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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Understanding, discovery, and synthesis of 2D materials enabled by machine learning

Machine learning (ML) is becoming an effective tool for studying 2D materials. Taking as input computed or experimental materials data, ML algorithms predict the structural, electronic, mechanical, and chemical properties of 2D materials that have yet to be discovered. Such predictions expand investigations on how to synthesize 2D materials and use them in various applications, as well as greatly reduce the time and cost to discover and understand 2D materials. This tutorial review focuses on the understanding, discovery, and synthesis of 2D materials enabled by or benefiting from various ML techniques. Here, we introduce the most recent efforts to adopt ML in various fields of study regarding 2D materials and provide an outlook for future research opportunities. The adoption of ML is anticipated to accelerate and transform the study of 2D materials and their heterostructures.

2D Materials↗
DOE OSTI2022

What Is the Smallest Zeolite That Could Be Synthesized?**

Abstract Zeolites with a few unit cells are promising as catalyst and adsorbents. The quest to synthesize the smallest zeolites has recently resulted in 4 to 8 nm nanozeolites, about 2 to 4 unit cells. These findings pose the question of what is the smallest zeolite that could be obtained by hydrothermal synthesis. Here we address this question using molecular simulations and thermodynamic analysis. The simulations predict that amorphous precursors as small as 4 nm can crystallize zeolites, in agreement with the experiments. We find that interfacial forces dominate the structure of smaller particles, resulting in size‐dependent compact isomers that have ring and pore distributions different from open framework zeolites. The instability of zeolites smaller than 3±0.5 nm precludes a classical mechanism of nucleation from solution or through assembly of small nanoslabs.

Dhabal, Debdas↗
DOE OSTI2024

THD-C Sheet: A Novel Nonbenzenoid Carbon Allotrope with Tetra-, Hexa-, and Dodeca-Membered Rings

Here, we propose a novel two-dimensional carbon-based structure with tetra-, hexa-, and dodeca-membered rings, which we refer to by the abbreviated name, THD-C. The structure presents a mixture of sp–sp 2 hybridization and can potentially be synthesized by the topological assembly of 4-ethynyldiphenylacetylene molecules. By employing first-principles calculations, the stability and ease of synthesis of the sheet are investigated and compared with various C-allotropes. We predict its metallic behavior and excellent kinetic and dynamic stability. Due to the crystal structure of the sheet, a strong mechanical anisotropy is observed. The effects of functionalization on the electronic properties of the material are also studied, and different semiconducting systems are obtained. The potential of THD-C for energy storage in metal-based batteries, hydrogen storage, and catalysis is also investigated, and we find a superior performance in comparison to graphite and other allotropes. The quantum confinement effect is investigated by constructing nanoribbons and nanotubes of various sizes. For ribbons, we find that tailor-made electronic and magnetic properties can be obtained and explored in potential spintronic devices. Additionally, we observe that nanotubes are conducting irrespective of their chirality and can potentially be used for capture, storage, and separation of industrially relevant small gas molecules.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Bonding and reactivity of isostructural uranyl and neptunyl peroxide phases

Understanding the reactivity of actinide peroxides is critical for predicting the behavior of spent nuclear fuel in radiolytic environments. Herein, we report the synthesis and characterization of a lithium neptunyl(VI) hydroxo peroxo compound (LiNp), which is isostructural to the uranyl analogue (LiU). Single-crystal X-ray diffraction reveals that LiNp contains both [NpO 2 (O 2 ) 3 ] 4- and [NpO 2 (OH) 4 ] 2- units stabilized by Li + and hydrogen bonding and Raman spectroscopy shows systematic redshifts in neptunyl vibrational modes relative to uranyl. DFT calculations highlight the importance of secondary coordination in reproducing vibrational and structural features, but challenges remain with correctly modeling strong sigma donors. Solid-state EPR spectroscopy and DFT confirm superoxide stabilization within LiU and calculations suggest favorability of the analogous radical species in LiNp. Solution state EPR spectroscopy with the radical spin trap 5-tert-butoxycarbonyl-5-methyl-1-pyrroline N-oxide (BMPO) reveal evidence of superoxide in the LiU and LiNp phases and suggests stabilization of superoxide within actinyl triperoxide complexes, forming [AnO 2 (O 2 ) 2 (O) 2 • ] 3- .

