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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,657 records · Page 92
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Reactive Transport Modeling of Hydrogen Production from Serpentinization of Olivine

Hydrogen production from serpentinization of ultramafic rocks represents a promising natural pathway for generating carbon-free energy, yet its kinetics and controlling factors remain incompletely understood. A key challenge in advancing serpentinization research lies in the heterogeneity of porosity and permeability in rocks, which leads to nonuniform fluid velocity fields, as well as uncertainties in estimating reactive surface area and identifying appropriate mineral reaction equilibria. Additional complexities arise from the role of dissolved SiO 2 , Fe 2+ /Fe 3+ partitioning, and the limited effect of pH variations within the strongly alkaline regime on hydrogen yields. These challenges hinder straightforward extrapolation from laboratory tests to practical applications of hydrogen production from natural rocks. Here, in this work, we address these questions using a simulation-based reactive transport modeling framework calibrated against controlled laboratory experiments reported elsewhere. The model couples geochemical kinetics, multiphase flow, and mineralogical feedbacks, enabling systematic evaluation of how surface area, dissolved silica concentration, Fe redox state, temperature, and pressure govern serpentinization and H2 generation. We find that surface area exerts the strongest control on reaction rates and hydrogen yields, while Fe 2+ /Fe 3+ ratios act as secondary modulators. Elevated dissolved silica concentrations suppress hydrogen production but accelerate serpentine precipitation, whereas increasing pH beyond 12 within the strongly alkaline regime produces only marginal gains. Finally, we demonstrate that integrating targeted experiments with calibrated simulations offers a powerful and efficient approach for predicting hydrogen yields and assessing parameter trade-offs in industrial-scale applications. This integration can substantially reduce the experimental burden while improving predictive capability, thereby enhancing both the mechanistic understanding and the practical feasibility of hydrogen production from serpentinization.

08 HYDROGEN↗
DOE OSTI2018

Initiation of Flash Boiling of Multicomponent Miscible Mixtures with Application to Transportation Fuels and Their Surrogates

This paper presents methodologies to predict thermodynamic conditions that initiate flash boiling by spontaneous nucleation of liquids consisting of hundreds of miscible liquids and their lower order surrogate mixtures. The methods are illustrated with a kerosene-based fuel and a seven-component surrogate for it. The predictions are compared to measurements of nucleation temperatures obtained from a pulse-heating technique that rapidly heats a microscale platinum film immersed in a pool of the test fluid. Nucleation temperatures are predicted using a generalized corresponding states principle (GCSP), and a modification of classical nucleation theory that considers the mixture as a pseudo single component fluid (PSCF). The intent is to offer a simple means to predict the initiation of flash boiling that can have important consequences for fuel efficiency in combustion engines. We show that contact angle has a strong effect such that predicted and measured spontaneous nucleation temperatures agree for a given heating rate only if contact angle is accounted for in the theory. For low heating rates (less than 2 × 10 7 K/s), predicted nucleation temperatures, assuming a spherical bubble, are 13% higher than measurements. The gap is closed to about 6% as the heating rate reaches 3 × 10 8 K/s (the highest that could be reached in the experiments) and to less than 1% when the measured nucleation temperature is extrapolated to an asymptotic (zero contact angle) limit. Here, the PSCF method (using mixture properties as mole fraction averages of mixture component properties evaluated at the same reduced temperatures as the mixtures) and the GSCP predict virtually identical nucleation temperatures, while the GCSP method does not require any mixture property values.

42 ENGINEERING↗
DOE OSTI2020

Identifying Abandoned Well Sites Using Database Records and Aeromagnetic Surveys

Oil and natural gas are primary sources of energy in the United States. Improved drilling and extracting techniques have led to a renewed interest in historic oil and gas fields, but limited records of legacy wells make new drilling efforts more difficult, as abandoned wells may provide conduits for liquids and gases to migrate into groundwater reservoirs or the atmosphere. Well finding using aeromagnetic surveys pinpoints the location of steel-cased wells, detecting both active and abandoned wells, including buried casings lacking aboveground markers. Here, we present six aeromagnetic surveys conducted in Pennsylvania and Wyoming as case studies, comparing the magnetic points to locations known in databases. In all study sites, more magnetic points were detected than recorded in databases. Based on differences between theoretical database well counts and the actual number of wells detected in surveys, we estimated the total number of wells in Pennsylvania to be 395 000–466 000 and 181 000–182 000 in Wyoming. Extrapolating to the national level, in this work, we estimate the average number of wells in the continental United States is 6.04 ± 19.97 million wells with 1.16 ± 3.84 million of those designated as abandoned wells, within the range of previous abandoned well count estimations. Although aeromagnetic surveys are limited to detecting steel-cased wells and do not differentiate sites based on well status, this study nevertheless demonstrates the utility of aeromagnetic surveys in well finding efforts across the country and shows limitations in database records of oil and natural gas wells.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Temporal Variability of Emissions Revealed by Continuous, Long-Term Monitoring of an Underground Natural Gas Storage Facility

