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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A foundation model for atomistic materials chemistry

Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much of our understanding of chemistry and materials science. Over the last decade or so, machine-learned force fields have transformed atomistic modeling by enabling simulations of ab initio quality over unprecedented time and length scales. However, early machine-learning (ML) force fields have largely been limited by (i) the substantial computational and human effort required to develop and validate potentials for each particular system of interest and (ii) a general lack of transferability from one chemical system to the next. Here, we show that it is possible to create a general-purpose atomistic ML model, trained on a public dataset of moderate size, that is capable of running stable molecular dynamics for a wide range of molecules and materials. We demonstrate the power of the MACE-MP-0 model-and its qualitative and at times quantitative accuracy-on a diverse set of problems in the physical sciences, including properties of solids, liquids, gases, chemical reactions, interfaces, and even the dynamics of a small protein. The model can be applied out of the box as a starting or "foundation" model for any atomistic system of interest and, when desired, can be fine-tuned on just a handful of application-specific data points to reach ab initio accuracy. Establishing that a stable force-field model can cover almost all materials changes atomistic modeling in a fundamental way: experienced users obtain reliable results much faster, and beginners face a lower barrier to entry. Foundation models thus represent a step toward democratizing the revolution in atomic-scale modeling that has been brought about by ML force fields.

Batatia, Ilyes↗
DOE OSTI2026

Cellulose-MOFs hybrid materials: Chemistry and mechanism of applications in biomedical - A review

Rising costs and performance limits of modern biomedical materials motivate the search for advanced, biocompatible alternatives. Cellulose-based metal-organic frameworks (cellulose-MOFs) emerge as distinctive hybrids combining renewable polymer chemistry with tunable porous architectures, enabling uncommon structure–function relationships. Their large surface area, controllable pore size, adaptable functional groups, and efficient host–guest interactions underpin diverse biomedical functions. Till now, no comprehensive, application-focused review has systematically summarized cellulose-MOFs synthesis for biomedical applications. This review critically analyzes cellulose-MOFs, emphasizing mechanistic links between chemistry, synthesis routes, interfacial interactions, and biomedical performance, rather than cataloging applications alone. Antibacterial action, targeted drug delivery, and sensing/biosensing are discussed through comparative insights. The article identifies unresolved challenges and proposes future research pathways to rationally design next-generation cellulose-MOFs systems, guiding researchers and clinicians alike.

Biomedical↗
DOE OSTI2026

Examination of Replicate Syntheses of Metal Organic Frameworks as a Window into Reproducibility in Materials Chemistry

Replicate experiments are a useful tool in understanding the repeatability of scientific measurements. In 2019, a systematic search for replicate syntheses of a collection of 130 metal–organic frameworks (MOFs) found that 89% of these materials had no reported replicate syntheses apart from the original publications identifying the material (Agrawal, M. Proc. Natl. Acad. Sci. U.S.A. 2020, 117, 877−88210.1073/pnas.1918484117). A potential weakness of that search was that only 5–11 years had elapsed since the original publication of each material. Here, this analysis is extended to all publications 11–17 years after the original publication. Although this extended time period identifies more repeat syntheses, 83% of the materials still have no reported replicate syntheses. We also consider how appropriately selected Density Functional Theory (DFT) calculations can provide corroboration for the experimentally reported crystal structures. By using data from previous high-throughput DFT studies, corroborating evidence from DFT was available for 17% of the 130 structures for which no replicate syntheses are available. In total, approximately 1/3 of the 130 MOFs have data associated with replicate synthesis experiments and/or directly corroborating DFT calculations.

Sholl, David S. [Oak Ridge National Laboratory (OR↗
DOE OSTI2026

Foundation models for atomistic simulation of chemistry and materials

Conventional computational methods for modeling chemical and materials systems are limited by system size and timescale, forcing a trade-off between quantum-mechanical accuracy and the sampling needed for realistic observables. Large language and vision foundation models — pre-trained on massive datasets using transformer architectures — have revolutionized many fields. It is thus interesting to ask whether a foundation model — subject to suitable data, parameter scaling and training — could enable learned simulations of chemistry and materials. Here, in this study, we review the field of machine-learned interatomic potentials (MLIPs) and posit that scaling up large and diverse chemical and materials datasets and highly expressive architectures using advanced training strategies should result in models that are: more efficient, transferable, robust to out-of-distribution scenarios, and easier to fine-tune to a variety of downstream physical observables than models trained from scratch on small datasets corresponding to specific, targeted atomistic simulation tasks. We provide specific criteria for creating such large-scale MLIP foundation models, coordinated strategies for their development, evaluation and deployment, and highlight potential emergent capabilities that could transform predictive simulations in chemistry and materials science and accelerate discovery across multiple technological domains.

