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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Variations of Alloying Site Density in Pd 1 Cu Single‐Atom Alloy Catalysts Lead to Shifted Product Yields in Electrochemical CO Reduction

Single-atom alloy (SAA) catalysis research often reports that a SAA catalyst, in the general formulation of a single-atom metal M1 alloyed on the surface of the host metal M2, facilitates a probe reaction. However, for catalytic reactions that present decoupled rate- and selectivity-limiting steps, the alloying site density may significantly manipulate these independent steps, but it has rarely been explicitly examined for any SAA systems. Herein, using the electrocatalytic CO reduction as a probe reaction, we report that the nominal Pd 1 Cu cube SAA catalysts exhibit distinctive high reactivity toward ethylene or ethanol, respectively, depending on whether the Pd atoms are in dilute or crowded forms. Although the presence of single-atom Pd embedded on Cu uniformly promotes CHO* formation and C─C coupling, the dilute-Pd 1 Cu favors ethylene formation by enabling low-barrier C─O cleavage from a flat CH 2 CH 2 OH* intermediate, whereas the crowded-Pd 1 Cu promotes ethanol formation by stabilizing an upright hydrogenation transition state of the same intermediate. Furthermore, we present evidence that the catalytic chemistry of crowded Pd 1 species differs from that of the Pd 2 -dimer; the latter, albeit unstable, steers reaction selectivity to acetate instead. In conclusion, these results uncovered the underappreciated importance of controlling SAA catalytic chemistry from the perspective of single-atom site densities.

Jin, Zehua [Clemson University, SC (United States)↗
DOE OSTI2025

Contact with Electrically Conductive Inert Solids Alters Intrinsic Heterogeneous Brønsted Acid Catalysis

Interfacial electric fields at heterogeneous catalyst surfaces have been demonstrated to alter kinetics of liquid-phase reactions. In these systems, electric fields are generated from applying a potential to the catalyst through connection to a potentiostat or through electron transfer from redox-active species in solution. Here, we demonstrate that catalyst polarization can also occur by simply contacting electrically conductive inert solids, leading to the counterintuitive conclusion that a catalyst particle touching an inert solid can alter intrinsic reaction rates. Using dehydration of 1-methylcyclopentanol to 1-methylcyclopentene catalyzed by Brønsted-acidic carboxylic acid groups on carbon nanotubes as a proof-of-concept probe reaction, we show that catalyst contact with inert, thermally reduced carbon nanotubes leads to order-of-magnitude changes in reaction rate. Furthermore, we demonstrate that these contact-induced effects can also be observed under standard laboratory reaction conditions, where particle-to-particle contact in stirred catalyst powder suspensions is sufficient to demote rates by ∼8-fold. This work provides the foundation for a new method of reaction rate control, which could have implications whenever heterogeneous catalyst particles are in contact with inert materials for liquid-phase reactions in the presence of electrolyte.

Alcohols↗
DOE OSTI2021

Tandem In 2 O 3 -Pt/Al 2 O 3 catalyst for coupling of propane dehydrogenation to selective H 2 combustion

Tandem catalysis couples multiple reactions and promises to improve chemical processing, but precise spatiotemporal control over reactive intermediates remains elusive. We used atomic layer deposition to grow In 2 O 3 over Pt/Al 2 O 3 , and this nanostructure kinetically couples the domains through surface hydrogen atom transfer, resulting in propane dehydrogenation (PDH) to propylene by platinum, then selective hydrogen combustion by In 2 O 3 , without excessive hydrocarbon combustion. Other nanostructures, including platinum on In 2 O 3 or platinum mixed with In 2 O 3 , favor propane combustion because they cannot organize the reactions sequentially. The net effect is rapid and stable oxidative dehydrogenation of propane at high per-pass yields exceeding the PDH equilibrium. Tandem catalysis using this nanoscale overcoating geometry is validated as an opportunity for highly selective catalytic performance in a grand challenge reaction.

Yan, Huan↗
DOE OSTI2021

Light-driven hydrogen production with CdSe quantum dots and a cobalt glutathione catalyst

A photocatalytic hydrogen (H 2 ) production system is reported using glutathione (GSH)-capped CdSe QDs with a cobalt precatalyst, yielding 130 000 mol H 2 per mol cobalt over 48 hours. Analysis of the reaction mixtures after catalysis indicates that the active catalyst is a labile complex of cobalt and GSH formed in situ.

