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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Artificial intelligence-driven municipal solid waste sortation and its significance on downstream waste valorization in the United States: Techno-economic and life cycle assessment

Municipal solid waste (MSW) generation is quickly increasing due to population growth, industrialization, and urbanization. Current sorting technologies, e.g., optical sorters have low sortation efficiency and produce contaminated sorted materials unsuitable for downstream valorization. This study evaluates a novel artificial neural network (ANN)-based MSW sortation system through sorting efficiencies, economic performance, and environmental impacts. ANN system can sort up to 8 plastic types and all organic waste (i.e., food, yard, and paper waste) with 99% and 93% recovery efficiency, respectively, compared to optical sortation which could separate only polyvinyl chloride (PVC) plastic ultimately producing mixed plastics (except PVC) and organics streams as products. Nonetheless, their sortation costs are similar, $28.7 for ANN-based sortation and $28 for optical sortation per metric ton (Mt) of MSW at processing capacity of 94,860 metric tons (Mt)/yr of MSW in the United States. Additionally, ANN system produced high purity feedstocks for downstream valorization. A biochar pyrolysis plant modeled to process 3.6 and 14 Mt/h of sorted yard waste outperformed biochar production from mixed organic fraction (MOF), achieving 8% lower production cost and 34% lower global warming impact (1.37 kg CO 2 eq. per kg) compared to biochar derived from mixed organic feedstock (2.08 kg CO 2 eq.).

09 BIOMASS FUELS↗
DOE OSTI2024

Co-Hydrotreatment of Pyrolytic Lignin and Waste Cooking Oil to Produce Hydrocarbons

The co-hydrotreatment of pyrolysis bio-oils and vegetable oils could facilitate the integration of bioderived fuels into existing fuel infrastructures without significant modifications. Co-hydrotreatment studies with different PL/WCO blend ratios (0, 10, 20, 30, and 40 wt %) were conducted over the NiMo/γ-Al 2 O 3 catalyst. The coke formation value for WCO was 0.7 wt % and ranged between 1.5 and 2.5 wt % with the increase of pyrolytic lignin in the blend. The data suggest that coke is formed from both the sugar- and lignin-derived oligomers since the coke yield reported in this study is comparable with the coke yield obtained by coprocessing the bio-oil fraction without light oxygenate compounds, based on previous studies. All blends are recommended for co-hydrotreatment based on coke yield. The resulting organic phase was distilled into hydrocarbons at <150 °C, 150 to 250 °C, and 250 to 350 °C. The organic product’s overall hydrocarbon distribution was strongly dependent on the pyrolytic lignin concentration. The yield of the distillation cut with a boiling point range between 250 and 350 °C was higher in the hydrotreated samples, with more than 20% during distillation. Two-dimensional GC×GC was used to determine carbon distribution on the combustibles. FTIR and UV fluorescence spectra showed that most feedstocks were converted to potential combustibles for transportation applications after hydrotreatment. The distillation cuts properties, such as density, viscosity, and surface tension, were reported and compared to transportation fuel properties. Hydrocarbon yields reveal the potential of cohydrotreatment to upgrade pyrolytic lignin into valuable products. Furthermore, these findings provide useful insights into utilizing pyrolytic lignin to produce promising combustibles suitable for transportation applications.

co-hydrotreatment↗
DOE OSTI2025

Systematic softening in universal machine learning interatomic potentials

Machine learning interatomic potentials (MLIPs) have introduced a new paradigm for atomic simulations. Recent advancements have led to universal MLIPs (uMLIPs) that are pre-trained on diverse datasets, providing opportunities for universal force fields and foundational machine learning models. However, their performance in extrapolating to out-of-distribution complex atomic environments remains unclear. In this study, we highlight a consistent potential energy surface (PES) softening effect in three uMLIPs: M3GNet, CHGNet, and MACE-MP-0, which is characterized by energy and force underprediction in atomic-modeling benchmarks including surfaces, defects, solid-solution energetics, ion migration barriers, phonon vibration modes, and general high-energy states. The PES softening behavior originates primarily from the systematically underpredicted PES curvature, which derives from the biased sampling of near-equilibrium atomic arrangements in uMLIP pre-training datasets. Our findings suggest that a considerable fraction of uMLIP errors are highly systematic, and can therefore be efficiently corrected. We argue for the importance of a comprehensive materials dataset with improved PES sampling for next-generation foundational MLIPs.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Anti-fungal bioactive terpenoids in the bioenergy crop switchgrass (Panicum virgatum) may contribute to ecotype-specific microbiome composition