Yuan, Eric C.-Y. [University of California, Berkel↗
DOE OSTI2026

Multireference Methods for Chemistry and Materials Science: Automated Active Spaces, Efficient Dynamic Correlation, and Extended Systems

While multiconfigurational approaches have long been relegated to expert practitioners working on a case-by-case basis, recent developments have increasingly made these methods more routine and applicable to broader sets of systems. This article outlines the state-of-the-art in multiconfigurational approaches, with an emphasis on moving from delicate hand-selected pathways through configuration space toward more robust and efficient approaches to treating a host of challenging chemical systems accurately. First, we overview recent work in automated active-space selection, which has enabled increasingly large-scale applications of multireference methods to modeling vertical excitations and reactivity. Second, we highlight the increasingly efficient methods for recovering correlation energy beyond the active space, as headlined by extensions of pair-density functional theory and its role in accurate and efficient treatment of excited-state dynamics and its utilization to train machine-learned potentials. Finally, we highlight recent efforts to treat extended systems that until recently have lied beyond the traditional limits of active-space methods, giving center stage to product-form wave functions of the localized active space family of methods that allow for the computation of multiconfigurational band structures. These recent advancements point to a broader use of multireference approaches for high-impact chemical and materials science applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Chemistry of Materials Underpinning Photoelectrochemical Solar Fuel Production

Since its inception, photoelectrochemistry has sought to power the generation of fuels, particularly hydrogen, using energy from sunlight. Efficient and durable photoelectrodes, however, remain elusive. Here we review the current state of the art, focusing our discussion on advances in photoelectrodes made in the past decade. We open by briefly discussing fundamental photoelectrochemical concepts and implications for photoelectrode function. We next review a broad range of semiconductor photoelectrodes broken down by material class (oxides, nitrides, chalcogenides, and mature photovoltaic semiconductors), identifying intrinsic properties and discussing their influence on performance. We then identify innovative in situ and operando techniques to directly probe the photoelectrode|electrolyte interface, enabling direct assessment of structure–property relationships for catalytic surfaces in active reaction environments. We close by considering more complex photoelectrochemical fuel-forming reactions (carbon dioxide and nitrogen reduction, as well as alternative oxidation reactions), where product selectivity imposes additional criteria on electrochemical driving force and photoelectrode architecture. By contextualizing recent literature within a fundamental framework, we seek to provide direction for continued progress toward achieving efficient and stable fuel-forming photoelectrodes.

08 HYDROGEN↗
DOE OSTI2023

Smart Droplets Stabilized by Designer Surfactants: From Biomimicry to Active Motion to Materials Healing

The science and technologies of emulsion droplets have been a long‐term focus of extensive research endeavors for their practical utility across a breadth of industries, including pharmaceutical products, oil recovery processes, and the food sciences. However, with advances in materials chemistry and characterization tools, new emerging areas are arising with a focus on “smart droplets”. The versatility of emulsion droplets across is based on their ability to partition and create isolated systems with properties defined by the liquid–liquid interface, while preparative routes allow manipulation of droplet size, stability, and encapsulated contents. As described in this article, significant efforts are being devoted to creating new types of droplets by “activating” this interface through the incorporation of reactive structures that trigger droplet response to applied or environmental stimuli (e.g., pH, temperature, salt, or external fields). Moreover, parallels between droplets and live cells inspire efforts to conceive systems that resemble biological motifs or that can produce cellular behaviors that imitate biology (e.g., swarming, communication, or motion). Here, the authors highlight recent advances in smart droplets, with emphasis on organic, polymer, and/or particle surfactants that give rise to inter‐droplet communication (via aggregation, fusion, division, or mass transfer), droplet vehicles for controlled delivery, autonomous droplet motion, and tunable emulsion inversion. Especially emphasized is the macromolecular design to produce reactive and functional surfactants, which are crucial to responsive droplet behavior and their underlying mechanisms. More generally, the exquisite interplay between materials science and biology inspires the review of this research area that provides unique opportunities for insight and inspiration into the capabilities of new droplet designs.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry

Graph deep learning models, which incorporate a natural inductive bias for atomic structures, are of immense interest in materials science and chemistry. Here, we introduce the Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry. Built on top of the popular Deep Graph Library (DGL) and Python Materials Genomics (Pymatgen) packages, MatGL is designed to be an extensible “batteries-included” library for developing advanced model architectures for materials property predictions and interatomic potentials. At present, MatGL has efficient implementations for both invariant and equivariant graph deep learning models, including the Materials 3-body Graph Network (M3GNet), MatErials Graph Network (MEGNet), Crystal Hamiltonian Graph Network (CHGNet), TensorNet and SO3Net architectures. MatGL also provides several pre-trained foundation potentials (FPs) with coverage of the entire periodic table, and property prediction models for out-of-box usage, benchmarking and fine-tuning. Finally, MatGL integrates with PyTorch Lightning to enable efficient model training.

chemistry↗
DOE OSTI2025

32 examples of LLM applications in materials science and chemistry: towards automation, assistants, agents, and accelerated scientific discovery

Abstract Large language models (LLMs) are reshaping many aspects of materials science and chemistry research, enabling advances in molecular property prediction, materials design, scientific automation, knowledge extraction, and more. Recent developments demonstrate that the latest class of models are able to integrate structured and unstructured data, assist in hypothesis generation, and streamline research workflows. To explore the frontier of LLM capabilities across the research lifecycle, we review applications of LLMs through 32 total projects developed during the second annual LLM hackathon for applications in materials science and chemistry, a global hybrid event. These projects spanned seven key research areas: (1) molecular and material property prediction, (2) molecular and material design, (3) automation and novel interfaces, (4) scientific communication and education, (5) research data management and automation, (6) hypothesis generation and evaluation, and (7) knowledge extraction and reasoning from the scientific literature. Collectively, these applications illustrate how LLMs serve as versatile predictive models, platforms for rapid prototyping of domain-specific tools, and much more. In particular, improvements in both open source and proprietary LLM performance through the addition of reasoning, additional training data, and new techniques have expanded effectiveness, particularly in low-data environments and interdisciplinary research. As LLMs continue to improve, their integration into scientific workflows presents both new opportunities and new challenges, requiring ongoing exploration, continued refinement, and further research to address reliability, interpretability, and reproducibility.

Computer Science↗
DOE OSTI2025

Suppressed paramagnetism in amorphous Ta 2 O 5 − x oxides and its link to superconducting-qubit performance

Amorphous-oxide layers in thin-film capacitors are linked to reduced transmon-qubit T 1 coherence times. Ta -based capacitors outperform Nb -based ones, suggesting that amorphous Ta 2 O 5 − x is less lossy than Nb 2 O 5 − x . We investigate the microscopic features of these amorphous oxides using ab initio molecular dynamics and density functional theory, revealing the origins of the superior performance of Ta 2 O 5 − x . We establish that oxygen deficiency is less likely to occur in amorphous Ta 2 O 5 − x than in Nb 2 O 5 − x for 0 ≤ x ≤ 0.25 and that for a given oxygen deficiency x , metal Ta — Ta bond formation is enhanced. Such bonds, which are accommodated by structural flaws in the amorphous network, capture electrons better than in amorphous Nb 2 O 5 − x . These thermochemical differences quench or highly suppress magnetic moments in amorphous Ta 2 O 5 − x and eliminate a potential source of quasiparticles and magnetic flux noise. We also show that hyperfine couplings between Nb nuclei and local magnetic moments in Nb 2 O 5 − x can form “two-level systems” (TLSs) or “two-level fluctuators” with energy splittings of 100–1000 MHz or higher. This reveals a TLS mechanism in amorphous Nb 2 O 5 − x oxide layers that is likely inactive in Ta 2 O 5 − x . Our work provides a fundamental understanding of the materials chemistry and limitations imposed by native oxides of superconducting qubits that can be used to guide materials selection and processing.