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

In situ spectroscopic insights into the redox and acid-base properties of ceria catalysts

Cerium oxide (ceria) plays an important and fascinating role in heterogeneous catalysis as illustrated by its versatile use as a catalyst, a catalyst support, or a promotor in various oxidation and reduction reactions. Central to these reactions is the rich defect chemistry, facile redox capability, and unusual acid-base properties of ceria. Understanding the unique redox and acid-base properties of ceria is essential to build the structure-catalysis relationship so that improved catalytic functions can be achieved for ceria-based materials. Among the characterization toolbox, spectroscopic approach indisputably stands out for its unparalleled power in offering chemical insights into the surface properties of ceria at atomic and molecular level. In this review, we summarize advances in revealing the redox and acid-base properties of ceria via a variety of spectroscopic methods including optical, X-ray, neutron, electronic and nuclear spectroscopy. Both direct spectroscopy characterization and its coupling with probe molecules are analyzed to illustrate how the nature, strength and density of different surface sites are influenced by the pretreatment, the morphology and size of ceria nanoparticles. Further directions in taking advantage of in situ/operando spectroscopy for better understanding the catalysis of ceria-based materials are detailed in the summary and outlook section.

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

BETO 2021 Peer Review - Overview of the Chemical Catalysis for Bioenergy Consortium

Catalysis plays a central role in converting biomass and carbon-rich waste feedstocks into fuels and chemicals; however, critical catalysis challenges exist that are limiting commercialization of emerging bioenergy technologies. By leveraging unique U.S. Department of Energy National Laboratory capabilities and expertise, the Chemical Catalysis for Bioenergy consortium seeks to overcome these catalysis challenges and accelerate the catalyst and process development cycle. The foundation of the consortium consists of an integrated and collaborative portfolio of catalytic technologies and enabling capabilities, which positions ChemCatBio to address both technology-specific and overarching catalysis challenges across the development cycle from discovery to scale-up. The core catalysis projects target technological advancements for specific conversion processes, such as catalytic upgrading of biochemical process intermediates, catalytic fast pyrolysis, C1 and C2 upgrading, and electrochemical CO2 reduction, while the enabling technologies provide access to world-class capabilities and expertise in computational modeling, materials synthesis, advanced in situ and in operando catalyst characterization, and catalyst design tools.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Multifunctional Catalysts for the Tandem Reactions of Oxygenates

Industrially-relevant catalytic reactions rarely consist of a simple sequence of elementary steps. Moreover, kinetic coupling of multiple reactions on a catalyst surface is highly desired for process intensification and improved energy efficiency for large scale chemical transformations. The shifting landscape of hydrocarbon chemical feedstocks in the US also motivates research on the selective conversion of more complex molecules. One desirable type of catalytic reaction is the reduction of carboxylic acids that are produced from biomass feedstocks to their corresponding alcohols. The proposed research explores the fundamental importance of hydrogen spillover on a multifunctional catalyst for carboxylic acid reduction with H 2 composed of metal particles coupled to metal oxide particles. Recent work has demonstrated the excellent performance of supported tungsten oxide clusters for carboxylic acid reduction, but only after they are promoted with a late transition metal such as palladium. Elucidating the active state of the catalyst and the associated reaction mechanism for acid reduction on that active state are the overall goals of the proposed project and successful completion will enable future design of efficient multifunctional catalysts. The critically important role of the metal promoter is hypothesized to be its ability to dissociate H2 and spillover atomic H to the support. Although hydrogen spillover is a well-recognized phenomenon in catalysis, its role in both catalyst activation and catalytic turnover are still unresolved. The study combined materials synthesis, characterization, reactivity testing, and molecular simulations, to explore the effect of hydrogen chemical potential on the formation of the active catalytic sites and on the steady state catalytic reduction of carboxylic acid. Varying the hydrogen chemical potential through modification of the gas conditions, support composition, and metal loading to modulated the structure and catalytic performance of the reducible metal oxide. Dual function catalysts containing supported Pd and WO x species co-located on a non-reducible carrier (silica) and a reducible carrier (titania) were synthesized and characterized by electron microscopy, temperature-programmed reduction, and chemisorption. Spectroscopic methods such as X-ray absorption and UV-vis were also used to evaluate the catalysts, which were used in the reduction of propionic acid to aldehyde and alcohol. Quantum chemical calculations, including ab initio phase diagrams provided molecular insights into the H spillover phenomenon.