Abstract Plant derived bioactive small molecules have attracted attention of scientists across fundamental and applied scientific disciplines. We seek to understand the influence of these phytochemicals on rhizosphere and root-associated fungi. We hypothesize that – consistent with accumulating evidence that switchgrass genotype impacts microbiome assembly – differential terpenoid accumulation contributes to switchgrass ecotype-specific microbiome composition. An initial in vitro Petri plate-based disc diffusion screen of 18 switchgrass root derived fungal isolates revealed differential responses to upland- and lowland-isolated metabolites. To identify specific fungal growth-modulating metabolites, we tested fractions from root extracts on three ecologically important fungal isolates – Linnemania elongata, Trichoderma sp. and Fusarium sp. Saponins and diterpenoids were identified as the most prominent antifungal metabolites. Finally, analysis of liquid chromatography-purified terpenoids revealed fungal inhibition structure – activity relationships (SAR). Saponin antifungal activity was primarily determined by the number of sugar moieties – saponins glycosylated at a single core position were inhibitory whereas saponins glycosylated at two core positions were inactive. Saponin core hydroxylation and acetylation were also associated with reduced activity. Diterpenoid activity required the presence of an intact furan ring for strong fungal growth inhibition. These results inform future breeding and biotechnology strategies for crop protection with reduced pesticide application.

09 BIOMASS FUELS↗
DOE OSTI2022

Divergence of Velocity Fields in Electrochemical Systems

The passage of current through electrochemical systems results in the development of concentration gradients in the electrolytic phase that can be modeled using concentrated solution theory. Application of this theory requires knowledge of three concentration-dependent transport coefficients, which are often taken to be conductivity, diffusion coefficient, and the cation transference number with respect to the solvent velocity. The governing diffusion equation for molar concentration contains two additional terms - the thermodynamic factor which is related to activity of the electrolytic species and the solvent velocity. The main advance in this paper is the derivation of an expression for the divergence of the solvent velocity. Solving this equation requires knowledge of the partial molar volume of the electrolyte. Analogous expressions are derived for the mass average and molar average velocities. These velocities occur naturally in the diffusion equation if concentration is expressed as weight fraction and mole fraction of the electrolytic species, respectively.

diffusion↗
DOE OSTI2020

Two dimensional topology optimization of heat exchangers with the volume fraction method

We perform a comparison study of two topology optimizations methods applied to the design of a two fluids heat exchanger modeled with a coupled thermal-flow problem. The flow follows an isothermal and incompressible Stokes-Brinkman equation and the heat transfer is governed by a convection-diffusion equation without internal generation and high Peclet number. To keep the two fluid phases separated, we solve two Stokes-Brinkman equations, where the Brinkman term models the other phase as a solid. These two velocity fields are then fed to the heat transfer equation. Our goal is to maximize the enthalpy at the cold outlet while constraining the pressure drop. We first solve the design modeling the solid and fluid phases with a volume fraction variable. A SIMP-like penalization in the Brinkman term drives the optimization to a discrete design. The cost and constraint function derivatives are calculated with the library pyadjoint and the optimization is performed by IPOPT. We present optimized designs in two dimensions and discuss the influence of the parameters.