Pritchard, P. Graham [Northwestern U.] (ORCID:0000↗
DOE OSTI2025

Super-expansive thermo-reversible interstitial solid solution of nanocrystal superlattices with mesogens

Designing superlattices of nanocrystals to mimic and extend the properties of atomic crystals has been a long-standing motivation in materials chemistry. Interstitial solid solutions, such as steel, are well-studied atomic lattices in which mobile components move among the interstices. These materials exhibit unique properties, including reversible structural changes and phase transitions. Interstitial solid solutions possess unique dynamic structures and reversible responses, which motivate the creation of their colloidal equivalents. Here, in this study, we report a fully thermo-reversible colloidal interstitial solid solution by combining liquid crystals and nanocrystals functionalized with promesogenic ligands. Mesogen molecules fill and diffuse among the interstices of a superlattice, resulting in a super-large thermal expansivity. The approach uses a modular design of interparticle interactions, allowing control of interparticle distance, microstructure and transition between crystallographic forms.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2026

First-Principles Evaluation of Proton Hopping in Tetrahedral Oxide Motifs

Proton-conducting oxides (PCOs) are important materials used as ionic conductors for energy conversion technologies. Existing research efforts on PCO optimization and discovery generally focus on complex perovskite-based oxides that require doping and alloying to engineer oxygen deficiency and high proton conductivity. However, the variety of chemical compositions and coordination environments in oxides poses challenges for efficient materials design. In this computational study, we construct a database of simplified motifs to elucidate the relationship between fundamental materials chemistry and proton kinetics. Specifically, we focus on the zincblende crystal structure as a proxy for tetrahedral metal–oxide (M–O) coordination environments. We systematically quantified the effects of cation type, oxidation states, and M–O bond lengths on the proton hopping barrier, and found that strong M–O bonds and metal cations with large and variable oxidation states (e.g., Mo 6+ , V 5+ ) lead to smaller proton hopping barriers. By mapping the candidate cations and their preferred bond geometries onto materials databases such as the Inorganic Crystal Structure Database (ICSD) and Materials Project, we identified real materials containing the corresponding metal–oxide units. In general, we observed good agreement between the calculated proton hopping barriers obtained in real crystal structures and those predicted by our motif database. We also discuss the limitations of our model and possible future extensions to improve its predictive capabilities. Overall, our model provides a first step for the rational design and quick screening of energy-efficient PCOs.

organic↗
DOE OSTI2026

Analysis of attenuation data from the decommissioned ZIon unit 1 reactor pressure vessel beltline weld

In order to examine the attenuation of radiation damage through the thickness of an irradiated reactor pressure vessel (RPV), four segments were acquired from the Zion Unit 1 power plant RPV after the plant was decommissioned. The Zion Unit 1 RPV Beltline Weld Segment 1 was cut into seven blocks, consisting of five base metal and two beltline welds from the high fluence region of the segment. Through-wall test specimens were machined and tested. Specimens included those used for Charpy impact, Master Curve fracture toughness testing, and chemical analysis. The observed through-thickness ductile-to-brittle transition temperatures in the beltline weld deviated significantly from the expected behavior based on the attenuation of fast fluence as a function of depth into the RPV. Beginning at the inside surface, the 41-J Charpy transition temperature was either flat or slightly increasing until the ¾ -T location. The results of a simple, model-based analysis of the Zion beltline weld material that included the irradiation conditions and material chemistry were generally consistent with industry trend curves and the standard attenuation model, rather than the observed data. Although there was no archive material from the RPV available to permit measurement of the unirradiated properties, fracture toughness specimens fabricated from archive surveillance weld were used to obtain an estimate of the initial through-thickness values of the Charpy transition temperature. The Charpy shifts obtained using this approach were similarly in disagreement with the predictions of the US NRC Regulatory Guide 1.99, Rev. 2. However, testing of irradiated Charpy specimens taken from the RPV following post-irradiation annealing (10 hr. at 500 °C) provided a quite different estimate of the unirradiated properties which improved the agreement between the inferred through-thickness Charpy shifts and exponential attenuation model included in Regulatory Guide 1.99/2. In conclusion, the analysis of the Zion data and data obtained in previous post-mortem examinations of decommissioned RPVs indicates that more work is needed to understand the through-thickness properties of RPV materials in order to properly assess through-wall damage attenuation.

