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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Superstructure Optimization of Waste Plastic Pyrolysis, Integrating Thermal, Catalytic, and Plasma Technologies with Machine Learning

Global plastic waste generation exceeds 430 million tonnes per year, yet fewer than 9% are recycled in the United States. Pyrolysis offers a chemical recycling route at scale, but existing techno-economic and life cycle assessments fix product yields to single pure polymers, producing economic and environmental outputs that break down when the feed composition changes. Here, we present a superstructure optimization framework that addresses this by embedding a composition-aware random forest yield predictor, trained on 566 pyrolysis experiments, within a full-scale process simulation. Product distributions update automatically as feed allocation shifts across four reactor chemistries: conventional thermal, catalytic (HZSM-5), thermal oxo-degradation, and nonequilibrium CO2 plasma. The optimal superstructure achieves minimum selling prices of −0.56 to −0.76/kg feed and global warming potentials of −0.276 to −0.322 kg CO2-eq/kg feed across four commodity price scenarios, confirming profitable, carbon-negative operation without tipping fees. Carbon abatement costs of $\$$0.46 to $\$$1.25/kg CO2-eq are competitive with direct air capture. Sensitivity analysis shows that the catalytic-plasma split fraction is the single largest driver of both economic and climate performance, while hydrocracking allocation in the wax upgrading stage is emission-neutral across the full variable range. Mixed plastic waste streams, evaluated as composition-variable feedstocks rather than pure resins, are profitable and carbon-negative across realistic market conditions. These results give a quantitative basis for reactor selection, circular economy investment, and policy design targeting chemical recycling on a large scale.

Life cycle assessment↗
DOE OSTI2025

Sulfate Promotes Compact CaCO 3 Formation and Protects Portland Cement from Supercritical CO 2 Attack

Supercritical (sc) CO 2 in geologic carbon sequestration (GCS) can chemically and mechanically deteriorate wellbore cement, raising concerns for long-term operations. In contrast to the conventional view of “sulfate attack” on cement, we found that adding 0.15 M sulfate to the acidic brine can significantly reduce the impact of scCO 2 attack on Portland cement, resulting in stronger cement than that found in a sulfate-free system. Scanning electron microscopy revealed a decreased total attack depth in reacted cement in the presence of sulfate. With a newly defined minimum porosity term in reactive transport modeling, our model suggests that sulfate caused CaCO 3 to fill more nanopore spaces in the cement. Small angle X-ray scattering experiments also showed that sulfate can decrease the pore sizes of the carbonate layer. The results suggest that the interactions between sulfate and cement can generate a less porous CaCO 3 layer, which better resists acidic brine. Using this mechanism as a proof-of-concept, we tested the incorporation of sodium sulfate into Portland cement and synthesized new cement composites that show stronger resistance against scCO 2 attacks. Finally, these newly discovered interfacial interactions between CaCO 3 and sulfate provide new insights into engineering mechanically strong and green materials for safer GCS.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Techno-Economic Analysis and Life Cycle Assessment of Alternative Fuels for Locomotives in the U.S. Freight Rail Sector

Freight rail is more energy-efficient than truck transport over long-haul distances, offering a low-energy and emissions-intensive option for transporting freight. This study evaluates techno-economic analysis and life cycle assessment of seven alternative unblended fuels for freight locomotive engines─biodiesel, renewable diesel (RD), bio-oils, methanol, dimethyl ether (DME), ethanol, and ammonia─across 16 fuel pathways utilizing soybean, corn, woody biomass, renewable hydrogen, and waste sources, e.g., sludge, manure, and industrial CO 2 , and compares these to conventional diesel. The minimum fuel selling price (MFSP) ranged from $\$2.05$ to $\$8.27$ per diesel gallon equivalent (2020 US dollars), with biocrude and RDs produced from hydrothermal liquefaction (HTL) of sludge having the lowest MFSPs due to coproduct credits and avoided waste treatment cost. Life cycle GHG emissions ranged from −41 to 53 g of CO 2 e/MJ. RD from waste via HTL achieves negative emissions by diverting sludge/manure from GHG-intensive conventional management. Few pathways such as biocrude, methanol, and DME require additional control for SO X emissions in the refinery, while ethanol, FT-diesel, and bio-oil require additional control for particulate matter emissions. Bio-oil and RD from sludge have lower marginal abatement cost or MAC (–$\$38$/tonne CO 2 lowest) while methanol and ammonia with renewable hydrogen have higher MAC ($\$490$/tonne CO 2 maximum).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Cost and Carbon Intensity Implications of Coprocessing Sustainable Aviation Fuel at Petroleum Refineries