Beck, VictorA.↗
DOE OSTI2022

Mass Transport Limitations and Kinetic Consequences of Corn Stover Deacetylation

Alkaline pretreatment of herbaceous feedstocks such as corn stover prior to mechanical refining and enzymatic saccharification improves downstream sugar yields by removing acetyl moieties from hemicellulose. However, the relationship between transport phenomena and deacetylation kinetics is virtually unknown for such feedstocks and this pretreatment process. Here, we report the development of an experimentally validated reaction–diffusion model for the deacetylation of corn stover. A tissue-specific transport model is used to estimate transport-independent kinetic rate constants for the reactive extraction of acetate, hemicellulose and lignin from corn stover under representative alkaline conditions (5–7 g L -1 NaOH, 10 wt% solids loadings) and at low to mild temperatures (4–70°C) selected to attenuate individual component extraction rates under differential kinetic regimes. The underlying transport model is based on microstructural characteristics of corn stover derived from statistically meaningful geometric particle and pore measurements. These physical descriptors are incorporated into distinct particle models of the three major anatomical fractions (cobs, husks and stalks) alongside an unsorted, aggregate corn stover particle, capturing average Feret lengths of 917–1239 μm and length-to-width aspect ratios of 1.8–2.9 for this highly heterogeneous feedstock. Individual reaction–diffusion models and their resulting particle model ensembles are used to validate and predict anatomically-specific and bulk feedstock performance under kinetic-controlled vs. diffusion-controlled regimes. In general, deacetylation kinetics and mass transfer processes are predicted to compete on similar time and length scales, emphasizing the significance of intraparticle transport phenomena. Critically, we predict that typical corn stover particles as small as ~2.3 mm in length are entirely diffusion-limited for acetate extraction, with experimental effectiveness factors calculated to be 0.50 for such processes. Debilitatingly low effectiveness factors of 0.021–0.054 are uncovered for cobs—implying that intraparticle mass transfer resistances may impair observable kinetic measurements of this anatomical fraction by up to 98%. These first-reported quantitative maps of reaction vs. diffusion control link fundamental insights into corn stover anatomy, biopolymer composition, practical size reduction thresholds and their kinetic consequences. These results offer a guidepost for industrial deacetylation reactor design, scale-up and feedstock selection, further establishing deacetylation as a viable biorefinery pretreatment for the conversion of lignocellulosics into value-added fuels and chemicals.

09 BIOMASS FUELS↗
DOE OSTI2023

Climate Change Will Increase Biomass Proportion of Global Forest Carbon Stocks Under an SSP5–8.5 Climate Trajectory

Abstract A large amount of carbon is stored in global forests. However, the fraction of carbon stored as plant biomass versus soil organic carbon (SOC) varies among forest types, and potential changes over the 21st century are uncertain. Here, we used extensive data derived from inventories and remote sensing and Coupled Model Intercomparison Project Phase 6 (CMIP6) models to examine the current and 21st century dynamics in the proportion of biomass and SOC across global forests. We found that precipitation, elevation, soil, and wildfire were the primary controls of these differences in carbon pools. Under the SSP5–8.5 climate scenario, CMIP6 models project that the ratio of biomass to ecosystem carbon in global forests will increase across the 21st century, with the largest increases in boreal forests (95 ± 37%) compared to moist tropical forests (16 ± 15%). Changes in forest carbon pools resulting in greater biomass fraction will affect disturbance, and ecosystem carbon and energy balances, all of which interact with the climate system.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Microstructure Validation of Graph Theory Model-Derived Cooling Rates in the Wire Arc Additive Manufacturing of ER70S-6 Steel

Wire arc additive manufacturing (WAAM) enables high-rate fabrication of large metallic components, but spatial variations in thermal history can lead to microstructural heterogeneity that requires efficient process models to evaluate. This study evaluates whether cooling rates extracted from a graph theory model (GTM)-based thermal simulation are consistent with the microstructural evolution observed in an ER70S-6 WAAM wall. Thermal histories from the model were analyzed at selected build heights, and cooling rates were extracted from the final thermal excursion through the austenite phase field. Microstructures at corresponding locations were characterized using electron backscatter diffraction (EBSD) to quantify grain size distributions, and pearlite interlamellar spacing was used as an additional indicator of cooling behavior. The modeled cooling rates were highest near the substrate and generally decreased with build height, consistent with the observed reduction in the fine grain fraction and the progressive shift in the grain size distribution as build height increased. Pearlite spacing trends also supported the modeled cooling rate variation. These results indicate that GTM-derived thermal histories can be post-processed into metallurgically meaningful cooling rate estimates for WAAM steel builds and linked to dataset specific empirical grain size distribution relationships for process–thermal history–microstructure assessment.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Post-Wildfire Mobilization of Organic Carbon