Charpy impact↗
DOE OSTI2025

A Perspective on Pathways Toward Commercial Sodium‐Ion Batteries

Lithium-ion batteries (LIBs) have been widely adopted in the automotive industry, with an annual global production exceeding 1000 GWh. Despite their success, the escalating demand for LIBs has created concerns on supply chain issues related to key elements, such as lithium, cobalt, and nickel. Sodium-ion batteries (SIBs) are emerging as a promising alternative due to the high abundance and low cost of sodium and other raw materials. Nevertheless, the commercialization of SIBs, particularly for grid storage and automotive applications, faces significant hurdles. This perspective article aims to identify the critical challenges in making SIBs viable from both chemical and techno-economic perspectives. First, a brief comparison of the materials chemistry, working mechanisms, and cost between mainstream LIB systems and prospective SIB systems is provided. The intrinsic challenges of SIBs regarding storage stability, capacity utilization, cycle stability, calendar life, and safe operation of cathode, electrolyte, and anode materials are discussed. Furthermore, issues related to the scalability of material production, materials engineering feasibility, and energy-dense electrode design and fabrication are illustrated. Finally, promising pathways are listed and discussed toward achieving high-energy-density, stable, cost-effective SIBs.

25 ENERGY STORAGE↗
DOE OSTI2025

Understanding Oxide–Metal Interactions During Hot Isostatic Pressing to Diffusion Bond Aluminum Alloy 6061 Plates

The interaction between Mg, Si, and Al 2 O 3 during hot isostatic pressing diffusion bonding of aluminum alloy 6061 (AA6061) plates was investigated through thermodynamic calculations and experimental microstructural characterization. Thermodynamic calculations as functions of temperature, pressure, and composition revealed that the interaction among Mg, Si, and Al 2 O 3 yields Mg 2 Si and either MgO + Al or MgAl 2 O 4 + Al, facilitating the reduction of Al 2 O 3 and allowing Al/Al metallic bonds to form. Total pressure variation had a negligible influence on the oxygen partial pressure, and consequently, the reaction product formation. Oxygen partial pressure variation as a function of temperature and initial amount of Al 2 O 3 determined the formation of either MgO or MgAl 2 O 4 . Experimental Hot Isostatic Pressure (HIP) bonding at 723 K and 833 K under a constant pressure of 1017 atm documented the cooling rate-dependent formation of β–Mg 2 Si precipitates. High-resolution transmission electron microscopy imaging and selected area electron diffraction patterns verified the formation of β–Mg 2 Si and MgO at the interface but did not detect MgAl 2 O 4 . In conclusion, findings from this study clarify the role of thermochemical interactions in oxide disruption and bonding mechanisms during HIP diffusion bonding of AA6061 and provide guidance for optimizing joining processes for monolithic nuclear fuel assemblies.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Thermal decomposition of neptunyl ammonium nitrate: mechanistic insights and structural characterization of the Np 2 O 5 intermediate phase

Neptunium (Np) possesses a rich and unique chemistry that often diverges from other actinide elements yet remains relatively underexplored compared with the other light actinides. A resurgence of interest in Np has been spurred by the application of 237 Np for plutonium-238 ( 238 Pu) production for use in radioisotope thermoelectric generators (RTGs), necessitating evaluation of Np chemical reactions and materials. The work presented here studied the thermal decomposition of neptunyl ammonium nitrate (NH 4 Np VI O 2 (NO 3 ) 3 ) for synthesis of neptunium dioxide (NpO 2 ), which is the target material used for production of 238 Pu. Additionally, structural characterization of the intermediate solid Np pentoxide (Np 2 O 5 ) was performed. Advanced solid-state characterization techniques, including simultaneous thermal analysis (STA), powder X-ray diffraction (pXRD), Raman spectroscopy, and density functional theory (DFT) modeling have been combined to study the reaction pathways. Analysis revealed that NH 4 Np VI O 2 (NO 3 ) 3 thermally decomposes to a proposed neptunyl nitrate intermediate, followed by Np 2 O 5 and finally NpO 2 , all within the temperature range of 150 °C–600 °C. Further characterization of the pentoxide intermediate provided the first Raman spectra of pure-phase Np 2 O 5 and associated DFT modeling confirmed Raman peak assignments for this phase. These findings provide mechanistic information to advance production of the critical radioisotope 238 Pu and advance the state of knowledge on Np materials chemistry using modern characterization techniques.