09 BIOMASS FUELS↗
DOE OSTI2022

Dilute Alloys Based on Au, Ag, or Cu for Efficient Catalysis: From Synthesis to Active Sites

The development of new catalyst materials for energy-efficient chemical synthesis is critical as over 80% of industrial processes rely on catalysts, with many of the most energy-intensive processes specifically using heterogeneous catalysis. Catalytic performance is a complex interplay of phenomena involving temperature, pressure, gas composition, surface composition and structure over multiple length and time scales. In response to this complexity, the integrated approach to heterogeneous dilute-alloy catalysis reviewed here brings together materials synthesis, mechanistic surface chemistry, reaction kinetics, in-situ and operando characterization, and theoretical calculations in a coordinated effort to develop design principles to predict and improve catalytic selectivity. Dilute alloy catalysts—in which isolated atoms or small ensembles of the minority metal on the host metal lead to enhanced reactivity while retaining selectivity—are particularly promising as selective catalysts. Several dilute alloy materials using Au, Ag and Cu as the majority host element, including more recently introduced support-free nanoporous metals and oxide-supported nanoparticle "raspberry colloid templated (RCT)" materials, are reviewed for selective oxidation and hydrogenation reactions. Progress in understanding how such dilute alloy catalysts can be used to enhance selectivity of key synthetic reactions is reviewed, including quantitative scaling from model studies to catalytic conditions. The dynamic evolution of catalyst structure and composition studied in surface science and catalytic conditions and their relationship to catalytic function are also discussed, followed by advanced characterization and theoretical modeling that have been developed to determine the distribution of minority metal atoms at or near the surface. Furthermore, the integrated approach demonstrates the success of bridging the divide between fundamental knowledge and design of catalytic processes in complex catalytic systems, which can accelerate the development of new and efficient catalytic processes.

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

BETO 2021 Peer Review - Advanced Catalyst Synthesis and Characterization (ACSC) Project WBS 2.5.4.304; 303; 305

The Advanced Catalyst Synthesis and Characterization (ACSC) project, in close collaboration with the Chemical Catalysis for Bioenergy (ChemCatBio) Consortium enabling projects, CatCost, and the Engineering of Catalyst Scale-Up project, (1) provides fundamental insight into working catalysts leading to actionable recommendations for all of the ChemCatBio catalysis projects, (2) addresses overarching catalysis challenges central to the ChemCatBio Consortium, and (3) adapts and applies new synthesis methodologies and in situ/operando characterization capabilities to meet the evolving needs of the catalysis projects. The outcome is a transition from empirical catalyst development to rational design through the prediction of materials with targeted properties based on advanced characterization combined with computational modeling, and the synthesis of next generation catalysts with predicted structures that yield demonstrated improvements in catalytic performance. In FY18, the ACSC helped to demonstrate the utility of the complete catalyst and process development cycle for dimethyl ether to high-octane gasoline over metal-modified zeolite catalysts for the Upgrading of C1 Building Blocks project, and in FY21 will leverage capabilities, expertise, and computational models established for this effort to target next-generation catalysts for ethanol to distillates for the Upgrading of C2 Intermediates project with enhanced performance in half the time.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

A measure of active interfaces in supported catalysts for high-temperature reactions

Formulating knowledge of structure-function relationships in heterogeneous catalysis is central to the design of efficient catalysts; yet, the elucidation of dominant reaction sites has remained as a challenge. Here, in this paper, we present a methodology that can be used to visualize metal-gas and metal-oxide-gas interfaces in three dimensions and to quantify their catalytic activity levels. As a case study, CH 4 oxidation occurring in a Pt/CeO 2 system is chosen. By employing thermally robust Pt@CeO 2 model catalysts with size-tunable and monodisperse cores, and gas-permeable shells, we reconstruct a series of structures in 3D via electron tomography and match the information to activity data and theoretical calculations. This strategy reveals that two different interfaces catalyze the CH 4 oxidation and that their contribution to the overall rate changes with the Pt size, temperature, and gas atmosphere. Our results provide an analytic platform on which to explore reaction pathways and mechanisms applicable to multiple reactions and materials.