Temporal variability contributes to uncertainty in inventories of methane emissions from the natural gas supply chain. Extrapolation of instantaneous, “snapshot-in-time” measurements, for example, can miss temporal intermittency and confound bottom-up/top-down comparisons. Importantly, no continuous long-term datasets record emission variability from underground natural gas storage facilities despite substantial contributions to sector-wide emissions. We present 11 months of continuous observations on a section of a storage site using dual-frequency comb spectroscopy (DCS observing system) and aircraft measurements. Here, we find high emission variability and a skewed distribution in which the 10% highest 3 h emission periods observed by the continuous DCS observing system comprise 41% of the total observed 3-hourly emissions. Monthly emission rates differ by >12×, and 3-hourly rates vary by 17× in 24 h. We find links to the operating phase of the facility—emission rates, including as a percentage of the total gas flow rate, are significantly higher during periods of injection compared to those of withdrawal. We find that if a high frequency of aircraft flights can occur, then the ground- and aircraft-based approaches show excellent agreement in emission distributions. A better understanding of emission variability at underground natural gas storage sites will improve inventories and models of methane emissions and clarify pathways toward mitigation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Risk-based high-throughput chemical screening and prioritization using exposure models and in vitro bioactivity assays

We present a risk-based high-throughput screening (HTS) method to identify chemicals for potential health concerns or for which additional information is needed. The method is applied to 180 organic chemicals as a case study. We first obtain information on how the chemical is used and identify relevant use scenarios (e.g., dermal application, indoor emissions). For each chemical and use scenario, exposure models are then used to calculate a chemical intake fraction, or a product intake fraction, accounting for chemical properties and the exposed population. We then combine these intake fractions with use scenario-specific estimates of chemical quantity to calculate daily intake rates (iR; mg/kg/day). These intake rates are compared to oral equivalent doses (OED; mg/kg/day), calculated from a suite of ToxCast in vitro bioactivity assays using in vitro-to-in vivo extrapolation and reverse dosimetry. Bioactivity quotients (BQs) are calculated as iR/OED to obtain estimates of potential impact associated with each relevant use scenario. Of the 180 chemicals considered, 38 had maximum iRs exceeding minimum OEDs (i.e., BQs > 1). For most of these compounds, exposures are associated with direct intake, food/oral contact, or dermal exposure. The method provides high-throughput estimates of exposure and important input for decision makers to identify chemicals of concern for further evaluation with additional information or more refined models.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2017

Mineral Reactions in Shale Gas Reservoirs: Barite Scale Formation from Reusing Produced Water As Hydraulic Fracturing Fluid

Hydraulic fracturing for gas production is now ubiquitous in shale plays, but relatively little is known about shale-hydraulic fracturing fluid (HFF) reactions within the reservoir. To investigate reactions during the shut-in period of hydraulic fracturing, experiments were conducted flowing different HFFs through fractured Marcellus shale cores at reservoir temperature and pressure (66 °C, 20 MPa) for one week. Results indicate HFFs with hydrochloric acid cause substantial dissolution of carbonate minerals, as expected, increasing effective fracture volume (fracture volume + nearfracture matrix porosity) by 56-65%. HFFs with reused produced water composition cause precipitation of secondary minerals, particularly barite, decreasing effective fracture volume by 1-3%. Barite precipitation occurs despite the presence of antiscalants in experiments with and without shale contact and is driven in part by addition of dissolved sulfate from the decomposition of persulfate breakers in HFF at reservoir conditions. The overall effect of mineral changes on the reservoir has yet to be quantified, but the significant amount of barite scale formed by HFFs with reused produced water composition could reduce effective fracture volume. In conclusion, further study is required to extrapolate experimental results to reservoir-scale and to explore the effect that mineral changes from HFF interaction with shale might have on gas production.