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Resolving atomistic structure and oxygen evolution activity in nickel antimonates

The oxygen evolution reaction (OER) requires electrodes that are not only catalytically active, but also stable under harsh electrochemical environments to enable efficient, durable technologies. Our recent report of a stable amorphous Ni 0.5 Sb 0.5 O z OER photoanode established Ni–Sb–O as an important system for computational understanding of both the structural and catalytic behavior of these complex oxides. In the present work we show that Ni x Sb 1-x O z with x > 0.33 crystallizes into a previously unknown phase. Guided by experimental X-ray diffraction, here, we use density functional theory calculations to perform a prototype phase search to identify a broad family of stable and metastable mixed rutile and hexagonal-like phases for x = 0.33, 0.50, and 0.66 compositions. For the identified phases, we predict favorable oxygen vacancy formation energies for Ni-rich compositions under the reducing synthesis conditions which match measured Ni K-edge X-ray absorption spectra. The calculated overpotential for the most active site decreases with increasing Ni content, from 0.91 V (x = 0.33) to 0.49 V (x = 0.66), which captures the experimentally observed trend. We find the active site changes from the Ni–O–Sb bridge to a Ni–O–Ni bridge at increasing Ni concentrations, rather than the commonly studied singly under-coordinated sites. Finally, detailed Pourbaix analysis of the identified phases show excellent electrochemical stability, consistent with experimentally measured low metal ion concentrations in the electrolyte of photoelectrochemical cells. Collectively, our consideration of an ensemble of structures enables identification of the most catalytically prolific structural motifs, aiding the understanding of crystalline and amorphous catalysts and elucidating the co-optimization of activity and durability in nickel antimonates.

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

Interplay of ferromagnetic and antiferromagnetic interactions in epitaxial Co 3 ZnN

Antiperovskite nitrides with the general formula M 3 AN have attracted significant attention due to their tunable electronic and magnetic properties. Among them are many cobalt-based compounds predicted to exhibit high thermodynamic stability and intriguing magnetic behavior. Here, we report the synthesis and magnetic characterization of epitaxial Co3ZnN thin films grown by radio frequency sputtering on SrTiO 3 (STO) and MgO substrates. X-ray diffraction confirms phase-pure (00l)-oriented films with cube-on-cube epitaxy on STO, with a c-lattice parameter of 3.752 Å. Magnetic measurements reveal clear hysteresis at 2 K with a coercive field of ∼0.11 T and a small net moment of 0.108 μ B /f.u., suggesting either a canted antiferromagnetic (AFM) or ferrimagnetic (FiM) configuration. Temperature-dependent magnetization measurements show a transition near 25 K, with strong AFM interactions with Curie–Weiss temperature (Θ) = −80.13 K. Complementary density functional theory and Monte Carlo simulations indicate a ferromagnetic (FM) ground state, with the FM–AFM energy difference decreasing systematically with increasing supercell size, consistent with competition between FM and AFM/FiM interactions. These results highlight Co3ZnN as a magnetically complex antiperovskite nitride with competing exchange interactions.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Nanotechnology for catalysis and solar energy conversion