Sustainable aviation fuel (SAF) will play a critical role in decarbonizing the aviation industry. Among SAF production pathways, alcohol-to-jet (ATJ) stands out for its scalability, supported by abundant feedstock availability and a well-established bioethanol industry. However, significant reductions in SAF carbon intensity (CI) require the use of future feedstocks (e.g., cellulosic) whose adoption is hindered by high capital costs for feedstock processing and ethanol upgrading. Here, we evaluate the financial viability and environmental implications of integrating an ATJ SAF biorefinery within a petroleum refinery, utilizing miscanthus and switchgrass as example feedstocks. Three scenarios are evaluated: standalone (benchmark), colocated, and repurposing (coprocessing SAF within the petroleum refinery). Results show repurposing reduces baseline capital costs by 36% and SAF minimum selling price (MSP) by 12% to 8.14 USD·gal −1 ; the superior performance of repurposing is consistent across both feedstocks. Integration has a limited effect on SAF CI, which remains stable across scenarios, whereas using cellulosic feedstocks reduces CI by over 70% relative to corn, with baseline values of 17.01 g CO 2 e· MJ −1 for miscanthus and 12.23 g CO 2 e·MJ −1 for switchgrass. Global sensitivity analysis reveals MSP declines with greater coprocessing levels.

09 BIOMASS FUELS↗
DOE OSTI2026

A Kinetic Model-Driven Techno-Economic Analysis of Plastic Pyrolysis: Linking Process Dynamics to Economic Viability

This study employs a kinetic model integrated into Aspen Plus to predict pyrolysis product distribution under various conditions. A techno-economic assessment calculated the minimum selling price (MSP) of pyrolysis oil under different operating conditions for the baseline capacity of 100 kta, and across eight processing capacities ranging from 30 to 150 kta. The lowest MSP under the baseline capacity is estimated at $\$$420/ton, which is 33% lower than the 2023 average US crude oil price ($\$$74.6/bbl, equivalent to $\$$634/ton based on the density of pyrolysis oil). Under Monte Carlo simulation, accounting for variability in key economic and technical parameters, the mean MSP is estimated at $\$$1137/ton. The economic viability depends on feedstock price remaining below $\$$320/ton, defining the break-even feedstock price threshold. Sensitivity analysis further identifies capital investment and transportation cost as key economic drivers. Capacities beyond 90 kta show limited economies of scale benefits. Reducing product storage time cuts capital costs by 7% but raises operational risk. Uncertainty analysis suggests the economic feasibility of pyrolysis oil is unlikely to compete with crude oil without policy incentives.

petrochemicals↗
DOE OSTI2025

Fermi’s Golden Rule Rate Expression for Transitions Due to Nonadiabatic Derivative Couplings in the Adiabatic Basis

Starting from a general molecular Hamiltonian expressed in the basis of adiabatic electronic and nuclear position states, where a compact and complete expression for the nonadiabatic derivative coupling (NDC) Hamiltonian term is obtained, we provide a general analysis of the Fermi’s golden rule (FGR) rate expression for nonadiabatic transitions between adiabatic states. We then consider a quasi-adiabatic approximation that uses crude adiabatic states and NDC couplings, both evaluated at the minimum potential energy configuration of the initial adiabatic state, for the definition of the zeroth and first-order terms of the Hamiltonian. Although the application of this approximation is rather limited, it allows deriving a general FGR rate expression without further approximation while accounting for non-Condon contribution to the FGR rate arising from momentum operators of NDC terms and its coupling with vibronic displacements. For a generic and widely used model where all nuclear degrees of freedom and environmental effects are represented as linearly coupled harmonic oscillators, we derive a closed-form FGR rate expression that requires only Fourier transform. The resulting rate expression includes quadratic contributions of NDC terms and their couplings to Franck–Condon modes, which require evaluation of two additional bath spectral densities in addition to the conventional one that appears in a typical FGR rate theory based on the Condon approximation. Model calculations for the case where nuclear vibrations consist of both a sharp high-frequency mode and an Ohmic bath spectral density illustrate new features and implications of the rate expression. We then apply our theoretical expression to the nonradiative decay from the first excited singlet state of azulene, which illustrates the utility and implications of our theoretical results.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Low-Lying Excited States of Linear All- Trans Polyenes: Insights from Analytic Gradient and Nonadiabatic Coupling Calculations Based on Multireference Configuration Interaction