03 NATURAL GAS↗
DOE OSTI2020

Empirical Evidence for the Potential Climate Benefits of Decarbonizing Light Vehicle Transport in the U.S. with Bioenergy from Purpose-Grown Biomass with and without BECCS

Climate mitigation scenarios limiting global temperature increases to 1.5 °C rely on decarbonizing vehicle transport with bioenergy production plus carbon capture and storage (BECCS), but climate impacts for producing different bioenergy feedstocks have not been directly compared experimentally or for ethanol vs electric light-duty vehicles. A field experiment at two Midwest U.S. sites on contrasting soils revealed that feedstock yields of seven potential bioenergy cropping systems varied substantially within sites but little between. Bioenergy produced per hectare reflected yields: miscanthus > poplar > switchgrass > native grasses ≈ maize stover (residue) > restored prairie ≈ early successional. Greenhouse gas emission intensities for ethanol vehicles ranged from 20 to -179 g CO 2 e MJ –1 : maize stover $\gg$ miscanthus ≈ switchgrass ≈ native grasses ≈ poplar > early successional ≥ restored prairie; direct climate benefits ranged from ~80% (stover) to 290% (restored prairie) reductions in CO 2 e compared to petroleum and were similar for electric vehicles. With carbon capture and storage (CCS), reductions in emission intensities ranged from 204% (stover) to 416% (restored prairie) for ethanol vehicles and from 329 to 558% for electric vehicles, declining 27 and 15%, respectively, once soil carbon equilibrates within several decades of establishment. Extrapolation based on expected U.S. transportation energy use suggests that, once CCS potential is maximized with CO 2 pipeline infrastructure, negative emissions from bioenergy with CCS for light-duty electric vehicles could capture >900 Tg CO 2 e year –1 in the U.S. In the future, as other renewable electricity sources become more important, electricity production from biomass would offset less fossil fuel electricity, and the advantage of electric over ethanol vehicles would decrease proportionately.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Benchmarking Soft Sensors for Remote Monitoring of On-Site Wastewater Treatment Plants

On-site wastewater treatment plants (OSTs) are usually unattended, so failures often remain undetected and lead to prolonged periods of reduced performance. To stabilize the performance of unattended plants, soft sensors could expose faults and failures to the operator. In a previous study, we developed soft sensors and showed that soft sensors with data from unmaintained physical sensors can be as accurate as soft sensors with data from maintained ones. The monitored variables were pH and dissolved oxygen (DO), and soft sensors were used to predict nitrification performance. In the present study, we use synthetic data and monitor three plants to test these soft sensors. We find that a long solids retention time and a moderate aeration rate improve the pH soft-sensor accuracy and that the aeration regime is the main operational parameter affecting the accuracy of the DO soft sensor. We demonstrate that integrated design of monitoring and control is necessary to achieve robustness when extrapolating from one OST to another in the absence of plant-specific fine-tuning. Additionally, we provide a unique labeled dataset for further feature and data-driven soft-sensor development. Our benchmarking results indicate that it is feasible to monitor OSTs with unmaintained sensors and without plant-specific tuning of the developed soft sensors. This is expected to drastically reduce monitoring costs for OST-based sanitation systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

PemNet: A Transfer Learning-Based Modeling Approach of High-Temperature Polymer Electrolyte Membrane Electrochemical Systems

Widespread adoption of high-temperature electrochemical systems such as polymer electrolyte membrane fuel cells (HT-PEMFCs) requires models and computational tools for accurate optimization and guiding new materials for enhancing fuel cell performance and durability. Furthermore, while robust and better suited for extrapolation, knowledge-based modeling has limitations as it is time-consuming and requires information about the system that is not always available (e.g., material properties and interfacial behavior between different materials). Data-driven modeling, on the other hand, is easier to implement but often necessitates large datasets that could be difficult to obtain. In this contribution, knowledge-based modeling and data-driven modeling are combined by implementing a few-shot learning (FSL) approach. A knowledge-based model originally developed for a HT-PEMFCs was used to generate simulated data (887,735 points) and used to pretrain a neural network source model tuned via a genetic algorithm-based AutoML. Then, experimental datasets from HT-PEMFCs with different materials and operating conditions (~50 points each) were used to train six target models via FSL. Models for the unseen data reached high accuracies in all cases (rRMSE < 10%).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Aqueous TiO 2 Nanoparticles React by Proton-Coupled Electron Transfer