This roadmap on Nanotechnology for Catalysis and Solar Energy Conversion focuses on the application of nanotechnology in addressing the current challenges of energy conversion: 'high efficiency, stability, safety, and the potential for low-cost/scalable manufacturing' to quote from the contributed article by Nathan Lewis. This roadmap focuses on solar-to-fuel conversion, solar water splitting, solar photovoltaics and bio-catalysis. It includes dye-sensitized solar cells (DSSCs), perovskite solar cells, and organic photovoltaics. Smart engineering of colloidal quantum materials and nanostructured electrodes will improve solar-to-fuel conversion efficiency, as described in the articles by Waiskopf and Banin and Meyer. Semiconductor nanoparticles will also improve solar energy conversion efficiency, as discussed by Boschloo et al in their article on DSSCs. Perovskite solar cells have advanced rapidly in recent years, including new ideas on 2D and 3D hybrid halide perovskites, as described by Spanopoulos et al 'Next generation' solar cells using multiple exciton generation (MEG) from hot carriers, described in the article by Nozik and Beard, could lead to remarkable improvement in photovoltaic efficiency by using quantization effects in semiconductor nanostructures (quantum dots, wires or wells). These challenges will not be met without simultaneous improvement in nanoscale characterization methods. Terahertz spectroscopy, discussed in the article by Milot et al is one example of a method that is overcoming the difficulties associated with nanoscale materials characterization by avoiding electrical contacts to nanoparticles, allowing characterization during device operation, and enabling characterization of a single nanoparticle. Besides experimental advances, computational science is also meeting the challenges of nanomaterials synthesis. The article by Kohlstedt and Schatz discusses the computational frameworks being used to predict structure–property relationships in materials and devices, including machine learning methods, with an emphasis on organic photovoltaics. The contribution by Megarity and Armstrong presents the 'electrochemical leaf' for improvements in electrochemistry and beyond. In addition, biohybrid approaches can take advantage of efficient and specific enzyme catalysts. These articles present the nanoscience and technology at the forefront of renewable energy development that will have significant benefits to society.

14 SOLAR ENERGY↗
DOE OSTI2025

A15 phase Ta 3⁢ Sb thin films: Direct synthesis, charge transport, and spin-orbit torque

Ta 3 ⁢Sb is one of the A15 compounds that have been predicted to have giant spin Hall conductivities due to the gapped Dirac-like band crossings in their electronic structures. We use co-sputtering to directly synthesize thin films of Ta 3 ⁢Sb and identify a large window of Ta:Sb flux ratio that permits the formation of single-phase A15 structure. These sputtered films have an actual Ta:Sb atomic ratio of 4:1, as determined from Rutherford backscattering spectrometry. Their high resistivity, at the Mott-Ioffe-Regel limit, suggests that the electron mean free path is comparable to interatomic distances. From harmonic Hall and spin-torque ferromagnetic resonance measurements, the intrinsic spin Hall conductivity of thin film Ta 3 ⁢Sb is estimated to be in the range of −526 to −1230⁢(ℏ/𝑒)S/cm at 300 K, lower in magnitude than the predicted value of −1400⁢(ℏ/𝑒)S/cm . Here, first-principles calculations of the electronic structure show that the discrepancy is consistent with an increase of the Fermi level due to the nonideal stoichiometry needed to stabilize the A15 structure.

Density functional theory↗
DOE OSTI2026

Soil fertility controls on tropical forest productivity and mortality: synthesis and roadmap

Tropical forests are highly diverse and productive ecosystems and store nearly 60% of vegetation biomass. Across the tropics, forests span large gradients of soil fertility, from vast regions situated on highly weathered, ancient geological formations to others on young, nutrient-rich landscapes. Tropical forest productivity, mortality, and biomass all vary systematically across these gradients in soil fertility. Aboveground forest productivity tends to increase with higher soil fertility, while counterintuitively, aboveground biomass does not increase proportionally. This disconnect is likely due to coinciding increases in mortality with higher soil fertility. However, we know relatively little about the mechanisms underlying how soil fertility regulates productivity or mortality – two critical determinants of forest biomass – and even less about how these relationships will shape tropical forest responses to global change. Here, we present a mechanistic framework for understanding how soil fertility affects productivity through photosynthesis, carbon allocation, and carbon-use efficiency; how it modulates mortality via direct and indirect interactions with various drivers of mortality; and how global change may alter these relationships. Based on this synthesis, we outline a roadmap to advance our understanding of tropical forests and improve predictions of their responses to global change.

