published experimental literature indicate that H i +1 diffusion is the likely mechanism of hydrogen transport. A good agreement is obtained between H i +1 diffusivity calculated in α-Al 2 O 3 from DFT: 5.05 10 -3 exp(-0.81 eV/k B /T) cm 2 /s and reported experiment: 9.7X10 -4 exp(-0.83 eV/k B /T) cm 2 /s. Computationally and experimentally calculated energy barriers (0.81 and 0.83 eV respectively) only differ by 2.5%. Similarly, the pre-exponential diffusion coefficients only differ by 0.5 orders of magnitude. Moreover, the diffusivity of H i +1 in amorphous Al 2 O 3 in the 1000–2000 K range is calculated to be 2.53X10 -2 exp(-0.89 eV/k B /T), just one order of magnitude higher than the corresponding value in α-Al 2 O 3 . This suggests that local structural disorder does not significantly affect the energy landscape and diffusion behavior of H i +1 in Al 2 O 3 . Overall, these results show promise for the application of alumina polymorphs as hydrogen permeation barriers.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2021

Analysis and prediction of reaction kinetics using the degree of rate control

“Degree of rate control” (DRC) analysis provides a quantitative approach for analysing the kinetics of multi-step reaction mechanisms that has been widely applied to both heterogeneous and homogeneous catalysis research, as well as electrocatalysis. The DRC of any given transition state or intermediate is defined as a partial derivative such that it approximately equals the fractional increase in net rate to the product of interest per differential decrease in its standard-state free energy for that species (÷RT), holding constant the standard-state free energies of all other transition states and intermediates. Even very complex mechanisms usually have only a few species with non-zero DRCs and are thus the species whose interactions with the catalyst most strongly affect the net rate. These key DRC values thus offer a simple and intuitive route to optimize catalyst materials, especially with the assistance of computational methods like density functional theory (DFT). These high-DRC species are also the species whose energetics must be most accurately measured or calculated to achieve an accurate kinetic model for any reaction mechanism. In simple cases with a single “rate-determining step”, the DRC for its transition state (TS) is + 1. Catalyst-bound intermediates, on the other hand, often have negative DRCs equal to a small integer times their fractional occupancy of catalyst sites. The apparent activation energy equals a weighted average of the standard-state enthalpies (relative to reactants) of all the species (intermediates, transition states and products) in the reaction mechanism, each weighted by its DRC (+RT). It has been shown that the apparent transfer coefficient in electrocatalysis, an inverted form of the Tafel slope, is a weighted average of the number of electrons transferred to generate each intermediate or product species in the mechanism, weighted again by the DRC. Quantitative analysis of kinetic isotope effects (KIEs, or the ratio of net rates for different reactant isotopes) in complex mechanisms has shown that the logarithm of the KIE equals the weighted average over all species in the mechanism of the difference between the two isotopes in their standard-state free energies (÷RT), again weighted by the DRC. The reaction orders with respect to fluid-phase concentrations of reactants, products and intermediates have also been proven to be directly related to DRCs. Thus, there are numerous experimental observables which equate to short linear combinations of DRCs, so that combinations of experimental measurements might provide access to DRC values. Since its invention for transition states in 1994 and its generalization to include intermediates in 2009, the DRC has thus far mainly been calculated for microkinetic models based either entirely on DFT or on DFT with the key energies (i.e., those for high-DRC species) being fine-tuned to match experiments. The relationships summarized in this work provide new opportunities for using experiments earlier in the development and optimization of microkinetic models that require input from computational catalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Ultrafast X-ray Spectroscopy of Intersystem Crossing in Hexafluoroacetylacetone: Chromophore Photophysics and Spectral Changes in the Face of Electron-Withdrawing Groups