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

Hydrogen Dissociation Controls 1-Hexyne Selective Hydrogenation on Dilute Pd-in-Au Catalysts

Increasing the selectivity of the catalytic hydrogenation of alkynes to alkenes is of major importance for the processing of petrochemicals and the production of fine chemicals. Achieving high selectivity for alkene formation at high conversions, however, remains a long-standing challenge in heterogeneous catalysis. Here, the mechanism and origin of the high selectivity of dilute Pd-in-Au catalysts has been studied by a combination of first-principles calculations, microkinetic simulations, and isotopic exchange hydrogenation experiments using Pd 0.04 Au 0.96 nanoparticles embedded in raspberry colloid-templated silica. The Pd is predominantly in the form of isolated atoms, only surrounded by Au atoms, based on prior studies. The simulations indicate that the rate-limiting process for 1-hexyne hydrogenation on Pd monomers in Au(111) is H 2 dissociation, which has a large free energy barrier of 0.86 eV at 363 K and 0.2 bar of H 2 . The C–H bond formation steps, on the other hand, proceed with lower barriers, which contrasts with previous studies of extended Pd catalysts. The microkinetic simulations identify the sizable H 2 dissociation barrier and the small barrier for the hydrogenation of 1-hexyne as key factors that lead to a high selectivity for the production of 1-hexene from 1-hexyne, even at high conversion. The unconventional H 2 dissociation limiting process in combination with the low coverage of weakly bound hydrocarbon intermediates explains the near-zero order of 1-hexyne found experimentally. Furthermore, the partial hydrogenation of 1-hexyne to form 1-hexene is shown to be an irreversible process from our isotopic exchange hydrogenation experiments and is explained by the strongly exothermic nature of the reaction. Diluting active species, like Pd, in a less active host metal, like Au, hence appears promising as a means of tuning the binding energy of reactants and altering reaction profiles, leading to distinct kinetic behavior for an optimal catalytic activity and selectivity. Furthermore, the combination of microkinetic modeling, density functional theory calculations, and isotopic exchange experiments is thus demonstrated to be an effective approach to modeling important catalytic phenomena.

1-hexyne↗
DOE OSTI2022

Can Li: A Career in Catalysis

Prof. Can Li is distinguished for his seminal achievements in fundamental and applied researches in catalysis, especially in advancing the characterization of catalysts and catalytic reactions related to solar energy conversion and fine chemical synthesis. The scope of his research is wide ranging, and he is leading some of the fields. This account highlights his major scientific achievements in advancing the fundamental sciences of characterizing catalysts and understanding the mechanisms of catalytic reactions by developing in situ spectroscopic techniques (e.g., infrared (IR), ultraviolet (UV), Raman, space- and time-resolved spectroscopies and photoelectric imaging), and applying the obtained knowledge in the rational design and synthesis of catalysts for practical applications in photocatalytic, electrocatalytic and photoelectrocatalytic water splitting and CO 2 reduction, chiral synthesis in nanoreactors and ultradeep desulfurization of fuels, and fine chemical synthesis. In particular, his demonstration of the kiloton scale solar fuel production project will be introduced as a successful model for converting fundamental science into practical and impactful applications to convert CO 2 and water into solar fuels. This account also highlights his services to the catalysis communities and international collaborations for promoting catalysis science worldwide.

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

One-Step Synthesis and Operando Electrochemical Impedance Spectroscopic Characterization of Heterostructured MoP–Mo 2 N Electrocatalysts for Stable Hydrogen Evolution Reaction

Here this study presents a novel synthesis of self-standing MoP and Mo 2 N heterostructured electrocatalysts with enhanced stability and catalytic performance. Facilitated by the controlled phase and interfacial microstructure, the seamless structures of these catalysts minimize internal resistivity and prevent local corrosion, contributing to increased stability. The chemical synthesis proceeds with etching step to activate the surface, followed by phosphor-nitriding in a chemical vapor deposition chamber to produce MoP-Mo 2 N@Mo heterostructured electrocatalysts. X-ray diffraction analyses confirm the presence of MoP, Mo 2 N, and Mo phases in the electrocatalyst. Morphology studies using scanning electron microscopy characterizes the hierarchical growth of structures, indicating successful formation of the heterostructure. X-ray photoelectron spectroscopy (XPS) analyses of the as-synthesized and post-catalytic activity samples reveal the chemical shift in terms of binding energy (BE) of Mo 3d XPS peak, especially after catalytic activity. The XPS BE shifts attributed to changes in oxidation state, electron transfer, and surface reconstruction during catalysis. Electrochemical evaluation of the catalysts demonstrates the superior performance of the MoP-Mo 2 N@Mo heterostructured catalyst in hydrogen evolution reactions (HER), with lower overpotentials and enhanced Tafel slopes. Stability tests reveal changes in double layer capacitance over time, suggesting surface reconstruction and increased active surface area during catalysis. Operando electrochemical impedance spectroscopy (EIS) further elucidates the dynamic changes in resistance and charge transfer during HER. Overall, comprehensive understanding of the synthesis, characterization, and electrochemical behavior of the developed MoP-Mo 2 N@Mo heterostructured electrocatalyst as presented in this paper highlights their potential utilization in sustainable energy applications.

