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DOE OSTI2026

Architector 2.0: Expanded Capabilities for Metal Complex Engineering

Automated three-dimensional molecular construction from two-dimensional graph representations is critical to high-throughput discovery eIorts. Software capabilities in this area have accelerated research across fields ranging from protein design and drug discovery to transition metal catalyst development. When Architector was first introduced, it uniquely enabled high-throughput, chemically relevant three-dimensional construction of f-element complexes. Since its introduction, Architector has been applied in large-scale computational campaigns, targeted studies in critical mineral extraction, and artificial intelligence-driven discovery eIorts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

From Fundamental Interfacial Reaction Kinetics to Macroscopic Current–Voltage Characteristics: Case Study of Solid Acid Fuel Cell Limitations and Possibilities

The unique properties of solid acid electrolytes, in particular CsH 2 PO 4 , are in many ways ideal for fuel cell operation. However, the technology is constrained by high cathode overpotentials. Here a simplified cathode geometry is employed to obtain the fundamental electrochemical parameters (exchange current density and charge transfer coefficient) describing the oxygen reduction reaction (ORR) at the CsH 2 PO 4 -Pt-gas interface. The parameters are incorporated into a 1D model of the voltage–current characteristics of realistic SAFC cathodes, which reproduced the measured polarization behavior of such cathodes without recourse to fitting adjustable parameters. Following this validation, the model is utilized to evaluate the impact of changes to cathode properties, microstructure, and operating conditions. Of these, the charge transfer coefficient, measured to have a value of ≈0.6 for ORR on Pt in the SAFC cathode environment, is found to have the greatest impact on power output. Nevertheless, even without material modifications, a combination of microstructural and operational modifications are identified with projected performance metrics meeting Department of Energy targets (0.8 V at 300 mA cm –2 , and peak power density of 1 W cm –2 ), albeit at high Pt loadings. However, the analysis indicates that truly meaningful advances will likely necessitate the discovery of alternative ORR catalysts.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Designing the Protocols for Programmable Ammonia Catalysis

Programmable catalysis can provide a more energy-efficient and cost-effective route to enhancing commercial ammonia production, a key process in the advancement of renewable energy technologies and the manufacture of fertilizers and basic chemicals. This work explores the computational discovery of optimal forcing protocols to drive such dynamic catalysis models. By employing matrix-free time-stepper methods, coupled with an optimization approach, that integrates Bayesian optimization with a Bayesian continuation strategy to efficiently discover the periodic steady states of such periodically forced systems, we enable the discovery of complex optimal catalyst strain waveforms, while ensuring robust solver convergence. We demonstrate the flexibility of our approach to discover optimized forcing protocols under varying physical constraints on strain modulation or other catalyst operating parameters. We show that these can have a temporal structure more complex than simple step functions. In order to detect undesirable catalytic loops that may correlate with overall reduced performance, we perform a study using graph-theoretical analysis to investigate the dynamics of catalytic kinetic networks formed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Low-Temperature Direct Oxidation of Propane to Propylene Oxide Using Supported Subnanometer Cu Clusters

Propylene oxide, a key commodity of the chemical industry for a wide range of consumer products, is synthesized through sequential propane dehydrogenation and epoxidation reactions. However, the lack of a direct catalytic route from propane to propylene oxide reduces efficiency and represents a major challenge for catalysis science. Herein, we report the discovery of a highly active and selective catalyst, made of alumina-supported subnanometer copper clusters, which can directly convert propane to propylene oxide at temperatures as low as 150 °C. Moreover, at higher temperatures, on the same catalysts, the selectivity is switched to propylene. Accompanying theoretical calculations indicate that partially oxidized and/or hydroxylated clusters have low activation energies for both propane dehydrogenation and propylene epoxidation pathways, enabling direct conversion with very high selectivity for propylene oxide. The discovery of a low-temperature catalyst that can convert propane directly to propylene oxide provides an important opportunity for the development of energy-efficient and economic catalysts for this industrially critical process. Similarly, when operating at higher temperatures, these catalysts are posed as potent oxidative dehydrogenation catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Interpretable, extensible linear and symbolic regression models for charge density prediction using a hierarchy of many-body correlation descriptors