Intersystem crossings between singlet and triplet states represent a crucial relaxation pathway in photochemical processes. Herein, we probe the intersystem crossing in hexafluoro-acetylacetone with ultrafast X-ray transient absorption spectroscopy at the carbon K-edge. In this study, we observe the excited state dynamics following excitation with 266 nm UV light to the 1 ππ* (S 2 ) state with element and site-specificity using a broadband soft X-ray pulse produced by high harmonic generation. These results are compared to X-ray spectra computed from orbital optimized density functional theory methods. It is found that the electron-withdrawing fluorine atoms decongest the X-ray absorption spectrum by enhancing separation between features originating from different carbon atoms. This facilitates the elucidation of structural and electronic dynamics at the chromophore. The evolution of the core-to-valence resonances at the carbon K-edge reveals an ultrafast population transfer between the 1 nπ* (S 1 ) and 3 ππ* (T 1 ) states on a 1.6 ± 0.4 ps time scale, which is similar to the 1.5 ps time scale earlier observed for acetylacetone [ J. Am. Chem. Soc. 2017, 139, 16576—16583, DOI: 10.1021/jacs.7b07532]. It therefore appears that terminal fluorination has little influence on the intersystem crossing rate of the acetylacetone chromophore. In addition, the significant role of hydrogen-bond opened and twisted rotational isomers is elucidated in the excited state dynamics by comparison of the experimental transient X-ray spectra with theory.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Revisiting the Hole Size in Double Helical DNA with Localized Orbital Scaling Corrections

The extent of electronic wave function delocalization for the charge carrier (electron or hole) in double helical DNA plays an important role in determining the DNA charge transfer mechanism and kinetics. The size of the charge carrier’s wave function delocalization is regulated by the solvation induced localization and the quantum delocalization among the π stacked base pairs at any instant of time. Using a newly developed localized orbital scaling correction (LOSC) density functional theory method, we accurately characterized the quantum delocalization of the hole wave function in double helical B-DNA. This approach can be used to diagnose the extent of delocalization in fluctuating DNA structures. Our studies indicate that the hole state tends to delocalize among 4 guanine–cytosine (GC) base pairs and among 3 adenine–thymine (AT) base pairs when these adjacent bases fluctuate into degeneracy. The relatively small delocalization in AT base pairs is caused by the weaker π–π interaction. This extent of delocalization has significant implications for assessing the role of coherent, incoherent, or flickering coherent carrier transport in DNA.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Calculated Reduction Potentials of Electrolyte Species in Lithium–Sulfur Batteries

In this work, reduction potentials of electrolyte molecules in the lithium–sulfur (Li/S) battery and their variations in several solvent environments are studied using the density functional theory method with Dunning’s triple-ζ correlation consistent basis set. Reliable reduction potential values are key for electrolyte additive design needed for suppressing polysulfide dissolution shuttle mechanism, resulting in poor cycle performance and severe self-discharge of the Li/S battery. Although isolated electrolyte molecules have reduction potentials outside the operating voltage range of the Li/S battery, complexation with other electrolyte species enables the electrolyte molecules to be reduced within the operating voltage range. Among the electrolyte species considered in this study, bis(fluorosulfonyl)imide (FSI – ) and fluoroethylene carbonate (FEC) yield reduction potentials within the expected range, suggesting the development of fluorine-containing additives as a promising line of research.

25 ENERGY STORAGE↗

DOE OSTI2023

Ethylene Hydrogenation Molecular Mechanism on MoC y Nanoparticles

Ethylene hydrogenation catalyzed by MoC y nanoparticles has been studied by means of density functional theory methods and several models. These include MetCar (Mo 8 C 12 ), Nanocube (Mo 14 C 13 ), and Mo 12 C 12 nanoparticles as representatives of experimental MoC y nanostructures. The effect of hydrogen coverage has been studied in detail by considering low-, intermediate-, and high-hydrogen regimes. The calculated enthalpy and energy barriers show that ethylene hydrogenation is feasible on the MetCar, Mo 12 C 12 , and Nanocube but at low, medium, and high hydrogen coverages, respectively. An additional step, related to the H* migration from a Mo to a C site in the nanoparticle, has been found to be the key to establishing the best hydrogenation system. In most cases, the reactions are exothermic, featuring low hydrogenation energy barriers, especially for the Nanocube at high hydrogen coverage. In addition, the calculated adsorption Gibbs free energy shows that, for this system, the C 2 H 4 adsorption is feasible in the 300–400 K temperature range and pressures from 10 –10 to 2 atm. For the hydrogenation steps, calculated transition state theory rates show that the overall process is limited by the first hydrogenation step (C 2 H 4 → C 2 H 5 ) at temperatures of 330–400 K. However, at the lower temperatures of 300–320 K, the reaction rates are comparable for the two steps. The present results indicate that the Mo 14 C 13 Nanocube models of MoC y nanoparticles exhibit appropriate thermodynamic and kinetic features to catalyze ethylene hydrogenation at a high-hydrogen-coverage regime. The present findings provide a basis for understanding the chemistry of active MoC y catalysts, suggest appropriate working conditions for the reaction to proceed, and provide a basis for future experimental studies.