Wong, Michelle Y [Yale University] (ORCID:00000002↗
DOE OSTI2024

The multifaceted role of c-di-AMP signaling in the regulation of Porphyromonas gingivalis lipopolysaccharide structure and function

This study unveils the intricate functional association between cyclic di-3’,5’-adenylic acid (c-di-AMP) signaling, cellular bioenergetics, and the regulation of lipopolysaccharide (LPS) profile in Porphyromonas gingivalis, a Gram-negative obligate anaerobe considered as a keystone pathogen involved in the pathogenesis of chronic periodontitis. Previous research has identified variations in P. gingivalis LPS profile as a major virulence factor, yet the underlying mechanism of its modulation has remained elusive. We employed a comprehensive methodological approach, combining two mutants exhibiting varying levels of c-di-AMP compared to the wild type, alongside an optimized analytical methodology that combines conventional mass spectrometry techniques with a novel approach known as FLAT n . We demonstrate that c-di-AMP acts as a metabolic nexus, connecting bioenergetic status to nuanced shifts in fatty acid and glycosyl profiles within P. gingivalis LPS. Notably, the predicted regulator gene cdaR, serving as a potent regulator of c-di-AMP synthesis, was found essential for producing N-acetylgalactosamine and an unidentified glycolipid class associated with the LPS profile. The multifaceted roles of c-di-AMP in bacterial physiology are underscored, emphasizing its significance in orchestrating adaptive responses to stimuli. Furthermore, our findings illuminate the significance of LPS variations and c-di-AMP signaling in determining the biological activities and immunostimulatory potential of P. gingivalis LPS, promoting a pathoadaptive strategy. The study expands the understanding of c-di-AMP pathways in Gram-negative species, laying a foundation for future investigations into the mechanisms governing variations in LPS structure at the molecular level and their implications for host-pathogen interactions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Understanding Trigger Linkage Dynamics in Energetic Materials Using Mixed Picramide Nitrate Ester Explosives

The ability to predict the handling sensitivity of new organic energetic materials has been a longstanding goal. We report the synthesis and characterization of six new nitropicramide energetic materials with mixed functional groups that mimic known explosives such as nitroglycerin, erythritol tetranitrate (ETN), and pentaerythritol tetranitrate (PETN). The molecules have been studied theoretically using quantum molecular dynamics (QMD) simulations and density functional theory (DFT) calculations to identify the weakest bond in the reactants - the trigger-linkages - which control handling sensitivity, and to quantify their specific enthalpies of explosion. In good accord with the drop weight impact sensitivity data, our calculations predict that the sensitivities of the molecules are very similar owing to the small variations of the energy output and rates of trigger linkage rupture. In addition, both the QMD and DFT calculations point to the nitropicramide N–NO 2 bonds as the trigger linkages rather than the more typical O–NO 2 bonds. We propose that the switch of the trigger linkage from the nitrate esters to the nitramine groups arises from the strongly electron withdrawing character of the adjacent trinitrobenzene groups.

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

Automated and High-Throughput Phase Separation Control for Supramolecular Polymer Blends Enabled by Machine Learning

Supramolecular polymer blends (SPBs) offer tunable morphologies that dictate their macroscopic properties, yet their rational design is limited by the absence of predictive structure−morphology models. Here, we introduce a data-driven highthroughput workflow that integrates modular polymer synthesis, robotic formulation, automated morphology characterization, and machine learning (ML) for accelerated SPB discovery. Using a plug-and-play synthetic strategy, 33 hydrogen-bonding endfunctional homopolymers were prepared and orthogonally combined to generate 260 SPBs in 1 day. A fully automated atomic force microscopy (AFM) pipeline enabled systematic imaging, producing 2340 morphology data sets with minimal human intervention. Domain spacings were extracted through complementary imageprocessing methods and used to train ML models. A support vector regression (SVR) model accurately predicted target phase-separation sizes (50, 100, and 150 nm), which were experimentally validated. This work demonstrates the power of coupling high-throughput experimentation with ML to accelerate morphology discovery and provides one of the first large-scale experimental data sets for supramolecular polymer systems.

