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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

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↗

NASA NTRS2004

Constructing an Educational Mars Simulation

January 14th 2004, President George Bush announces his plans to catalyst the space program into a new era of space exploration and discovery. His vision encompasses a robotics program to explore our solar system, a return to the moon, the human exploration of Mars, and to promote international prosperity towards our endeavors. We at NASA now have the task of constructing this vision in a very real timeframe. I have been chosen to begin phase 1 of making this vision a reality. I will be working on creating an Educational Mars Simulation of human exploration of Mars to stimulate interest and involvement with the project from investors and the community. GRC s Computer Services Division (CSD) in collaboration with the Office of Education Programs will be designing models, constructing terrain, and programming this simulation to create a realistic portrayal of human exploration on mars. With recent and past technological breakthroughs in computing, my primary goal can be accomplished with only the aid of 3-4 software packages. Lightwave 3D is the modeling package we have selected to use for the creation of our digital objects. This includes a Mars pressurized rover, rover cockpit, landscape/terrain, and habitat. Once we have the models completed they need textured so Photoshop and Macromedia Fireworks are handy for bringing these objects to life. Before directly importing all of this data into a simulation environment, it is necessary to first render a stunning animation of the desired final product. This animation with represent what we hope to capture out of the simulation and it will include all of the accessories like ray-tracing, fog effects, shadows, anti-aliasing, particle effects, volumetric lighting, and lens flares. Adobe Premier will more than likely be used for video editing and adding ambient noises and music. Lastly, V-Tree is the real-time 3D graphics engine which will facilitate our realistic simulation. Additional information is included in the original extended abstract.

Henke, Stephen A.↗

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↗

DOE OSTI2024

Porous liquids: an integrated platform for gas storage and catalysis

Porous liquids (PLs) represent a new frontier in materials design, combining the unique features of fluidity in liquids and permanent porosity in solids. By engineering well-defined pores into liquids via designed structure modification techniques, the greatly improved free volume significantly enhances the gas transport and storage capability of PL sorbents. Triggered by the promising applications of PLs in gas separation, PLs are further explored in catalysis particularly to integrate the gas storage and catalytic transformation procedure. This emerging field has demonstrated promising progress to advance catalytic procedures using PLs as catalysts, with performance surpassing that of the pure liquid and porous host counterparts. In this perspective article, the recent discoveries and progress in the field of integrated gas storage and catalysis by leveraging the PL platforms will be summarized, particularly compared with the traditional homogeneous or heterogeneous catalytic procedures. The unique features of PLs endow them with combined merits from liquid and solid catalysts and beyond which will be illustrated first. This will be followed by the unique techniques being utilized to probe the porosity and active sites in PLs and the structural evolution during the catalytic procedures. The catalytic application of PLs will be divided by the reaction categories, including CO 2 -involving transformation, O 2 -involving reaction, H 2 S conversion, hydrogenation reaction, and non-gas involving cascade reactions. In each reaction type, the synthesis approaches and structure engineering techniques of PLs, structure characterization, catalytic performance evaluation, and reaction mechanism exploration will be discussed, highlighting the structure–performance relationship and the advancement benefiting from the unique features of PLs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1985

A New Global Geomorphology?

Geomorphology is entering a new era of discovery and scientific excitement centered on expanding scales of concern in both time and space. The catalysts for this development include technological advances in global remote sensing systems, mathematical modeling, and the dating of geomorphic surfaces and processes. Even more important are new scientific questions centered on comparative planetary geomorphology, the interaction of tectonism with landscapes, the dynamics of late Cenozoic climatic changes, the influence of cataclysmic processes, the recognition of extremely ancient landforms, and the history of the world's hydrologic systems. These questions all involve feedback relationships with allied sciences that have recently yielded profound developments.

Baker, V. R.↗

DOE OSTI2025

Rethinking 𝛼−RuCl 3 : Parameters, models, and phase diagram

RuCl 3 was likely the first ever deliberately synthesized ruthenium compound, following the discovery of the 44 Ru element in 1844. For a long time it was known as an oxidation catalyst, with its physical properties being discrepant and confusing, until a decade ago when its allotropic form 𝛼−RuCl 3 rose to exceptional prominence. This “rediscovery” of 𝛼−RuCl 3 has not only reshaped the hunt for a material manifestation of the Kitaev spin liquid, but it has opened the floodgates of theoretical and experimental research in the many unusual phases and excitations that the anisotropic-exchange magnets as a class of compounds have to offer. Given its importance for the field of Kitaev materials, it is astonishing that the low-energy spin model that describes this compound and its possible proximity to the much-desired spin-liquid state is still a subject of significant debate ten years later. In the present study, we argue that the existing key phenomenological observations put strong natural constraints on the effective microscopic spin model of 𝛼−RuCl 3 , and specifically on its spin-orbit-induced anisotropic-exchange parameters that are responsible for the nontrivial physical properties of this material. These constraints allow one to focus on the relevant region of the multidimensional phase diagram of the 𝛼−RuCl 3 model, suggest an intuitive description of it via a different parametrization of the exchange matrix, offer a unifying view on the earlier assessments of its parameters, and bring closer together several approaches to the derivation of anisotropic-exchange models. We explore extended phase diagrams relevant to the 𝛼−RuCl 3 parameter space using quasiclassical, Luttinger-Tisza, exact diagonalization, and density-matrix renormalization-group methods, demonstrating a remarkably close quantitative accord between them on the general structure and hierarchy of the phases, with the zigzag, ferromagnetic, and incommensurate phases that are proximate to each other. As a result, one of the highlights is the detailed agreement on the nature of the incommensurate phases that realize two distinct counterrotating helical states.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