Redox reactions of aqueous colloidal TiO 2 4 nm nanoparticles (NPs) have been examined, including both citratecapped and uncapped NPs (c-TiO 2 and uc-TiO 2 ). Photoreduction gave stable blue colloidal c-TiO 2 R NPs with 10–60 electrons per particle. Equilibration of these reduced NPs with soluble redox reagents such as methylviologen (MV 2+ ) provided measurements of the colloid reduction potential as a function of pH. The potentials of c-TiO 2 from pH 2–9 varied linearly with pH, with a slope of –60 ± 5 mV/pH. Estimates of the potential at pH 12 were consistent with extrapolating that line to high pH. The reduction potentials did not correlate with the zeta potentials (ζ) or the surface charge of the NPs across this pH range. Similar reduction potentials were observed for c- and uc-TiO 2 at low pH even though they have quite different ζ potentials. These results show that the common surface-charging explanation of the pH dependence is not tenable in these systems. Oxidation of reduced c-TiO 2 R with the electron-transfer oxidant potassium triiodide (KI 3 ) occurred with a significant drop in pH, showing that protons were released when the electrons were removed from the NPs. Smaller pH drops were observed for the proton-coupled electron transfer (PCET) reagents O 2 (air) and 4-MeO-TEMPO (4-methoxy-2,2,6,6-tetramethylpiperine-1-oxy radical). The difference in the number of protons released with KI 3 vs O 2 and 4-MeO-TEMPO was roughly one proton per electron removed. Thus, the thermodynamically preferred reactivity of these colloidal TiO 2 NPs is PCET over the pH 2–13 range studied. The measured redox potentials refer to the chemical process TiO 2 + H + + e – → TiO 2 ·e – ,H + ; and therefore they do not correspond with an electronic energy such as a conduction band edge or flat band potential. The 1e – /1H + stoichiometry means that the TiO 2 reduction potentials correspond to a TiO 2 –H bond dissociation free energy (BDFE), determined to be 49 ± 2 kcal mol –1 . The PCET description is consistent with the pH dependence of E(TiO 2 /TiO 2 ·e – ,H + ), the release of protons upon oxidation, the lack of correlation with ζ potentials, the similarity of capped and uncapped NPs, and the small change in the potential and BDFE from the first to the last electron/proton pair (H atom) removed. Furthermore, this behavior is suggested to be the norm for redox-active oxide/water interfaces.

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

Sequential Bond Dissociation Energies of Th + (CO) x , x = 3–6: Guided Ion Beam Collision-Induced Dissociation and Quantum Computational Studies

Collision-induced dissociation (CID) of [Th,xC,xO] + , x = 3–6, with Xe is performed using a guided ion beam tandem mass spectrometer (GIBMS). Products are formed exclusively by the loss of CO ligands. Analyses of the kinetic energy-dependent CID product cross sections yield bond dissociation energies (BDEs) of (CO) x–1 Th + –CO at 0 K as 1.09 ± 0.05, 0.82 ± 0.07, 0.63 ± 0.05, and 0.70 ± 0.05 eV, respectively. Different structures of [Th,xC,xO] + were explored using various electronic structure methods, and BDEs for CO ligand loss from precursor [Th,xC,xO] + complexes were computed. Both experimental and theoretical results corroborate that the structures of [Th,xC,xO] + , x = 3–6, formed experimentally are homoleptic thorium cation carbonyl complexes, Th + (CO) x . The nonmonotonic trend in experimental BDEs is reproduced theoretically, although ambiguities in the spin states of the x = 4–6 complexes (doublet or quartet) remain. BDEs calculated at the coupled cluster with single, double, and perturbative triple excitations (CCSD(T))/cc-pVXZ//B3LYP/cc-PVXZ (X = T and Q) level and a complete basis set (CBS) extrapolation agree reasonably well with the experimental values for all complexes. Thorium oxide ketenylidene carbonyl cations, OTh + CCO(CO) y , y = 1–4, were calculated to be the most stable structures of [Th,xC,xO] + , x = 3–6, respectively; however, these are not observed in our experiment. Potential energy profiles (PEPs) having either quartet or doublet spin calculated at the B3LYP/cc-pVQZ level suggest that the failure to observe OTh + CCO(CO) y , y = 1–4, is the result of a barrier corresponding to the C–C bond formation, making the formation of OTh + CCO(CO) y inaccessible kinetically under the present experimental conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Direct Determination of Absolute Absorption Cross Sections at the L-Edge of Dilute Mn Complexes in Solution Using a Transmission Flatjet