ML-guided polymer design↗
DOE OSTI2024

Artificial-Intelligence Aided Design and Synthesis of Novel Layered 2D Multi-Principal Element Materials for Energy Storage (Final Report)

This DOE-EPSCoR project aimed to predict, synthesize, and characterize novel layered two-dimensional (2D) high-entropy materials (HEMs). These 2D-HEMs, composed of multiple principal elements in nearly equal concentrations, are distinct from traditional 2D materials (typically containing two or three elements) and conventional alloys (dominated by a single primary element with minor secondary additions). Their unique structural and compositional features enable significant lattice strain accommodation, resulting in enhanced electrode performance and potential applications in catalysis, hydrogen storage, sensing, quantum information technologies, and flexible electronics. The research focused on addressing four fundamental questions: (i) What combinations of elements can form stable and synthesizable 2D-HEMs? (ii) What mechanisms drive the stability and synthesizability of crystalline single-phase 2D-HEMs? (iii) How do local chemical disorder and defects influence the macroscopic electronic and mechanical properties? and (iv) What charge storage mechanisms are active in selectively synthesized 2D-HEMs for battery and supercapacitor electrode applications? To achieve these goals, the project employed an integrated theory-experiment approach, incorporating high-throughput first-principles calculations, theoretical modeling, data mining, experimental synthesis, and advanced characterization techniques. The advanced computing resources and state-of-the-art experimental characterization facilities at Oak Ridge National Laboratory (ORNL) were leveraged through collaboration. Beyond scientific advancements, the project contributed to workforce development. Two postdoctoral researchers and three graduate students at the University of Maine were trained through co-advising by ORNL scientists and collaborative interactions, strengthening their expertise in cutting-edge materials science.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Revolutionizing Energy Storage: AI, Automation, and Advanced Modeling as Catalysts for Next-Generation Breakthroughs

The Presidential Symposium (PRES) at the 2025 Fall Meeting, hosted by the President’s Office and Energy and Fuels Division, American Chemical Society (ACS) in Washington, DC, brought together a diverse group of chemists, engineers, and materials scientists working in battery materials & systems, automation and artificial intelligence from academia, industry, and national laboratories. The accelerating demand for high-performance, scalable, and sustainable energy storage has catalyzed a paradigm shift in how materials are dis-covered, devices are engineered, and systems are optimized. This Presidential Symposium, entitled “Revolutionizing Energy Storage: AI, Automation, and Advanced Modeling Driving Next-Gen Breakthroughs”, brings together global leaders to unveil transformative strategies anchored in the AAA framework: Artificial Intelligence, Automation, and Advanced Modeling. Artificial Intelligence is redefining the frontiers of energy storage by enabling predictive design, real-time optimization, and intelligent control across diverse chemistries and architectures. Automation is streamlining the synthesis, characterization, and testing of battery materials, dramatically accelerating innovation cycles and unlocking scalable solutions for grid and mobility applications. Advanced Modeling, spanning atomic to system-level scales, provides unprecedented insight into electrochemical dynamics, degradation pathways, and thermal behavior, particularly when coupled with physics-informed machine learning and digital twin technologies. Digital twins, in turn, leverage the AAA framework by integrating real-time data, physics-based models, and AI predictions into dynamic virtual replicas, enabling proactive diagnostics, optimization, and system resilience. Together, these synergistic pillars are not only re-shaping the scientific landscape but also forging a new era of reproducible, data-driven, and resilient energy storage innovation. In conclusion, this symposium marks a pivotal moment in the convergence of computational intelligence and experimental rigor, charting the course for next-generation breakthroughs in lithium-ion, solid-state, and flow battery technologies.