Here, density functional theory (DFT) is routinely used to make electronic structure predictions for high-throughput screening of materials and molecules for technologically relevant areas, like the identification of better catalysts, electronic materials, and drug discovery. However, the DFT formalism is limited by (a) its poor (quadratic-to-quartic) scaling, and (b) the need to perform repeated eigenvalue computations of the electronic Hamiltonian as part of its self-consistent field (SCF) iteration procedure to obtain the converged ground state electron density, ρ (r). Approaches that directly predict ρ (r) of a structure with high accuracy can accelerate conventional SCF calculations and can also be used in linearly scaling methods such as orbital-free DFT. To this end, we present a procedure to predict the ground state electron density of molecular and periodic three-dimensional systems directly from the atomic structure with a particular emphasis on physical interpretability. In our framework, ρ (r) is modeled using many-body correlation descriptors that accurately capture the effects of local atomic arrangements in the neighborhood of a grid point. Our use of a linear regression scheme to fit to charge density data enables transparent analysis of the relative contributions of various types of local atomic correlations. By systematically including increasingly complex correlations, our model is shown to accurately predict ρ (r) for a variety of chemically and electronically diverse systems — amorphous Ge, Al(001) slab, crystalline Ga 2 O 3 , molecular benzene, and polyethylene. We then demonstrate a symbolic regression-based protocol to construct easily computable, interpretable features from lower-order correlations that significantly improves our electron density predictions with effectively no increase in the computational cost.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2026

Interpretable Deep Learning for Advancing Field-Enhanced Catalysis

This DOE Early Career project developed a physics-informed, interpretable AI-and-modeling framework to understand and exploit electric-field effects in heterogeneous catalysis, with ammonia cracking and synthesis as a representative pathway. The team built and validated methods to map local electric fields on metal surfaces and nanoparticles, showing that low-coordination features (tips/edges/corners) can concentrate fields by several-fold relative to flat facets. Using DFT-generated datasets, the project created physics-guided machine learning models that rapidly predict local electric fields and field-dependent adsorption energetics with near-DFT accuracy while reducing computational cost by orders of magnitude. These predictions were integrated with microkinetic modeling to quantify how field-dipole interactions reshape reaction energetics and mechanisms, enabling large increases in predicted catalytic rates and substantial reductions in operating temperature under favorable field conditions. To accelerate discovery of earth-abundant catalysts, the project combined interpretable ML screening (with electronic-structure descriptors identified as key drivers) with a generative inverse-design workflow based on diffusion models and physics constraints. The resulting closed-loop approach, linking simulation, mechanistic modeling, and AI, provides reusable tools and datasets for designing catalysts and operating conditions in field-enhanced catalysis, with broad relevance to electrostatic catalysis, plasma catalysis, electrocatalysis, and other energy-related chemical transformations.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2025

Photon counting with intensified charge coupled device (ICCD) – I. In-depth detector characterization

While the adsorption properties of transition metal catalysts have been widely studied, leading to the discovery of various scaling relations, descriptors of catalytic activity, and well-established computational models, a similar understanding of semiconductor catalysts has not yet been achieved. In this work, we present a high-throughput density functional theory investigation into the adsorption properties of 5 oxides of interest to the photocatalytic CO 2 reduction reaction: TiO 2 (rutile and anatase), SrTiO 3 , NaTaO 3 , and CeO 2 . Using a systematic approach, we exhaustively identify unique surfaces and construct adsorption structures to undergo geometry optimizations. We then perform a data-driven analysis, which reveals the presence of weak adsorption energy scaling relations, the propensity of adsorbates of interest to interact with oxygen surface sites, and the importance of slab deformation upon adsorption. Our findings are presented in the context of experimental observations and in comparison to previously studied classes of catalysts, such as pure metals and tellurium-containing semiconductors, and reinforce the need for a comprehensive approach to the study of site-specific surface phenomena on semiconductors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Systematic computational study of oxide adsorption properties for applications in photocatalytic CO 2 reduction

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Unlocking the distinctive enzymatic functions of the early plant biomass deconstructive genes in a brown rot fungus by cell-free protein expression