Salt Hydrate Eutectic Thermal Energy Storage for Building Thermal Regulation (Final Technical Report)

Thermal energy storage is anticipated to play an important role in developing the power grid of the future - a power grid that meets increasing demands of users, is resistant to disruptions, but also allows for greater penetration of renewable resources. Specifically, thermal energy storage materials can be integrated into HVAC systems and building envelopes, where they can be used to shift power demands for building climate control from periods of peak demand to periods of low demand. Phase change materials (PCMs) are compelling as low-cost, high energy density thermal energy storage materials for building thermal management. However, there is a lack of high performance low-cost PCMs within the specific temperature ranges which would most effectively allow for power load shifting. Inorganic salt hydrates represent a promising class of PCMs, but their inherent limitations cause them to be currently unavailable for reliable building applications. The overarching goals of this research effort are to: 1) Discover low-cost, high volumetric density salt hydrate eutectic PCMs to store low-quality heat (10 to 40 °C); 2) Introduce a high thermal conductivity matrix to reduce the time constant for energy storage to ~0.1 to 1 hr, incorporate nucleation catalysts to decrease undercooling, and utilize microencapsulation and shape stabilization approaches, minimizing moisture loss/gain, mitigating phase separation, and maintaining stable melting behavior over the lifetime of the compounds; 3) Evaluate the impact these systems have on peak load shifting, and the potential for overall energy savings under different climatic scenarios and building configurations. These goals will be achieved by an integrated research program consisting of six cohesive research subtasks: 1) Materials discovery of eutectic salt hydrate PCMs by using computationally predicted thermodynamic equilibria, coupled with high-throughput experimental validation, 2) Rapid experimental screening of nucleation catalysts identified through robust computational databases, 3) Embedding salt hydrate PCM into a low cost and scalable high conductivity matrix, 4) Microencapsulation of salt hydrate microspheres using hybrid inorganic-polymer microencapsulation approach, 5) Shape stabilization by thermoreversible salt hydrate salogels, and 6) Analysis of end-use using thermal simulations, and characterization of mock-up energy storage finished components.

25 ENERGY STORAGE↗

DOE OSTI2026

Bayesian Optimization of Catalysis with In-Context Learning

Large language models (LLMs) can perform accurate classification with zero or few examples through in-context learning (ICL), allowing the model to observe query-relevant examples at inference time and eliminating the need for additional weight updates to generalize beyond its original training data. We extend this capability to regression with uncertainty estimation using frozen LLMs (e.g., GPT-4o, Gemini), enabling Bayesian optimization (BO) in natural language without explicit model training or feature engineering. We apply this to materials discovery by representing materials as synthesis and testing procedures for use in natural language prompts. This Bayesian, design-first approach prioritizes optimization toward target material properties before detailed characterization, in contrast to conventional experimental workflows that often emphasize characterization of suboptimal materials. On benchmarks like aqueous solubility and oxidative coupling of methane (OCM), BO-ICL matches or outperforms Gaussian processes. In live experiments on the reverse water–gas shift (RWGS) reaction, BO-ICL identifies multimetallic catalysts that approach equilibrium CO yield within 6 and 10 iterations from a pool of 3,700 and 360,000 candidates, respectively. Our method redefines materials representation and accelerates discovery, with broad applications across catalysis, materials science, and AI.

Calibration↗

DOE OSTI2025

High entropy alloys as catalysts: A focused review

A brief literature review of recent experimental and computational efforts on the use of high entropy alloys (HEAs) as catalysts is presented, while also sharing some perspectives and future insights. To fully broach the vast compositional possibilities of HEA materials, integrating computational modeling with high-throughput experimental synthesis and validation is necessary to accelerate their design and development. Once identified, specific HEAs can be a class of materials for the next generation of catalysts when addressing global challenges related to energy independence, commodity chemical production, environmental remediation, abating emissions, and modernized domestic supply chain resilience.