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

Advancing Insights into Electrochemical Pre‐Treatments of Supported Nanoparticle Electrocatalysts by Combining a Design of Experiments Strategy with In Situ Characterization

Activation, break-in, and/or pre-treatment protocols are generally applied to energy conversion devices before regular operation to reach stable performance. There remains much to understand about the relationships among physical properties, performance, and electrochemical pre-treatments. Here, a design-of-experiments (DoE) strategy is employed to address this gap by demonstrating the influence of five pre-treatment parameters for carbon-supported Pt-nanoparticle catalysts on the electrocatalytic oxygen reduction reaction (ORR). A subset of pre-treatments, developed using a central composite design, are tested in a flow cell combined with an inductively-coupled plasma mass spectrometer (on-line ICP-MS). The DoE-based approach facilitates comprehensive insights from two orders of magnitude fewer experiments than a conventional grid search. The coupled on-line ICP-MS setup enables effective catalysis and real-time catalyst dissolution data. Leveraging insights from DoE for on-line ICP-MS and additional characterization, a model is built between the degradation of a multi-dimensional supported Pt surface, its performance, and applied electrochemical parameters. These investigations identify surface modifications, such as oxidation, and subsequent restructuring of Pt during pre-treatment as a primary cause of performance deterioration during ORR. By combining DoE with advanced characterization techniques, a powerful approach is demonstrated to gain a mechanistic understanding of pre-treatment protocols that can be broadly adapted to various reaction chemistries.

Platinum↗
DOE OSTI2023

Writing the Programs of Programmable Catalysis

It has long been known that non-steady state and periodic catalytic reactor operation in terms of temperature, pressure, and composition can lead to higher overall productivity and/or product selectivity than the best steady operation. Recently, the emergence of catalysts whose intrinsic properties can be made to oscillate with time, introduces advanced forcing capabilities that can be “programmed” into the catalysts to broaden the scope and applicability of periodic operation to surface chemistry. In this work, an algorithmic approach is implemented to significantly accelerate the discovery and optimization of periodic steady states of catalytic reactors. Decomposition of complex dynamics into fundamental mechanistic fast–slow steps is seen to improve conceptual understanding of the relationship between binding energy oscillation protocols and overall catalytic rates. Finding structured forcing protocols, optimally tailored to the multiple time scales of a given individual mechanism, requires an efficient search of high-dimensional parameter spaces. Here, this is enabled here through active learning (Bayesian optimization, enhanced by our proposed Bayesian continuation). Implementation of these methods is shown to accelerate the evaluation of catalyst programs by up to several orders of magnitude. Faster screening of programmable catalysts to discover periodic steady states enables the optimization of catalytic operating protocols and thus opens the possibility for catalyst engineering based on optimal forcing programs to control rate and product selectivity, even for complex multistep catalytic mechanisms.

catalysis↗
DOE OSTI2024

CO oxidation over a ligand coordinated single site Rh catalyst: identification of the active complex

Single atom catalysis has evolved as a promising strategy to enhance atom utilization efficiency, lower reaction temperatures, and control reaction pathways in heterogeneous catalytic reactions. An important challenge using supported single atom catalysts is the stability of metal single atoms during reactions. Here, we present an approach to stabilize single rhodium atoms on a titania support via a metal–ligand coordination strategy. We explore the reaction activity and mechanism of CO oxidation, as well as the stability under oxidative reaction conditions. Kinetic studies suggest that, with an excess of oxygen in the feed gas, oxygen activation is more facile on defective titania surfaces than on pristine titania surfaces. In situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) analysis shows that on the pristine titania surface, the 1,10-phenanthroline-5,6-dione (PDO) coordinated Rh catalyst (Rh–PDO/TiO 2 ) catalyzes CO oxidation via the formation of carbonate-like species, which is similar to what occurs on Rh nanoparticle catalysts. However, on the defective titania surface, no carbonate species form for Rh–PDO/def-TiO 2 . The supported Rh–ligand catalysts are also shown to be very stable in such a reaction environment at elevated temperatures, potentially allowing for wide applications.