ABSTRACT Saprotrophic fungi that cause brown rot of woody biomass evolved a distinctive mechanism that relies on reactive oxygen species (ROS) to kick-start lignocellulosic polymers’ deconstruction. These ROS agents are generated at incipient decay stages through a series of redox relays that shuttle electrons from fungus’s central metabolism to extracellular Fenton chemistry. A list of genes has been suggested encoding the enzyme catalysts of the redox processes involved in ROS’s function. However, navigating the functions of the encoded enzymes has been challenging due to the lack of a rapid method for protein synthesis. Here, we employed cell-free expression system to synthesize four redox or degradative enzymes, which were identified, by transcriptomic data, as conserved players of the ROS oxidation phase across brown rot fungal species. All four enzymes were successfully expressed and showed activities that enable confident assignment of function, namely, benzoquinone reductase (BQR), ferric reductase, α-L-arabinofuranosidase (ABF), and heme-thiolate peroxidase (HTP). Detailed analysis of their catalytic features within the context of brown rot environments allowed us to interpret their roles during ROS-driven wood decomposition. Specifically, we validated the functions of BQR as the driver redox enzyme of Fenton cycles and reconstructed its interactions with the co-occurring HTP or laccase and ABF. Taken together, this research demonstrated that the cell-free expression platform is adequate for synthesizing functional fungal enzymes and provided an alternative route for the rapid characterization of fungal proteins, escalating our understanding of the distinctive biocatalyst system for plant biomass conversion. IMPORTANCE Brown rot fungi are efficient wood decomposers in nature, and their unique degradative systems harbor untapped catalysts pursued by the biorefinery and bioremediation industries. While the use of “omics” platforms has recently uncovered the key “oxidative-hydrolytic” mechanisms that allow these fungi to attack lignocellulose, individual protein characterization is lagging behind due to the lack of a robust method for rapid synthesis of crucial fungal enzymes. This work delves into the studies of biochemical functions of brown rot enzymes using a rapid, cell-free expression platform, which allowed the successful depictions of enzymes’ catalytic features, their interactions with Fenton chemistry, and their roles played during the incipient stage of brown rot when fungus sets off the reactive oxygen species for oxidative degradation. We expect this research could illuminate cell-free protein expression system’s use to fulfill the increasing need for functional studies of fungal enzymes, advancing the discoveries of novel biomass-converting catalysts.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2026

Oxidation, Oligomerization, Isomerization of Hydrocarbons Using Metal–Organic Frameworks

The selective conversion of hydrocarbons into higher-value fuels and feedstocks is essential to the global energy and chemistry landscape. While porous inorganic materials have enabled significant progress in these transformations, achieving high activity, selectivity, and stability under industrially relevant conditions remains challenging. Metal–organic frameworks (MOFs) are a promising platform to precisely control active-site environments and interrogate structure–function relationships due to their crystallinity, tunability, and porosity. This review highlights relevant hydrocarbon transformations and outlines the general mechanisms for oxidation, oligomerization, and isomerization. Metal node acidity, confinement effects, and active site dispersion are analyzed for their impact on reactivity and selectivity across the three reactions. Lastly, we discuss current limitations in catalyst stability and offer a perspective on integrating reticular chemistry with high-throughput experimentation and machine learning to accelerate the discovery and design of robust, next-generation MOF catalysts.

Catalysts↗

DOE OSTI2026

Final Technical Report: In-Silico Heterogeneous Catalyst Design for GHG Reduction via Bulk Chemicals

This project has advanced the understanding of ammonia synthesis by developing novel thermochemical catalysts, setting a new benchmark for energy and carbon efficiency in large-scale bulk chemicals production. This research leverages a proprietary computational discovery platform and high-throughput experimental validation, significantly accelerating catalyst optimization and enabling ammonia synthesis at lower temperature and pressure conditions than conventional processes. As a result, green ammonia can be produced at a cost that meets or exceeds established targets, supporting a viable pathway toward decarbonized ammonia for fuel, fertilizers, and hydrogen supply applications.

Grose, Jacob E [Copernic Catalysts, Inc.]↗

DOE OSTI2026

Enzymatic Nylon Deconstruction: Enzyme Discovery, Engineering, and Opportunities

Nylons are widely used synthetic polyamides valued for their strength, versatility, and durability across diverse applications. However, their petrochemical origin and energy-intensive production underscore the need for efficient, circular solutions. Conventional recycling methods remain limited by incomplete recovery, material degradation, and costly sorting requirements. Enzymatic depolymerization offers a selective, low-energy alternative capable of processing mixed waste streams under mild conditions. While significant progress has been achieved for polyesters, enzymatic degradation of polyamides is still at an early stage. The discovery of nylon hydrolases demonstrated the potential of biological systems to evolve catalysts for synthetic polyamides, yet reported depolymerization yields remain low. These limitations reflect both the structural complexity of nylons and the need for improved enzyme discovery and engineering. In conclusion, this review highlights recent advances, key challenges, and future directions for enzymatic nylon recycling, outlining its potential role enabling mixed polymer waste to be used as a green feedstock for remanufacturing.