Polyenes serve as a rigorous test for theoretical models and electronic structure methods, playing a key role in advancing computational and theoretical chemistry. Here, we present a high-level theoretical investigation of linear, all-trans polyenes using energy gradients and nonadiabatic coupling vectors based on an MR-CISD wave function to describe electronic transitions involving the ground state (1 1 A g – ) and three low-lying excited states (2 1 A g – , 1 1 B u + , and 2 1 B u – ) of hexatriene, octatetraene, and decapentaene. This approach enables accurate evaluation of both adiabatic and vertical excitation and emission energies, yielding results in excellent agreement with experiment, as well as locating minima on the crossing seam between adiabatic states. Our results show that vertical excitation energies to the 1 1 B u + state are blue-shifted by 0.2–0.3 eV relative to the experimental absorption maximum, whereas the vertical emission energy from the 2 1 A g – state is red-shifted by ∼0.2 eV relative to the experimental emission maximum. Upon relaxation from the Franck–Condon geometry, the 2 1 A g – state stabilizes by around 1 eV, compared to 0.2–0.3 eV for the 1 1 B u + state. An analysis of the S 1 /S 0 crossing seam in hexatriene shows that its minimum involves asymmetric backbone deformations and provides an efficient channel for ultrafast internal conversion to the ground state, consistent with the absence of detectable fluorescence in this molecule. These results demonstrate the power of analytic gradients and nonadiabatic coupling vectors based on an MR-CISD wave function for accurately characterizing the electronic structure and photophysics of polyenes.

Excited states↗
DOE OSTI2024

Comparative Study of Neutral and Cationic Sn 2 H 2 : Toward Laboratory Detection of the Cation

Group 14 M 2 H 2 isomers (M = Si, Ge, Sn, and Pb) have attracted interest due to their radically differing electronic structures from acetylene. To better understand the Sn–H interactions of the neutral and cationic Sn 2 H 2 structures, we present the most rigorous study of these systems to date. CCSD(T)/cc-pwCVTZ harmonic frequencies are presented as the first predictions for the neutral and cationic species to date. CCSDT(Q)/CBS relative energies are reported using the focal point approach, confirming the butterfly isomer as the global minimum on the potential energy surface for both the neutral and cationic species. In all, there exist 7 minima and 15 transition states. NBO analysis is also performed to elucidate the changes in bond order going from neutral to cation across all isomers of Sn 2 H 2 . Our results provide insights into the important Sn–H interaction and provide guidance for future work that may detect Sn 2 H$^{+}_{2}$ in the laboratory for the first time.

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

Toward a Machine Learning Approach to Interpreting X-ray Spectra of Trace Impurities by Converting XANES to EXAFS

The fact that the photoabsorption spectrum of a material contains information about the atomic structure, commonly understood in terms of multiple scattering theory, is the basis of the popular extended X-ray absorption spectroscopy (EXAFS) technique. How much of the same structural information is present in other complementary spectroscopic signals is not obvious. Here we use a machine learning approach to demonstrate that within theoretical models that accurately predict the EXAFS signal, the extended near-edge region does indeed contain the EXAFS-accessible structural information. We do this by exhibiting deep operator neural networks (DeepONets) that have learned the relationship between the extended and near edge portions of the X-ray absorption spectrum to predict the former from the latter. We find that we can accurately predict the EXAFS spectrum between 6 and 14 Å –1 from the first 6 Å –1 (≈100 eV) of the absorption spectrum of Cu 2 + substitutional defects in the Fe 3+ mineral hematite (α-Fe 2 O 3 ). This surprising finding implies that theoretical analyses of X-ray absorption spectra could be implemented that extract the same conclusions as high-quality EXAFS studies from spectra collected over a much smaller range of photon energies. This relaxes a host of experimental limitations related to the X-ray source and measurement sample, including collection time, minimum dopant concentration, source brilliance, and energy range. We describe the theoretical data sets and DeepONet construction and show that the resulting DeepONets produce EXAFS that recovers linear combination fits to experimental data with accuracy approaching the original ab initio calculations. We discuss the implications of our findings for minor constituent characterization and for understanding the information content of spectroscopic data more broadly, including how this approach might be applied to measured experimental spectra. In conclusion, to encourage similar efforts, the simulated X-ray spectra, machine learning, and fitting code are publicly available.