03 NATURAL GAS↗

DOE OSTI2021

Optimizing accuracy and efficacy in data-driven materials discovery for the solar production of hydrogen

The production of hydrogen fuels, via water splitting, is of practical relevance for meeting global energy needs and mitigating the environmental consequences of fossil-fuel-based transportation. Water photoelectrolysis has been proposed as a viable approach for generating hydrogen, provided that stable and inexpensive photocatalysts with conversion efficiencies over 10% can be discovered, synthesized at scale, and successfully deployed. While a number of first-principles studies have focused on the data-driven discovery of photocatalysts, in the absence of systematic experimental validation, the success rate of these predictions may be limited. We address this problem by developing a screening procedure with co-validation between experiment and theory to expedite the synthesis, characterization, and testing of the computationally predicted, most desirable materials. Here, starting with 70 150 compounds in the Materials Project database, the proposed protocol yielded 71 candidate photocatalysts, 11 of which were synthesized as single-phase materials. Experiments confirmed hydrogen generation and favorable band alignment for 6 of the 11 compounds, with the most promising ones belonging to the families of alkali and alkaline-earth indates and orthoplumbates. This study shows the accuracy of a nonempirical, Hubbard-corrected density-functional theory method to predict band gaps and band offsets at a fraction of the computational cost of hybrid functionals, and outlines an effective strategy to identify photocatalysts for solar hydrogen generation.

08 HYDROGEN↗

DOE OSTI2021

Correction: Enhanced 5f-δ bonding in [U(C 7 H 7 ) 2 ] - : C K-edge XAS, magnetism, and ab initio calculations

The authors regret that two references to the multi-configurational pair-density functional theory methods used in the wavefunction calculations did not appear in the main article. The references were originally included in the Methods section of the Electronic Supplementary Information as ref. 17a and b, but should also have appeared in the second paragraph on page 9564. The references are listed below as ref. 1 and 2. The Royal Society of Chemistry apologises for these errors and any consequent inconvenience to authors and readers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Catlas: an automated framework for catalyst discovery demonstrated for direct syngas conversion

Catalyst discovery is paramount to support access to energy and key chemical feedstocks in a post fossil fuel era. Exhaustive computational searches of large material design spaces using ab initio methods like density functional theory (DFT) are infeasible. We seek to explore large design spaces at relatively low computational cost by leveraging large, generalized, graph-based machine learning (ML) models, which are pretrained and therefore require no upfront data collection or training. We present Catlas, a framework that distributes and automates the generation of adsorbate-surface configurations and ML inference of DFT energies to achieve this goal. Catlas is open source, making ML assisted catalyst screenings easy and available to all. To demonstrate its efficacy, we use Catlas to explore catalyst candidates for the direct conversion of syngas to multi-carbon oxygenates. For this case study, we explore 947 stable/metastable binary, transition metal intermetallics as possible catalyst candidates. On this subset of materials, we are able to predict the adsorption energy of key descriptors, *CO and *OH, with good accuracy (0.16, 0.14 eV MAE, respectively). Using the projected selectivity towards C 2+ oxygenates from an existing microkinetic model, we identified 144 candidate materials. For 10 promising candidates, DFT calculations reveal a good correlation with our assessment using ML. Finally, among the top elemental combinations were Pt–Ti, Pd–V, Ni–Nb, and Ti–Zn, all of which appear unexplored experimentally.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