Wildfires significantly alter watershed functions, particularly the mobilization of organic carbon (OC). This study investigated OC mobility and the physicochemical characteristics of wildfire-impacted soils and ashes from the northern California and Nevada fires (Dixie, Beckworth, Caldor). Organic carbon in wildfire-derived ashes (9.2–57.3 mg/g) generally exceeded levels in the background soils (4.3–24.4 mg/g), except at the Dixie fire sites. The mobile OC fraction varied from 0.0093 to 0.029 in ashes and 0.010 to 0.065 in soils, though no consistent trend was observed between the ashes and soils. Notably, the ash samples displayed lower OC mobility compared with the soils beneath them. A negative correlation was found between the mobile OC fraction and bulk OC content. Wildfire increased the total amount of mobile OC substantially by 5.2–574% compared to the background soils. Electron paramagnetic resonance (EPR) spectra confirmed the presence of environmentally persistent free radicals (EPFRs), which correlated with observed redox reactivity. Additionally, X-ray absorption near edge structure (XANES) and X-ray fluorescence (XRF) imaging revealed that Fe(II) oxidation in soils beneath the ashes may have enhanced the OC mobility, likely driven by pyrogenic carbon and free radicals. These findings enhance our understanding of post-wildfire OC mobilization and the impact of ash–soil physicochemical properties on watershed health.

organic carbon↗
DOE OSTI2021

The Chemical Inventory of the Planet-hosting Disk PDS 70

As host to two accreting planets, PDS 70 provides a unique opportunity to probe the chemical complexity of atmosphere-forming material. We present ALMA Band 6 observations of the PDS 70 disk and report the first chemical inventory of the system. With a spatial resolution of 0."4–0."5 (~50 au), 12 species are detected, including CO isotopologs and formaldehyde, small hydrocarbons, HCN and HCO{sup +} isotopologs, and S-bearing molecules. SO and CH{sub 3}OH are not detected. All lines show a large cavity at the center of the disk, indicative of the deep gap carved by the massive planets. The radial profiles of the line emission are compared to the (sub)millimeter continuum and infrared scattered light intensity profiles. Different molecular transitions peak at different radii, revealing the complex interplay between density, temperature, and chemistry in setting molecular abundances. Column densities and optical depth profiles are derived for all detected molecules, and upper limits obtained for the nondetections. Excitation temperature is obtained for H{sub 2}CO. Deuteration and nitrogen fractionation profiles from the hydrocyanide lines show radially increasing fractionation levels. Comparison of the disk chemical inventory to grids of chemical models from the literature strongly suggests a disk molecular layer hosting a carbon-to-oxygen ratio C/O > 1, thus providing for the first time compelling evidence of planets actively accreting high C/O ratio gas at present time.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2022

SDSS-IV MaNGA: Cannibalism Caught in the Act—On the Frequency of Occurrence of Multiple Cores in Brightest Cluster Galaxies

Although it is generally accepted that massive galaxies form in a two-phased fashion, beginning with a rapid mass buildup through intense starburst activities followed by primarily dry mergers that mainly deposit stellar mass at outskirts, the late time stellar mass growth of brightest cluster galaxies (BCGs), the most massive galaxies in the universe, is still not well understood. Several independent measurements have indicated a slower mass growth rate than predictions from theoretical models. We attempt to resolve the discrepancy by measuring the frequency of BCGs with multiple cores, which serve as a proxy of the merger rates in the central region and facilitate a more direct comparison with theoretical predictions. Using 79 BCGs at z = 0.06–0.15 with integral field spectroscopic data from the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) project, we obtain a multiple-core fraction of 0.11 ± 0.04 at z ≈ 0.1 within an 18 kpc radius from the center, which is comparable to the value of 0.08 ± 0.04 derived from mock observations of 218 simulated BCGs from the cosmological hydrodynamical simulation IllustrisTNG. We find that most cores that appear close to the BCGs from imaging data turn out to be physically associated systems. Anchoring on the similarity in the multiple-core frequency between the MaNGA and IllustrisTNG, we discuss the mass growth rate of BCGs over the past 4.5 Gyr.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

CLEAR: The Ionization and Chemical-enrichment Properties of Galaxies at 1.1 < z < 2.3