42 - ENGINEERING↗

DOE OSTI2024

Additive manufacturing of multiscale NiFeMn multi-principal element alloys with tailored composition

Nanostructured multi-principal element alloys (MPEAs) have been explored as next-generation engineering materials due to unique mechanical and functional properties which have significant advantages over traditional dilute alloys. However, the practical applications of nanostructured MPEAs are still limited due to the lack of scalable processing approaches to prepare a large quantity of nanostructured MPEAs, as well as lack of an efficient pathway for high-throughput discovery of better functional nanostructured MPEAs within their vast compositional space. Here we tackle these challenges by presenting an integrated approach by combining direct-ink-writing-based additive manufacturing, solid-state sintering, and chemical dealloying to manufacture hierarchically porous MPEAs. The hierarchical structure is comprised of macro- and micro-scale pores introduced via extrusion printing and polymer decomposition during sintering, as well as nanoscale pores formed via chemical dealloying. The macro- and micro-scale pores allow efficient dealloying of a large mass of material as the diffusion length that the corroding medium must penetrate remains at the scale of the ligaments formed after sintering (∼10 μm), despite the large volume of the 3D-printed samples. In addition, this integrated approach enables versatile control of the alloy composition via precisely tuning the ratio of elemental powders in the starting ink, thus offering a pathway for high-throughput discovery of novel functional MPEAs. As a case study, multiscale macro/micro/nanoporous NiFeMn MPEAs with three different compositions were investigated as catalysts to reduce the overpotential of oxygen evolution reaction (OER), where NiFeMn-based electrocatalysts display composition-dependent performance such that the overpotential measured at a current of 0.5 A g −1 for OER increases in the order of Ni 58 Fe 29 Mn 13 ⩽ Ni 64 Fe 26 Mn 10 < Ni 76 Fe 18 Mn 6 . This introduced manufacturing process offers new opportunities for scalable fabrication and rapid screening of nanostructured multi-component complex alloys.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Machine learning-guided design, synthesis, and characterization of atomically dispersed electrocatalysts

The recent integration of machine learning into materials design has revolutionized the understanding of structure–property relationships and optimization of material properties beyond the trial-and-error paradigm. On one hand, machine learning has significantly accelerated the development of atomically dispersed metal-nitrogen-carbon (M-N-C) electrocatalysts, which traditionally heavily relied on heuristic approaches. On the other hand, the primary challenge of leveraging machine learning to expedite M-N-C materials discovery lies in the cost associated with data collection. Here, we review recent machine learning integration strategies for M-N-C catalyst development, including discussions on the typical algorithms such as symbolic regression and convolutional neural networks employed for the theoretical design, synthesis optimization via active learning, and advanced microscopy characterization. Subsequently, we provide our perspective on potential near-future directions for furthering machine learning-assisted development of new M-N-C catalysts and elucidating the complex physicochemical mechanisms governing the selectivity, activity, and durability in this class of materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2017

Electrochemical Detection of Biological Catalysts as Signatures of Extant Life

Liquid water is the quintessential ingredient for life. Biochemistry as we know it is not possible without it. There are two places in the Solar System, other than the Earth, where liquid water is known to exist: Europa and Enceladus. Cassini’s recent discovery of Enceladus’ ocean plumes has excited the scientific community about the possibility for liquid sampling and search for life. Characteristics of life are self-sustainability, its ability to evolve, and self-enclosure. To enable these, chemical energy must be utilized, information must be stored, and enclosing membranes must be built. Many of these processes are accomplished with the use of biological catalysts. To search for life in the ocean worlds, detection of biological catalysts serves as a strong marker of biological activity and thus life.

Koehne, J. E.↗

DOE OSTI2026

Sequence-based generative AI design of versatile tryptophan synthases

Enzymes are powerful and sustainable catalysts, but their widespread application is limited by the difficulty of identifying functional starting points for optimization, creating a major bottleneck in early- stage biocatalyst discovery. Designing libraries of such starting enzymes remains particularly challenging. Here, we use the GenSLM protein language model to generate novel β-subunit of tryptophan synthase (TrpB) enzymes that express in Escherichia coli and are both stable and catalytically active. Many generated TrpBs also display significant substrate promiscuity, outperforming their natural counterparts on non-native substrates. Some even surpass laboratory-evolved TrpBs. Comparison of the most-active and most-promiscuous generated TrpB to its closest natural homolog confirms that the enhanced versatility is absent from the natural enzyme, highlighting the creative potential of generative models. These results demonstrate that the generated TrpBs not only preserve natural structure and function but also acquire non-natural properties, establishing generative models as powerful tools for biocatalyst discovery and engineering.

biocatalysis↗

DOE OSTI2025

Co-Catalyst Enabled Biotransformation of Polyunsaturated Fatty Acids for Biobased Monomers

Enzymatic lipid epoxidation offers a promising approach to obtain renewable intermediates for biomaterials, but regiochemical control of these reactions has remained elusive. Here we report the discovery and application of artificial cocatalysts to direct the regioselectivity of catalytic epoxidation with the lipase CpLIP2. The methyl esters of alkyl dicarboxylic acids show the unique ability to direct regioselective epoxidation of polyunsaturated fatty acids, major components of plant and algae-based oils. We apply this transformation to the conversion of linoleic acid into sebacic acid, a dicarboxylic acid precursor valuable for biobased polyester polyurethanes, through a six-step pathway involving a regioselective Meinwald rearrangement. To highlight the route’s significance, sebacic acid was used to prepare a 100% biobased thermoplastic polyurethane, illustrating the relevance of this pathway to industrial applications. Furthermore, this regioselective chemoenzymatic oxidation and rearrangement process can be used to access multiple dicarboxylic acids that have remained previously unexplored as biobased monomers.

09 BIOMASS FUELS↗