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

Low-Energy Isomers of the Magic Number H + (H 2 O) 21 Cluster

Electronic structure calculations are used to characterize low-energy isomers of H + (H 2 O) 21 . Eleven different classes of isomers, based on the (H 2 O) 20 pentagonal dodecahedron with the excess proton localized on the surface (as a hydrated hydronium ion) and the “extra” water molecule located in the interior of the cluster, are characterized. In 10 of these classes, the internal water molecule is engaged in six 5-membered rings, but in the remaining class, which is predicted to start at only 0.6 kcal/mol above the global minimum, the internal water is engaged in a 4- membered ring, an additional 6-membered ring, and four 5- membered rings. In addition, isomers with two 4-membered rings and two 6-membered rings on the cluster surface are predicted to start at only ∼1.3 kcal/mol above the lowest-energy dodecahedralbased structure.

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

A Comparison of Electronic Structure Methods for Predicting the Hydrogenation Energies of Candidate Molecules for Hydrogen Storage

The development of novel energy materials and fuels is required to expand current available energy sources. Aiming to reach this goal, there is growing interest in using molecular hydrogen as an energy carrier due to its abundance and high energy density. Liquid organic hydrogen carriers (LOHCs) are a promising route to the large-scale storage and transport of hydrogen for use in the energy economy. The search for thermodynamically viable LOHC molecules for real world use has led to a set of constraints on the dehydrogenation enthalpy and the minimum gravimetric hydrogen capacity. These constraints allow one to formulate the search for an ideal LOHC candidate molecule as an optimization problem well suited to the strengths of machine learning and artificial intelligence computational approaches. A critical barrier to a large-scale, high-throughput screening of LOHC candidate molecules is the lack of reliable training data. Computational electronic structure methods including density functional theory, coupled cluster approximations, and diffusion Monte Carlo can be used to provide training data where experimental data are either unreliable or do not exist. In this work, we use these methods to calculate the dehydrogenation energies and enthalpies of candidate LOHC molecules.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Accurate Prediction of pKb in Amines: Validation of the CAM-B3LYP/6-311+G(d,p)/SMD Model

Amines play several key roles in chemistry and biology and are involved in numerous industrial processes, often with significant economic impacts. Recently, amines are also garnering interest as catalysts for polymer synthesis and for CO 2 fixation, incentivizing the need to rapidly design and screen new amino compounds. Hence, developing reliable methods to predict their physicochemical properties, e.g., the base dissociation constant (pKb), is pivotal. Here, a density functional theory (DFT)-based approach was employed to compute the pKb of substituted amines, exploring the impact of several key parameters, including (i) the number of explicit water molecules at the reaction center, (ii) the van der Waals (vdW) surface, and (iii) solvent polarizability. In previous work, it was determined that including two explicit water molecules at the reaction center resulted in highly accurate pKb estimates for primary amines. Here, we find that including a third water molecule at the reaction center is essential for accurate pKb for secondary and tertiary amines. The revised methodology was then applied to a wider selection of amines, obtaining a minimum average error (MAE) < 0.4. In conclusion, this result represents an extension of our “easy-to-use method,” a simple and direct DFT approach exploiting CAM-B3LYP/SMD/6-311G+(d,p) to compute pKb without post facto modifications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Bond Dissociation Energies of the Actinide Halides AnX, An = Ac–Lr and X = F–I, Utilizing Relativistic Composite Coupled Cluster Approaches

