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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Atomistic modeling of metal–nonmetal interphase boundary diffusion

Atomistic computer simulations are applied to investigate the atomic structure, thermal stability, and diffusion processes in Al–Si interphase boundaries as a prototype of metal–ceramic interfaces in composite materials. Some of the most stable orientation relationships between the phases found in this work were previously observed in epitaxy experiments. Here, a non-equilibrium interface can transform to a more stable state by a mechanism that we call interface-induced recrystallization. Diffusion of both Al and Si atoms in stable Al–Si interfaces is surprisingly slow compared with diffusion of both elements in Al grain boundaries but can be accelerated in the presence of interface disconnections. A qualitative explanation of the sluggish interphase boundary diffusion is proposed. Atomic mechanisms of interphase boundary diffusion are similar to those in metallic grain boundaries and are dominated by correlated atomic rearrangements in the form of strings and rings of collectively moving atoms.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Reversing inverse Hall-Petch and direct computation of Hall-Petch coefficients

A 2D bicrystal atomistic model of dislocation transmission through a Σ11<101>{131} symmetric-tilt grain boundary reveals details of Hall-Petch breakdown in the single dislocation regime in Al, Ni, and Cu. Partly based on a previous study, this research determines the stress required for a single dislocation to be transmitted through the Σ11 boundary and finds that single dislocation transmission typically deviates from Hall-Petch behavior because the leading partial dislocation becomes trapped in the boundary and emits glissile grain boundary disconnections that cause boundary sliding deformation at stresses well below boundary transmission stresses. Thus, mechanisms of inverse Hall-Petch, namely grain boundary shear and sliding, are shown to operate in lieu of grain boundary transmission in the single dislocation regime. However, by controlling the applied stresses, inverse Hall-Petch can be reversed and typical Hall- Petch grain boundary transmission regained. This, in turn, allows the direct computation of Hall-Petch coefficients. Further, a second dislocation on the same slip plane preserves typical Hall-Petch behavior for Al and Ni and does not lead to boundary sliding events, thus implying a critical grain size for Hall-Petch breakdown based on the pileup model.

36 MATERIALS SCIENCE↗
DOE OSTI2025

The structure and migration of twin boundaries in tetragonal β -Sn: An application of machine learning based interatomic potentials

Although atomistic simulations have contributed significantly to our understanding of twin boundary structure and migration in metals and alloys with hexagonal close packed (HCP) crystal structures, few direct atomistic studies of twinning have been conducted for other types of low symmetry materials, in large part due to a lack of reliable interatomic potentials. In this work, we examine twin boundary structure and migration in a tetragonal material, β-Sn, comparing high resolution Transmission Electron Microscopy (TEM) images of deformation twins in β-Sn to the results of direct atomistic simulations using multiple interatomic potentials. ML-based potentials developed in this work are found to give results consistent with our experimental data, revealing faceted twin boundary structures formed by the nucleation and motion of twinning disconnections. We use bicrystallographic methods in combination with atomistic simulations to analyze the structure, energy and shear coupled migration of observed twin facets in β-Sn. In analogy to Prismatic-Basal (PB/BP) interfaces in HCP metals, we discover low energy asymmetric Prismatic-A-plane (PA/AP) interfaces important to twin growth in β-Sn. Finally, a Moment Tensor Potential (MTP) and Rapid Artificial Neural Network (RANN) interatomic potential suitable for studying twinning and phase transformations in Sn are made publicly available as part of this work.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Benchtop assessment of sealing efficacy and breathability of additively manufactured (AM) face masks

The onset of the 2019 novel coronavirus disease (COVID-19) led to a shortage of personal protective equipment (PPE), medical devices, and other medical supplies causing many stakeholders and the general public alike to turn to additive manufacturing (AM) as a stopgap when normally accessible devices were not available. However, without a method to test these AM constructs, there continued to be a disconnect between AM suppliers and the community’s needs. The objective of this study was to characterize the pressure drop and leakage of four different publicly available AM face mask models with two filter material combinations, as well as to investigate the impact of frame modification techniques including the use of foam strips and hot-water face forming to improve fit when the masks are donned on manikin head forms. AM face mask frame designs were downloaded from public repositories during the early stages of the COVID-19 pandemic. AM face masks were fabricated and tested on manikin head forms within a custom chamber containing dry aerosolized NaCl. Pressure drops, particle penetration, and leakage were evaluated for various flow rates and NaCl concentrations. Results indicated that filter material combination and frame modification played a major role in the overall performance of the AM face masks studied. Filter material combinations showed improved performance when high filtration fabric was used, and the cross-sectional area of the fabric was increased. AM frame modifications appeared to improve AM face mask leakage performance by as much as 69.6%.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Crystallographic dependence of CO 2 hydrogenation pathways over HCP-Co and FCC-Co catalysts