Amides↗

DOE OSTI2025

Machine Learning‐Guided Discovery of High‐Entropy Perovskite Oxide Electrocatalysts via Oxygen Vacancy Engineering

Abstract High‐entropy perovskite oxides (HEPOs) have recently emerged as multifunctional catalysts. However, the HEPOs’ structural and compositional complexity hinders the easy and accurate extrapolation of activity indicators, which are essential for establishing structure‐property correlations. Here, OxiGraphX, is introduced as a novel graph neural network (GNN) model designed to capture the complex relationships among structure, composition, and atomic chemical environments for accurate prediction of oxygen vacancy formation energies (OVFEs) in HEPOs. By integrating machine learning (ML), density functional theory (DFT), and experimental validation, this work demonstrates an efficient framework for rapidly and accurately screening HEPO electrocatalysts for oxygen evolution reaction (OER). The OxiGraphX predicts OVFEs with a precision exceeding existing data, enabling the identification of compositions of higher oxygen vacancy content (OVC) and, thus, higher catalytic activity. Furthermore, the model explores latent spaces that translate effectively into experimental domains, bridging computational predictions with real‐world applications. This approach accelerates the discovery of high‐performance HEPO catalysts while providing deeper insights into their catalytic mechanisms.

Chemistry↗

DOE OSTI2025

Metal hydrides: a historical perspective

Metal hydrides are known for their outstanding performance as materials for hydrogen storage and processing. These materials find applications for short- and long-term energy storage, compression and supply of hydrogen gas, thermal energy storage, as electrodes and electrolytes in rechargeable batteries, for the microstructural optimisation of functional materials, in thin film technologies, as catalysts, getters and in many other uses. After the discovery of the first binary metal hydrides back in the 19th century, their studies covered all possible binary M-H systems and expanded rapidly into the field of ternary hydrides following the recognition of the excellent hydrogen storage performance of LaNi 5 - and TiFe-based materials, which operate efficiently at room temperature and at near-ambient H 2 pressures. This review aims to provide an overview of the early works, as well as selected recent results on various classes of metal hydrides. It also covers the recent activities from the major contributing countries and continents, including USA, Europe, Japan, China and Australia. These studies relate to achieving the hydrogen storage systems goals set by the Department of Energy in the United States which inspired the research activities at the national and international level, through execution of the tasks on hydrogen-based energy storage managed by the International Energy Agency. The review is prepared by international experts in the field and covers the most important past developments and also presents the recent achievements in the field.

08 HYDROGEN↗

DOE OSTI2024

Catalytic Upgrading of Renewable Feedstock (Final Technical Report)

The goal of this DOE-funded project was to investigate the fundamental science related to the development of homogeneous (de)hydrogenation catalysts in order to enable energy-relevant transformations of bio-relevant chemical feedstocks including ethanol. The primary focus was to improve the activity of ethanol upgrading catalysts, specifically informed through mechanistic studies, in order to enable rational design optimization strategies. Following in depth mechanistic studies, targeted reaction optimization approaches included ligand redesign to improve catalyst stability, developing new carbon-carbon bond forming reactions using ethanol as a precursor, and examining photochemically-mediated reactions, ultimately to integrate within other reactor designs such as continuous flow reactors. The high modularity of the catalyst components (ligands) has enabled the preparation and analyses of multiple catalyst precursors. These studies uncovered an unexpectedly beneficial substitution pattern of the ligand structure that led to the development of new catalysts that are the best in class for upgrading ethanol to butanol with a turnover number of 155,890 and a turnover frequency of 12,690 h –1 . In addition to upgrading ethanol to butanol, cascade reaction sequences were developed to form new C-C bonds using ethanol as a bio-relevant feedstock, providing access to platform chemicals from renewable sources. As part of the reaction discovery process, new mechanistic details were uncovered that provided insights into: a) catalyst speciation, b) decomposition pathways, c) carbon monoxide releasing pathways, and d) carbon-carbon and carbon hydrogen bond breaking pathways. Most of these outcomes were previously not known; however, they provide important directions for new catalyst design strategies. Finally, use of high throughput and in situ photochemical reaction analyses enabled detailed studies into changes to the catalyst structure upon irradiation. Irradiation was found to improve hydrogen transfer catalysis, by promoting a ligand dissociation event.