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

HydraGNN_Predictive_GFM_2026 - Ensemble of predictive graph foundation models for atomistic materials modeling

This release contains data and parameters of HydraGNN-based graph foundation models trained as a result of the work published in the pre-print "Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data" by M. Lupo Pasini et al. (https://arxiv.org/abs/2604.15380). We jointly train on 16 open first-principles datasets (544+ million structures covering 85+ elements) using a multi-task architecture with per-dataset heads and a scalable ADIOS2/DDStore data pipeline. On Frontier, we execute six large-scale DeepHyper hyperparameter optimization campaigns in FP64 and promote the top-performing message-passing models to sustained 2,048-node training, yielding a PaiNN-based lead model. The version of HydraGNN used to generate the outputs provided in this release is HydraGNN v5.0 (https://github.com/ORNL/HydraGNN/releases/tag/v5.0) The list of datasets used for the training of the graph foundation model is the following: 1) Alexandria [1] 2) ANI1x [2] 3) MPTrj [3] 4) Open Catalyst 2020 (OC20) [4] 5) Open Catalyst 2022 (OC22) [5] 6) Open Catalyst 2025 (OC25) [6] 7) Open Direct ir Capture 2023 (ODAC23) [7] 8) Open Materials 2024 (OMat24) [8] 9) Open Molecules 2025 (OMol25) [9] 10) OMol25-neutral (subset of OMol25 that contains only molecules with zero total charge) 11) OMol25-non-neutral (subset of OMol25 that contains only molecules with non-zero total charge) 12) Open Polymers 2026 (OPoly2026) [10] 13) Nabla2DFT [11] 14) QCML [12] 15) QM7X [reference 13] 16) transition1x [14] Dataset references: [1] J. Schmidt et al., “A dataset of 175k stable and metastable materials calculated with the PBEsol and SCAN functionals,” Scientific Data, vol. 9, p. 64, 2022. [2] J. S. Smith et al., “The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules,” Scientific Data, vol. 7, p. 134, 2020. [Online]. Available: https: //www.nature.com/articles/s41597-020-0473-z [3] A. Jain et al., “Commentary: The Materials Project: A materials genome approach to accelerating materials innovation,” APL Materials, vol. 1, no. 1, p. 011002, 07 2013. [Online]. Available: https://doi.org/10.1063/1.4812323 [4] L. Chanussot et al., “Open catalyst 2020 (oc20) dataset and community challenges,” ACS Catalysis, vol. 11, no. 10, pp. 6059–6072, 2021. [Online]. Available: https://doi.org/10.1021/acscatal.0c04525 [5] K. Tran et al., “Open catalyst 2022 (oc22) dataset and challenges for oxidation electrocatalysts,” ACS Catalysis, vol. 13, no. 5, pp. 3066–3084, 2023. [Online]. Available: https://doi.org/10.1021/acscatal.2c05426 [6] S. J. Sahoo et al., “The open catalyst 2025 (oc25) dataset and models for solid-liquid interfaces,” arXiv preprint arXiv:2509.17862, 2025. [Online]. Available: https://arxiv.org/abs/2509.17862 [7] A. Sriram et al., “The open DAC 2023 dataset and challenges for sorbent discovery in direct air capture,” ACS Central Science, vol. 10, no. 5, pp. 923–941, 2024. [8] L. Barroso-Luque et al., “Open materials 2024 (omat24) inorganic materials dataset and models,” 2024. [Online]. Available: https://arxiv.org/abs/2410.12771 [9] D. S. Levine et al., “The open molecules 2025 (OMol25) dataset, evaluations, and models,” 2025. [Online]. Available: https://arxiv.org/abs/2505.08762 [10] D. S. Levine et al., The open polymers 2026 (OPoly26) dataset and evaluations,” arXiv preprint arXiv:2512.23117, 2025. [Online]. Available: https://arxiv.org/abs/2512.23117 [11] K. Khrabrov et al., “Nabla2dft: A universal quantum chemistry dataset of drug-like molecules and a benchmark for neural network potentials,” in NeurIPS 2024 Datasets and Benchmarks Track, 2024. [Online]. Available: https://openreview.net/forum?id=ElUrNM9U8c [12] S. Ganscha et al., “The QCML dataset, quantum chemistry reference data from 33.5M DFT and 14.7B semi-empirical calculations,” Scientific Data, vol. 12, p. 406, 2025. [13] J. Hoja et al., “QM7-X, a comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules,” Scientific Data, vol. 8, p. 43, 2021. [Online]. Available: https://www.nature.com/articles/s41597-021-00812-2 [14] M. Schreiner et al., “Transition1x - a dataset for building generalizable