We use deep spectroscopy from the Hubble Space Telescope Wide-Field-Camera 3 IR grisms combined with broadband photometry to study the stellar populations, gas ionization and chemical abundances in star-forming galaxies at z ~ 1.1–2.3. The data stem from the CANDELS Lyα Emission At Reionization (CLEAR) survey. At these redshifts, the grism spectroscopy measure the [O II] λλ3727, 3729, [O III]λλ4959, 5008, and Hβ strong emission features, which constrain the ionization parameter and oxygen abundance of the nebular gas. We compare the line-flux measurements to predictions from updated photoionization models (MAPPINGS V; Kewley et al.), which include an updated treatment of nebular gas pressure, $\mathrm{log}P/k={n}_{e}{T}_{e}$. Compared to low-redshift samples (z ~ 0.2) at fixed stellar mass, $\mathrm{log}{M}_{* }/{M}_{\odot }\,=$ 9.4–9.8, the CLEAR galaxies at z = 1.35 (1.90) have lower gas-phase metallicity, ${\rm{\Delta }}(\mathrm{log}Z)$ = 0.25 (0.35) dex, and higher ionization parameters, ${\rm{\Delta }}(\mathrm{log}q)$ = 0.25 (0.35) dex, where U ≡ q/c. We provide updated analytic calibrations between the [O III], [O II], and Hβ emission-line ratios, metallicity, and ionization parameter. The CLEAR galaxies show that at fixed stellar mass, the gas ionization parameter is correlated with the galaxy specific star formation rates, where ${\rm{\Delta }}\mathrm{log}q\simeq 0.4\times {\rm{\Delta }}(\mathrm{log}\,\mathrm{sSFR})$, derived from changes in the strength of galaxy Hβ equivalent width. We interpret this as a consequence of higher gas densities, lower gas covering fractions, combined with a higher escape fraction of H-ionizing photons. We discuss both tests to confirm these assertions and implications this has for future observations of galaxies at higher redshifts.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Drop-in sustainable aviation fuels enabled by feedstock-agnostic lignin deoxygenation

Current sustainable aviation fuels (SAFs) require blending with petroleum-derived fuels due to incomplete hydrocarbon distributions, most notably a lack of aromatics. Lignin, the most abundant renewable source of aromatics, is a promising feedstock for addressing this limitation. Here, we demonstrate a sequential reductive catalytic fractionation and continuous hydrodeoxygenation process that converts multiple woody feedstocks into aromatic hydrocarbons at up to 93% of the theoretical carbon yield. Blending these products with commercial SAFs produces drop-in compatible fuels with elastomer swell performances equivalent to conventional aviation fuels. The process is adaptable across multiple biomass sources, yielding aromatic hydrocarbons with consistent enthalpic efficiencies and fuel properties. These findings establish a scalable route to 100% drop-in SAFs, leveraging lignin-derived aromatics within the existing biofuels infrastructure.

09 BIOMASS FUELS↗
DOE OSTI2025

Senescence-driven solubilization of biomass is the main source of kelp-derived dissolved organic carbon to the coastal ocean

Abstract Kelp forests form some of the most productive areas on earth and are proposed to sequester carbon in the ocean, largely in the form of released dissolved organic carbon (DOC). Here we investigate the role of environmental, seasonal and age-related physiological gradients on the partitioning of net primary production (NPP) into DOC by the canopy forming giant kelp (Macrocystis pyrifera). Rates of DOC production were strongly influenced by an age-related decline in physiological condition (i.e. senescence). During the mature stage of giant kelp development, DOC production was a small and constant fraction of NPP regardless of tissue nitrogen content or light intensity. When giant kelp entered its senescent phase, DOC production increased substantially and was uncoupled from NPP and light intensity. Compositional analysis of giant kelp-derived DOC showed that elevated DOC production during senescence was due to the solubilization of biomass carbon, rather than by direct exudation. We coupled our incubation and physiological experiments to a novel satellite-derived 20-year time series of giant kelp canopy biomass and physiology. Annual DOC production by giant kelp varied due to differences in standing biomass between years, but on average, 74% of the annual DOC production by giant kelp was due to senescence. This study suggests DOC may be a more important fate of macroalgal NPP than previously recognized.