09 BIOMASS FUELS↗

DOE OSTI2024

Machine learning materials properties with accurate predictions, uncertainty estimates, domain guidance, and persistent online accessibility

One compelling vision of the future of materials discovery and design involves the use of machine learning (ML) models to predict materials properties and then rapidly find materials tailored for specific applications. However, realizing this vision requires both providing detailed uncertainty quantification (model prediction errors and domain of applicability) and making models readily usable. At present, it is common practice in the community to assess ML model performance only in terms of prediction accuracy (e.g. mean absolute error), while neglecting detailed uncertainty quantification and robust model accessibility and usability. Here, we demonstrate a practical method for realizing both uncertainty and accessibility features with a large set of models. We develop random forest ML models for 33 materials properties spanning an array of data sources (computational and experimental) and property types (electrical, mechanical, thermodynamic, etc). All models have calibrated ensemble error bars to quantify prediction uncertainty and domain of applicability guidance enabled by kernel-density-estimate-based feature distance measures. All data and models are publicly hosted on the Garden-AI infrastructure, which provides an easy-to-use, persistent interface for model dissemination that permits models to be invoked with only a few lines of Python code. We demonstrate the power of this approach by using our models to conduct a fully ML-based materials discovery exercise to search for new stable, highly active perovskite oxide catalyst materials.

domain of applicability↗

DOE OSTI2025

Mechanistic Insights toward Accelerated Selenium-Catalyzed Allylic and Propargylic C–H Amination

Catalytic allylic and propargylic C–H aminations present valuable opportunities for the late-stage modification of pharmacophores in drug discovery. However, modern methodology is limited by reliance on expensive transition metal catalysts or slow reactivity. While selenium-catalyzed methods avoid some of these issues, in their current state, they require high catalyst loadings (15 mol %), superstoichiometric quantities of the amine and oxidant source, and long reaction times. Furthermore, our understanding of the mechanism remains incomplete: e.g., what is the catalytically active species, how is the precatalyst converted to it, and what is the role of the ligand? Here, in this paper, we report an N-heterocyclic carbene selenide (NHC-Se) that allows for substantially reduced loadings of selenium without sacrificing reaction time, improves conversions and yields for challenging substrates, and even exhibits the capacity to catalyze 1,4-allylic diamination of alkenes. To understand the origin of the enhanced activity compared to the state-of-the-art NHC-Se precatalyst, we conducted a mechanistic study that supports ligand-free selenium diimide as the active enophile in these systems, while the NHC-derived byproducts, like the corresponding urea, facilitate turnover. We also isolate and structurally characterize NHC-Se mono- and diimido species and explore their mechanistic role. Thus, this work advances C–H amination methodology, our understanding of its mechanistic underpinnings, and selenium chemistry at large.

allylic amination↗

DOE OSTI2025

Diffusion Model-Guided Inverse Design of Bimetallic Catalysts for Ammonia Decomposition

In the past decade, artificial intelligence and deep learning have played increasingly prominent roles in materials design and discovery. Among these, generative AI models, known for their ability to create unique and complex structures, have emerged as state-of-the-art tools for materials screening due to their high efficiency and low computational cost. In catalysis, one of the major challenges is identifying promising material candidates within an immense chemical space. This challenge can be addressed using generative approaches, such as diffusion-based inverse design models. In this study, we present a machine learning-guided workflow that employed a diffusion model for the inverse design of bimetallic alloy catalysts for low-carbon ammonia decomposition, a key reaction for ammonia emission control and sustainable hydrogen production. Catalyst candidates were evaluated using nitrogen adsorption energy as the key descriptor, inspired by multiscale modeling. The proposed workflow identified low-cost, environmentally friendly catalysts with excellent catalytic performance, which have been validated theoretically and experimentally. Our framework decoupled the generative and property-prediction components, enhancing both flexibility and accuracy in the catalytic material design process.

Adsorption↗