The 3d transition metals play a pivotal role in many charge transfer processes in catalysis and biology. X-ray absorption spectroscopy at the L-edge of metal sites probes metal 2p–3d excitations, providing key access to their valence electronic structure, which is crucial for understanding these processes. In this paper, we report L-edge absorption spectra of Mn II (acac) 2 and Mn III (acac) 3 complexes in solution, utilizing a liquid flatjet for X-ray absorption spectroscopy in transmission mode. With this, we derive absolute absorption cross-sections for the L-edge transitions with peak magnitudes as large as 12 and 9 Mb for Mn II (acac) 2 and Mn III (acac) 3 , respectively. We provide insight into the electronic structure with ab initio restricted active space calculations of these L-edge transitions, reproducing the experimental spectra with excellent agreement in terms of shapes, relative energies, and relative intensities for the two complexes. Crystal field multiplet theory is used to assign spectral features in terms of the electronic structure. Comparison to charge transfer multiplet calculations reveals the importance of charge transfer in the core-excited final states. Finally, on the basis of our experimental observations, we extrapolate the feasibility of 3d transition metal L-edge absorption spectroscopy using the liquid flatjet approach in probing highly dilute biological solution samples and possible extensions to table-top soft X-ray sources.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Reaction Mechanism Generator v3.0: Advances in Automatic Mechanism Generation

In chemical kinetics research, kinetic models containing hundreds of species and tens of thousands of elementary reactions are commonly used to understand and predict the behavior of reactive chemical systems. Reaction Mechanism Generator (RMG) is a software suite developed to automatically generate such models by incorporating and extrapolating from a database of known thermochemical and kinetic parameters. Here, we present the recent version 3 release of RMG and highlight improvements since the previously published description of RMG v1.0. Most notably, RMG can now generate heterogeneous catalysis models in addition to the previously available gas- and liquid-phase capabilities. For model analysis, new methods for local and global uncertainty analysis have been implemented to supplement first-order sensitivity analysis. The RMG database of thermochemical and kinetic parameters has been significantly expanded to cover more types of chemistry. The present release includes parallelization for faster model generation and a new molecule isomorphism approach to improve computational performance. RMG has also been updated to use Python 3, ensuring compatibility with the latest cheminformatics and machine learning packages. Overall, RMG v3.0 includes many changes which improve the accuracy of the generated chemical mechanisms and allow for exploration of a wider range of chemical systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Spin Splitting Energy of Transition Metals: A New, More Affordable Wave Function Benchmark Method and Its Use to Test Density Functional Theory

Accurately predicting the spin splitting energy of chemical species is important for understanding their reactivity and magnetic properties, but it is very challenging, especially for molecules containing transition metals. One impediment to progress is the scarcity of accurate benchmark data. Here we report a set of calculations designed to yield reliable benchmarks for simple transition-metal complexes that can be used to test density functional methods that are affordable for large systems of more practical interest. Various wave function methods are tested against experiment for Fe 2+ , Fe 3+ , and Co 3+ , including CASSCF, CASPT2, CASPT3, MRCISD, MRCISD+Q, ACPF, AQCC, CCSD(T), and CASPT2/CCSD(T) and also a new method called CASPT2.5, which is performed by taking the average of the CASPT2 and CASPT3 energies. Furthermore, we find that MRCISD+Q, ACPF, and AQCC require smaller active spaces for good accuracy than are required by CASPT2 and CASPT3, and this aspect may be important for calculations on larger molecules; here we find that CASPT2.5 extrapolated to a complete basis set is the most suitable method—in terms of computational cost and in terms of accuracy on monatomic systems—and therefore we chose this method for molecular benchmarks. Then Kohn–Sham density functional calculations with 60 exchange-correlation functionals are tested for FeF 2 , FeCl 2 , and CoF 2 . We find that MN15-L, M06-SX, and revM06 have very good agreement with CASPT2.5 benchmarks in terms of both the spin splitting energy and the optimized geometry for each spin state. In addition, we recommend def2-TZVP as the most suitable basis set to perform density functional calculations for molecular spin splitting energies; extra polarization functions in the basis set do not help to increase the accuracy of the spin splitting energy in KS calculations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

DMRG on Top of Plane-Wave Kohn–Sham Orbitals: A Case Study of Defected Boron Nitride