Life Sciences & Biomedicine - Other Topics↗
DOE OSTI2023

Liquefaction of different corn stover fractions assisted by enzyme-biomass deconstruction

Recalcitrant properties of different sources of lignocellulosic biomass represent challenges in materials processing within a biorefinery, as well as enzyme efficient deconstruction to fermentable sugars. Limitations include lignin derived enzyme inhibitors, enzyme inhibition by hydrolysis products, and resistance to mixing due to rheological properties of lignocellulosic particulates at high solids loadings. Consequently, we examined conditions that might be used to achieve liquid slurries (i.e., liquefaction) at solids loadings of 300 g/L before the material enters the biorefinery through a pretreatment step. This work explores enzyme-assisted liquefaction in a fed-batch process using the commercial enzymes Celluclast 1.5L or Ctec-2 at 1FPU/g or 3 FPU/g of dry solids, basis. Corn stover pellets were fed into a 1 L stirred bioreactor containing enzyme solution over a 5-hour period until reaching 30% solids loading (dry wgt / vol basis). After 6, 24 and 96 hours from the start of the run, samples were taken and characterized with respect to their sugar composition, rheology, water absorption and enzyme activity. Slurries with dramatically reduced yield stresses were achieved for corn stover. Yield stresses of 178±7 Pa (3 FPU, Celluclast 1.5L) and 79±6 Pa (3FPU, Ctec-2) were measured for corn stover at 24 hours, compared to 6,000 Pa for samples without enzyme.Yield stress was 155± 29 Pa (3FPU, Ctec-2) and 257 ± 72 Pa (1 FPU, Celluclast 1.5L) for corn cobs at 24 hours.A profile based on 6, 24 and 96h of yield stress measurements and sugar conversion is presented. Enzyme activity is measured and the impact of liquefaction on an integrated processing in a biorefinery operation is discussed.

Gutierrez, Diana↗
DOE OSTI2024

Rhenium Isotope Reconnaissance of Uranium Ore Concentrates

Exploration of natural isotopic variations of the element rhenium (Re) is in its infancy, with initial studies revealing isotopic fractionation in a variety of geological materials. Here, in this work, we investigate Re isotope variation as a new geochemical tool, given its redox-sensitive properties and affinity for organic matter and sulfides. In this work, Re abundance and isotope ratio data were collected from uranium ore concentrates (UOCs) across a variety of depositional ages, locations, geologic settings, and deposit types. Ore types from which the UOC were derived include sandstone, unconformity, and quartz-pebble (QP) conglomerate. To isolate Re from the U-rich matrix of UOCs, a new purification method utilizing DGA ion exchange resin was developed. We found that UOCs exhibit a wide range of Re isotope ratios, with sandstone ore-derived UOCs having the isotopically lightest values, QP conglomerate ore-derived UOCs having the heaviest, and unconformity ore-derived UOCs in between (with some overlap with sandstone UOCs). The Re isotope ratio range observed in UOCs extends previously reported values by more than a factor of two. Industrial processing (e.g., incomplete recovery of Re from ore, contamination, fractionation during processing) may play a role in the isotopic variability in the UOCs. However, systematic differences between ore types suggest that the depositional setting is a significant factor. For nuclear forensic investigations, Re isotopic compositions combined with data from other isotopic systems provide geochemical signatures that can aid in provenance assessment of UOCs. Regardless of the specific causes for the wide range of Re isotope ratios in UOCs, these initial data indicate Re is a promising tool for nuclear forensic investigations on samples from early in the nuclear fuel cycle.

58 GEOSCIENCES↗
DOE OSTI2024

Predicting Partial Atomic Charges in Metal–Organic Frameworks: An Extension to Ionic MOFs

Molecular simulation is an invaluable tool to predict and understand the usage of metal–organic frameworks (MOFs) for gas storage and separation applications. Accurate partial atomic charges, commonly obtained from density functional theory (DFT) calculations, are often required to model the electrostatic interactions between the MOF and adsorbates, especially when the adsorbates have dipole or quadrupole moments, such as water and CO 2 . Machine learning (ML) models have been previously employed to predict partial charges and avoid the computational cost associated with DFT calculations. However, previous ML models suffer from small training data sets, which limit their scope of application. In this work, we introduce two novel machine learning models, PACMOF2-neutral and PACMOF2-ionic, aimed at predicting the density-derived electrostatic and chemical (DDEC6) partial atomic charges for both neutral and ionic MOFs. These models not only yield DFT-level accuracy at a fraction of the computational cost but also demonstrate a remarkable improvement in prediction of adsorption, as validated with grand canonical Monte Carlo simulations. Furthermore, the robustness and fast computational time of the PACMOF2 models, along with their transferability to other porous materials such as covalent organic frameworks and zeolites, underscores their potential in high-throughput screening of MOFs for diverse applications.

36 MATERIALS SCIENCE↗