reactive machine learning potentials,” Scientific Data, vol. 9, p. 779, 2022. The folder "datasets_ADIOS2_format" contains the set of pre-processed datasets in Adaptable I/O System (ADIOS) format (https://www.exascaleproject.org/research-project/adios/) that have been used for the development and training of GFMs in this work. The "datasets_ADIOS2_format" directory contains 2 sub-directories, one for the version "v1" of the datasets and one for the version "v2" of the datasets. The version "v1" of the datasets provides values of the total energy as they are extracted from the original data as it was released by the respective institutions. The version "v2" of the datasets provides values of the energy that have been realigned. The realignment was performed by training a linear regression model that predicts the total energy as a function of the chemical composition of the atomistic structure, and then subtract such prediction from the original value of the total energy. Both folders "v1" and "v2" contain 16 sub-directories, each corresponding to an ADIOS2-formatted dataset The folder "DeepHyper-results" contains the configurational files and model's parameters for all the 186 HPO trials that were successfully completed by the scalable hyperparameter optimization (HPO) runs on Frontier. The content of the folder "DeepHyper-results" I structured as follows: 1) task-list.txt: list of mpnn name, jobid, and deephyper task id 2) gfm_${MPNN}_${JOBID}_0.${TASKID}: run directory with checkpoint files 3) gfm_${MPNN}: deephyper summary directory (*.csv) for each specific MPNN type 4) deephyper-experiment-${JOBID}: output and error logs for each job The file "deephyper-sorted.csv" contains the details of each HydraGNN model built and tested by HPO, obtained by merging the (*.csv) filed from each HPO run executed. Out of all the HPO trials, we selected 10 to continue the training of the respective HydraGNN models. Due to limited computational budget available in the LRN070 allocation we could not complete the training till convergence for all these 10 selected models. The folder "models" contains multiple sub-folders, one per each HydraGNN model trained. Each model sub-folder contains the parameters of each HydraGNN model, with multiple checkpoint-restarts. The list of sub-folders are as follows: 1) multidataset_hpo-BEST1-fp64 2) multidataset_hpo-BEST2-fp64 3) multidataset_hpo-BEST3-fp64 4) multidataset_hpo-BEST4-fp64 5) multidataset_hpo-BEST5-fp64 6) multidataset_hpo-BEST6-fp64 7) multidataset_hpo-BEST7-fp64 8) multidataset_hpo-BEST8-fp64 9) multidataset_hpo-BEST9-fp64 10) multidataset_hpo-BEST10-fp64 Within each one of these folders, additional auxiliary log files are provided with descriptions about how the training proceeded. The lead PaiNN-model is contained inside "multidataset_hpo-BEST6-fp64". The file "mlp_branch_weights" contains the parameters of the multi-layer perceptron (MLP) used to reconcile the predictions of the 16 output decoding heads of the HydragNN architectures. The MLP takes in input the chemical composition of the atomistic structure and predicts averaging weights to linearly mix the predictions of each output decoding head toward consolidating them into a single one. The folder "1.1billion-structure-inference" contains 1.1 billion atomistic structures randomly generated. Each structures is associated with energy and forces predicted with the lead-PaiNN model combined with the MLP model for reconciliation of the multi-branch predictions generated by the 16 output decoding heads. The folder "1.1billion-structure-inference" contains 9,300 (*.tar.gz) subdirectories, one per Frontier compute node used to execute the inference at exascale. Once uncompressed, each (*.tar.gz) subdirectory contains an ADIOS2 (*.bp) file container, where each atomistic structure is stored as a PyTorch-Geometric Data object. The file "export_dataset_environment_variables.sh" contains the environment variables that need to be set before running the HydraGNN code to reproduce the results provided in this dataset release. The code that can be used to load the ADIOS2 files, load HydraGNN models, and run inference is available at: https://github.com/ORNL/HydraGNN/releases/tag/v5.0

36 MATERIALS SCIENCE↗