In this paper, we analyze the numerical aspects of the inherent multi reference density matrix renormalization group (DMRG) calculations on top of the periodic Kohn-Sham density functional theory using the complete active space approach. The potential of the framework is illustrated by studying hexagonal boron nitride nanoflakes embedding a charged single boron vacancy point defect by revealing a vertical energy spectrum with a prominent multireference character. We investigate the consistency of the DMRG energy spectrum from the perspective of sample size, basis size, and active space selection protocol. Results obtained from standard quantum chemical atom-centered basis calculations and plane-wave based counterparts show excellent agreement. Furthermore, we also discuss the spectrum of the periodic sheet which is in good agreement with extrapolated data of finite clusters. These results pave the way toward applying the DMRG method in extended correlated solid-state systems, such as point defect qubit in wide band gap semiconductors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Stochastic Real-Time Second-Order Green’s Function Theory for Neutral Excitations in Molecules and Nanostructures

We present a real-time second-order Green’s function (GF) method for computing excited states in molecules and nanostructures, with a computational scaling of O(N e 3 ), where N e is the number of electrons. The cubic scaling is achieved by adopting the stochastic resolution of the identity to decouple the 4-index electron repulsion integrals. To improve the time propagation and the spectral resolution, we adopt the dynamic mode decomposition technique and assess the accuracy and efficiency of the combined approach for a chain of hydrogen dimer molecules of different lengths. We find that the stochastic implementation accurately reproduces the deterministic results for the electronic dynamics and excitation energies. Furthermore, we provide a detailed analysis of the statistical errors, bias, and long-time extrapolation. Overall, the approach offers an efficient route to investigate excited states in extended systems with open or closed boundary conditions.

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

Generating Protein Structures for Pathway Discovery Using Deep Learning

Resolving the intricate details of biological phenomena at the molecular level is fundamentally limited by both length- and time scales that can be probed experimentally. Molecular dynamics (MD) simulations at various scales are powerful tools frequently employed to offer valuable biological insights beyond experimental resolution. However, while it is relatively simple to observe long-lived, stable configurations of, for example, proteins, at the required spatial resolution, simulating the more interesting rare transitions between such states often takes orders of magnitude longer than what is feasible even on the largest supercomputers available today. One common aspect of this challenge is pathway discovery, where the start and end states of a scientific phenomenon are known or can be approximated, but the mechanistic details in between are unknown. Here, we propose a representation-learning-based solution that uses interpolation and extrapolation in an abstract representation space to synthesize potential transition states, which are automatically validated using MD simulations. The new simulations of the synthesized transition states are subsequently incorporated into the representation learning, leading to an iterative framework for targeted path sampling. Our approach is demonstrated by recovering the transition of a RAS-RAF protein domain (CRD) from membrane-free to interacting with the membrane using coarse-grain MD simulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Static Subspace Approximation for Random Phase Approximation Correlation Energies: Applications to Materials for Catalysis and Electrochemistry

Modeling complex materials using high-fidelity, ab initio methods at low cost is a fundamental goal for quantum chemical software packages. The GW approximation and random phase approximation (RPA) provide a unified description of both electronic structure and total energies using the same physics in a many-body perturbative approach that can be more accurate than generalized-gradient density functional theory (DFT) methods. However, GW/RPA implementations have historically been limited to either specific materials classes or application toward small chemical systems. Here, the static subspace approximation allows for reduced cost full-frequency GW/RPA calculations and has previously been benchmarked thoroughly for GW calculations. Here, we describe our approach to including partial occupations of electronic orbitals in full-frequency GW and RPA calculations for the study of electrocatalysts. We benchmarked RPA total energy calculations using the subspace approximation across a diverse test suite of materials for a variety of computational parameters. The benchmarking quantifies the impact of different extrapolation procedures for representing the static polarizability at infinite screened cutoff, and shows that using screened cutoffs above 20-25 Ryd result in diminishing accuracy returns for predicting RPA total energies. Additionally, for moderately sized electrocatalytic models, 2-3 times fewer computational resources are used to compute RPA total energies by representing the static polarizability with 20-30% of the static subspace basis, with an error of approximately 0.01 eV or better in RPA adsorption energy calculations. Finally, we show that for these electrochemical models RPA can shift DFT adsorption energy shifts by up to 0.5 eV and that GW can frequently shift DFT eigenvalues of surface and adsorbate states by approximately 0.5-1 eV.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