Artificial Intelligence (AI)↗
DOE OSTI2020

Roadmap on multiscale materials modeling

Modeling and simulation is transforming modern materials science, becoming an important tool for the discovery of new materials and material phenomena, for gaining insight into the processes that govern materials behavior, and, increasingly, for quantitative predictions that can be used as part of a design tool in full partnership with experimental synthesis and characterization. Modeling and simulation is the essential bridge from good science to good engineering, spanning from fundamental understanding of materials behavior to deliberate design of new materials technologies leveraging new properties and processes. This Roadmap presents a broad overview of the extensive impact computational modeling has had in materials science in the past few decades, and offers focused perspectives on where the path forward lies as this rapidly expanding field evolves to meet the challenges of the next few decades. The Roadmap offers perspectives on advances within disciplines as diverse as phase field methods to model mesoscale behavior and molecular dynamics methods to deduce the fundamental atomic-scale dynamical processes governing materials response, to the challenges involved in the interdisciplinary research that tackles complex materials problems where the governing phenomena span different scales of materials behavior requiring multiscale approaches. The shift from understanding fundamental materials behavior to development of quantitative approaches to explain and predict experimental observations requires advances in the methods and practice in simulations for reproducibility and reliability, and interacting with a computational ecosystem that integrates new theory development, innovative applications, and an increasingly integrated software and computational infrastructure that takes advantage of the increasingly powerful computational methods and computing hardware.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Emerging materials intelligence ecosystems propelled by machine learning

We report that the age of cognitive computing and artificial intelligence (AI) is just dawning. Inspired by its successes and promises, several AI ecosystems are blossoming, many of them within the domain of materials science and engineering. These materials intelligence ecosystems are being shaped by several independent developments. Machine learning (ML) algorithms and extant materials data are utilized to create surrogate models of materials properties and performance predictions. Materials data repositories, which fuel such surrogate model development, are mushrooming. Automated data and knowledge capture from the literature (to populate data repositories) using natural language processing approaches is being explored. The design of materials that meet target property requirements and of synthesis steps to create target materials appear to be within reach, either by closed-loop active-learning strategies or by inverting the prediction pipeline using advanced generative algorithms. AI and ML concepts are also transforming the computational and physical laboratory infrastructural landscapes used to create materials data in the first place. Surrogate models that can outstrip physics-based simulations (on which they are trained) by several orders of magnitude in speed while preserving accuracy are being actively developed. Automation, autonomy and guided high-throughput techniques are imparting enormous efficiencies and eliminating redundancies in materials synthesis and characterization. The integration of the various parts of the burgeoning ML landscape may lead to materials-savvy digital assistants and to a human-machine partnership that could enable dramatic efficiencies, accelerated discoveries and increased productivity. Here, we review these emergent materials intelligence ecosystems and discuss the imminent challenges and opportunities. The materials research landscape is being transformed by the infusion of approaches based on machine learning. This Review discusses the emerging materials intelligence ecosystems and the potential of human-machine partnerships for fast and efficient virtual materials screening, development and discovery.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Extending carbon chemistry at high-pressure by synthesis of CaC 2 and Ca 3 C 7 with deprotonated polyacene- and para-poly(indenoindene)-like nanoribbons

Metal carbides are known to contain small carbon units similar to those found in the molecules of methane, acetylene, and allene. However, for numerous binary systems ab initio calculations predict the formation of unusual metal carbides with exotic polycarbon units, [C 6 ] rings, and graphitic carbon sheets at high pressure (HP). Here we report the synthesis and structural characterization of a HP-CaC 2 polymorph and a Ca 3 C7 compound featuring deprotonated polyacene-like and para-poly(indenoindene)-like nanoribbons, respectively. We also demonstrate that carbides with infinite chains of fused [C6] rings can exist even at conditions of deep planetary interiors (~140 GPa and ~3300 K). Hydrolysis of high-pressure carbides may provide a possible abiotic route to polycyclic aromatic hydrocarbons in Universe.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