Bond dissociation energies (BDEs) have been calculated for the set of actinide halides AnX with An=Ac, Pa, and Np-Lr and X=F-I. Two composite thermochemistry methods based on the Feller-Peterson-Dixon (FPD) approach have been utilized, one involving spinor-based relativistic CCSD(T) calculations where spin-orbit (SO) was included at the orbital level and another using scalar relativistic CCSD(T) with a posteriori SO contributions based on 2-component multireference configuration interaction calculations. The method that was chosen for a given actinide halide was based on which representation yielded the best single determinant reference determinant for the coupled cluster calculation. The spinor-based method was chosen for all cases except for AmX, CmX, and BkX. Both composite approaches included contributions accounting for basis set truncation, outer-core correlation, the Gaunt interaction, and QED. The scalar FPD results, as well as the spinor-based calculations for AcF, also included higher order electron correlation up through CCSDT(Q). In addition to BDEs, CCSD(T) equilibrium bond lengths, harmonic frequencies, and vibrational anharmonicity constants are reported for all species. Last, the FPD BDEs for the fluorides were used to confirm the trend across the actinide series previously predicted by Gibson using bonding models based atomic promotion energies that provide a single 6d electron for bonding. In particular the local minimum in the BDEs at AmF is confirmed in the present calculations. Furthermore, the BDEs for LrX are predicted to be slightly larger than those of AcX, making them the largest in the actinide halide series.

Actinides↗
DOE OSTI2025

Modeling the Behavior of Complex Aqueous Electrolytes Using Machine Learning Interatomic Potentials: The Case of Sodium Sulfate

Understanding the structure and thermodynamics of solvated ions is essential for advancing applications in electrochemistry, water treatment, and energy storage. While ab initio molecular dynamics methods are highly accurate, they are limited by short accessible time and length scales whereas classical force fields struggle with accuracy. Herein, we explore the structure and thermodynamics of complex monovalent-divalent ion pairs using Na 2 SO 4 (aq) as a case study by applying a machine learning interatomic potential (MLIP) trained on density functional theory (DFT) data. Our MLIP-based approach reproduces key bulk properties such as density and radial distribution functions of water. We provide the hydration structure of the sodium and sulfate ions in the 0.1–2 M concentration range and the one-dimensional and two-dimensional potentials of mean force for the sodium–sulfate ion pairing at the low concentration limit (0.1 M), which are inaccessible to DFT. At low concentrations, the sulfate ion is strongly solvated, leading to the stabilization of solvent-separated ion pairs over contact ion pairs. Minimum energy pathway analysis revealed that coordinating two sodium ions with a sulfate ion is a multistep process whereby the sodium ions coordinate to the sulfate ion sequentially. Finally, we demonstrate that MLIPs allow the study of solvated ions beyond simple monovalent pairs with DFT-level accuracy in their low concentration limit (0.1 M) via statistically converged properties from ns-long simulations.

anions↗
DOE OSTI2026

Nanosecond Structure of Radical Pair Intermediates from High-Frequency Quantum Oscillations: Insight into the Q A •– to Q B Electron Transfer Step in Purple Bacterial Photosynthesis

We demonstrate the validity of our approach to deduce, from the anisotropy of quantum oscillations, the geometry of short-lived radical pair intermediates in photosynthesis. A global fit of a two-dimensional W-band (94 GHz) electron paramagnetic resonance (EPR) experiment provides the same global minimum values for the geometry of the A-side radical pair P 700 •+ A 1A •− in photosystem I (PSI) as observed in a previous Q-band (34 GHz) EPR study, yet with a significantly increased convergence rate of 62%. This demonstrates that the global fit yields the correct radical pair geometry even at Q-band frequencies. With this information, we revisit our previous Q-band study of the cofactor arrangement of P 865 •+ Q A •− , the stabilized charge-separated state in purple bacterial reaction centers (RCs). Analysis of calculated two-dimensional data sets of P 865 •+ Q A •− reveals that the quantum oscillation technique is unaffected by a mirror ambiguity in disordered solids and thus can provide unambiguous solutions for all five Euler angles of the radical pair geometry. This enables us to elucidate the Q A •− to Q B electron transfer step in purple bacterial photosynthesis, the subject of controversial discussions for more than 25 years. Our results show that this electron transfer step involves a gating mechanism requiring a 60° rotation of the headgroup of Q A •− in its binding pocket.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Insights into Rotational and Translational Dynamics in Mixtures of Ethylene Glycol and Choline Chloride Using Nuclear Magnetic Resonance Techniques