Lawson, Kathryn M. [Oak Ridge National Laboratory ↗
DOE OSTI2026

ASU’s DAC polymer-enhanced cyanobacterial bioproductivity (AUDACity)

ASU’s DAC polymer-enhanced cyanobacterial bioproductivity (AUDACity) project aims to demonstrate a novel, scalable method for removing carbon dioxide (CO 2 ) directly from ambient air and delivering it to cyanobacterial cultures to produce commodity biofuel, mid-value protein for supplements, and high value phycocyanin (PC), a natural blue colorant (Figure A). This approach uses low-cost, reusable anion exchange polymers embedded in modular mesh packets, which capture CO 2 during drying cycles when exposed to ambient air, and release concentrated CO 2 into aqueous cultivation systems. The project addresses a critical challenge in energy research needed for developing sustainable, economically viable methods of Direct Air Capture (DAC) that can be integrated with bio-based systems for fuel and chemical production. AUDACity contributes to scientific understanding by integrating materials chemistry, cyanobacterial biology, and system engineering to create a distributed CO 2 delivery platform. Key insights have emerged around the design of biocompatible sorbents, optimization of CO 2 capture-release cycles, and durability of packet-based delivery systems under outdoor conditions. Notably, the team has synthesized and tested a range of polymer sorbents, identified mechanisms of material degradation and fouling, and advanced both lab- and pilot-scale cultivation systems to evaluate performance. From a technical and economic standpoint, AUDACity shows promise for achieving cost-effective CO 2 capture and delivery into aqueous media and biofuel production. Preliminary techno-economic analysis (TEA) indicates that the DAC system based on current performance can reach $\$$680/tonne CO 2 delivered into aqueous solution; with reasonable improvements to sorbent lifetime, sorbent capacity, reducing water uptake the approach could reach $\$$66/tonne by avoiding the need for energy-intensive sorbent regeneration and CO 2 compression, making it more feasible for decentralized deployment. With these costs for CO 2 and by extracting and selling high-value PC ($\$$50/kg) and mid-value protein supplement ($\$$6/kg), the remaining biomass can be hydrothermally treated into biofuel for $\$$2.50/gallon, and would support a small first-of-a-kind biorefinery capable of producing 500 barrels per day of biofuel. The project offers meaningful public benefits by advancing carbon removal technologies that are low-energy, modular, and adaptable to non-arable land and brackish water use. It aligns with national goals to develop advanced biotechnology and supports future pathways for bio-based fuels and products. By enabling direct coupling of CO 2 transfer into aqueous medium and biological carbon utilization, AUDACity lays the groundwork for effective algae cultivation without wasteful CO 2 delivery and is a promising and innovative solution for low-carbon fuel and bioproduct generation contributing to a vigorous bioeconomy.

09 BIOMASS FUELS↗
DOE OSTI2025

Suppressing Ordering in Equiatomic Fe-Co via Laser Powder Bed Fusion

Neutron and electron diffraction was employed to evaluate the effectiveness of thermal management strategies in suppressing the formation of the equilibrium-ordered B2 (CsCl) phase in equiatomic binary Fe-Co specimens fabricated by laser powder bed fusion additive manufacturing. Specimens with a tensile dogbone geometry were fabricated using various combinations of process parameters (laser power and raster speed) and thermal management strategies (no support struts, struts only in the top gauge section, and struts throughout the gauge section). Diffraction results demonstrate that the rapid solidification during PBF-L effectively minimized B2 formation, with laser power and effective scan velocity having no significant impact on the degree of ordering. Furthermore, the inclusion of support struts in the top grip region had no perceivable impact on ordering, whereas specimens with support struts in the gauge region exhibited no detectable ordering. Results are discussed in the context of thermal finite element analysis predictions.

Laser Material Processing↗