This work examines molecular dynamics and interactions in ethylene glycol–choline chloride (EG–ChCl) mixtures across 0–33 mol % ChCl, spanning the true eutectic region near 17–20 mol % and the commonly used 1:2 formulation. We combine pulsed-field-gradient (PFG) diffusion, fast-field-cycling (FFC) relaxometry, temperature-dependent 13 C T 1 , and nuclear Overhauser effect spectroscopy (NOESY) to disentangle local from macroscopic dynamics. PFG and FFC show that both translational and average rotational motions largely track the strong increase in viscosity with ChCl content, with ethylene glycol consistently diffusing faster than the choline cation and no global dynamical anomaly at the eutectic composition. More subtle, site-specific composition effects nevertheless emerge. The ratio of the diffusion coefficient of the hydroxyl group of choline to the diffusion coefficient of the methyl group of choline displays a shallow minimum in the 17–25 mol % region, indicating a modest change in how the hydroxyl-bearing end of choline samples the underlying translational motion relative to the methyl groups. 13 C T 1 analysis shows that rotational correlation times at 25 °C generally increase with ChCl, reflecting viscosity-coupled slowing, while the CH 2 –N α site exhibits a small but reproducible deviation from this monotonic trend near the eutectic. NOESY spectra at similar compositions reveal enhanced cross-relaxation between EG and choline protons, consistent with increased headgroup–solvent contact density rather than a wholesale structural rearrangement. Overall, our multitechnique study demonstrates that EG–ChCl dynamics are predominantly viscosity-dominated, with the eutectic region acting as a subtle dynamical crossover where specific choline segments become maximally coupled to the hydrogen-bond network. These insights refine the structure–dynamics picture of choline-chloride DESs and provide practical guidance for tuning composition in electrochemical, separation, and catalytic applications.

diffusion↗
DOE OSTI2024

Role of Dynamic Polarization Interactions in the Electrical Double Layer at Calcite (104) Interfaces with Aqueous Solutions

Reactions at mineral interfaces with aqueous solutions control many geochemical and biogeochemical processes in the Earth’s critical zone. At the molecular level, insights into important properties such as the structure of the electrical double layer (EDL) at specific mineral interfaces continue to improve due to the increasing fidelity of laboratory instrumentation and computational approaches. However, molecular simulation approaches suffer from limited reach into relevant scales of time, length, and system complexity. To span this gap, a novel hybrid approach that couples first principle plane-wave density functional theory (DFT) with classical DFT (cDFT) is demonstrated and applied to calcite (104) interfaces with various electrolytes. In this approach, a region of interest described using DFT interacts with the surrounding medium described using cDFT to arrive at a self-consistent ground state. Benchmarking against experimental observations and entirely first principle DFT simulations demonstrates that this hybrid model efficiently encompasses the key short-range and collective interactions in the EDL. Simulations of calcite (104)/solution interfaces reveal the key static and dynamic polarization interactions that give rise to structuring of ions and water. Ion hydration interactions have the strongest effect on the depth of the first minimum in the density distribution of counterions at the surface, and the position and width of the first density peak is largely determined by the strength of ion-correlation forces. Finer details of ion distributions are controlled by mutual polarization of the calcite surface and interfacial electrolyte. Finally, this new ability to efficiently and rigorously predict EDL structure at mineral surfaces in contact with complex solutions paves the way to accurately modeling sorption, nucleation, dissolution, and growth in realistic systems.

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

Using Active Learning to Rapidly Develop Machine Learned Diffusion Coefficients of CO 2 Conversion Reagents in Metal–Organic Frameworks

Here, we used a combined molecular dynamics/active learning (AL) approach to create machine learning models that can predict the diffusion coefficient of epichlorohydrin and chloropropene carbonate, the reactant and product of a common CO 2 cycloaddition reaction, in metal–organic frameworks (MOFs). Nanoporous MOFs are effective catalysts for the cycloaddition of CO 2 to epoxides. The diffusion rates within nanoporous catalysts can control the rate of reaction as the reactants and products must diffuse to the active sites within the MOF and then out of the nanoporous material for reusability. However, the diffusion process is routinely ignored when searching for new materials in catalytic applications. Here we verified improvement during the AL process by consistently tracking metrics on the same groups of MOFs to ensure consistency. Metal identity was found to have little impact on diffusion rates, while structural features like pore limiting diameter act as a threshold where a minimum value is needed for high diffusion rates. We identified the MOFs with the highest epichlorohydrin and chloropropene carbonate diffusion coefficients which can be used for further studies of reaction energetics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