We report efficient conversion of CO 2 is of great significance for sustainable supply of chemicals and fuels. While Co-based catalysts are known to be effective for CO hydrogenation in Fischer-Tropsch synthesis, they work very differently in CO 2 hydrogenation. This study reveals a crystallographic dependence of reaction pathways for CO 2 hydrogenation on Co catalyst showing a new type of structure sensitivity and structure-activity-selectivity relationship for CO 2 conversion to chemicals and fuels. The experimental work on CO 2 conversion including steady-state isotopic transient kinetic analysis (SSITKA) using 13 C-labeled CO 2 shows a preferential CH 4 formation over HCP-Co but dominant CO formation over FCC-Co. The density functional theory calculations indicate that CO 2 does dissociate directly into chemisorbed CO * and O * on both HCP-Co and FCC-Co, but the CO * intermediates on HCP-Co prefer to be hydrogenated to form CH 4 whereas the CO* on FCC-Co preferentially desorb to form CO. The significantly altered adsorption strength of CO * due to the presence of chemisorbed O * and CO 2 * species on the catalyst surface is responsible for the mechanistic disconnection in product selectivity between the CO 2 and CO hydrogenation over Co catalysts. This study also shows that the addition of K to Co diminishes the direct impact of Co crystal structure, but improves the selectivity to C 2 + hydrocarbons along with higher CO 2 conversion. This seems to result from another pathway originating from HCOO* intermediate from bonding interaction of surface Co atoms with carbon in CO 2 , leading to the formation of CH x * whose coupling subsequently give rises to C 2 + products. The present study sheds new light into the crystallographic structural sensitivity of CO 2 hydrogenation towards the rational design of more selective catalysts for CO 2 conversion.

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

OR-AGENT framework – Architecting electrified heavy-duty drayage applications

The widespread adoption of zero-emission vehicles in heavy-duty (HD) commercial freight transportation faces considerable technoeconomic challenges. For heavy-duty trucks, ensuring high uptime, cost parity with diesel, and safety standards is especially critical as these vehicles operate over long distances with heavy loads, where any downtime or off-nominal behaviors significantly impacts logistics, productivity, and the total cost of ownership. Unlike traditional diesel refueling, BEV charging infrastructure must be co-optimized with vehicle deployment, operational demands, and grid capacity to ensure cost-effective and reliable freight operations. However, the lack of a standardized ownership and service model has led to a fragmented approach—where commercial vehicle operators may invest in, own, and maintain both vehicle/batteries and charging/energy infrastructure. This disconnect may exclude energy service providers from the equation, forcing fleet operators to explore ‘behind-the-fence’ energy solutions that increase capital investment, operational downtime, overhead costs, and, in some cases, net carbon emissions. To address these issues, this study introduces OR-AGENT (Optimal Regional Architecture Generation for Efficient National Transport), a comprehensive modeling framework that integrates powertrain architectures, charging infrastructures, and energy backbone systems into a cohesive strategy. In this paper, OR-AGENT is applied to develop an interconnected systems architecture for energy efficiency and resiliency enhancement of heavy-duty drayage vehicles at the Port of Savannah, GA. This framework showcases an interconnected systems approach to electrifying heavy-duty drayage vehicles at the Port of Savannah, GA. The study assessed BEVs with 400–1200 kWh battery capacities, accounting for seasonal variations in weather and freight routing. A diverse charging mix (150 kW–1250 kW) was evaluated alongside grid capacity constraints, cost, and carbon intensity analysis, leading to the development of a strategic microgrid/Distributed Energy Resources (DER) deployment architecture to ensure a reliable and sustainable transition. However, the findings also highlight the need for alternative zero-emission solutions for remaining trips, such as larger batteries, electrified roadways, hydrogen powertrains, or net-zero emission fuels. In conclusion, the findings are incorporated into a Total Cost of Ownership (TCO) model to identify optimal architectures for an interconnected electrified ecosystem.

Commercial vehicles↗
DOE OSTI2022

Trait drift in microalgae and applications for strain improvement

Microalgae are increasingly used to generate a wide range of commercial products, and there is growing evidence that microalgae-based products can be produced sustainably. However, industrial production of microalgal biomass is not as developed as other biomanufacturing platform technologies. In addition, results of bench-scale research often fail to translate to large-scale or mass production systems. This disconnect may result from trait drift and evolution occurring, through time, in response to unique drivers in each environment, such as cultivation regimes, weather, and pests. Moreover, outdoor and indoor cultivation of microalgae has the potential to impose negative selection pressures, which makes the maintenance of desired traits a challenge. In this context, this review sheds the light on our current understanding of trait drift and evolution in microalgae. Here, we delineate the basics of phenotype plasticity and evolution, with a focus on how microalgae respond under various conditions. In addition, we review techniques that exploit phenotypic plasticity and evolution for strain improvement in view of industrial commercial applications, highlighting associated advantages and shortcomings. Finally, we suggest future research directions and recommendations to overcome unwanted trait drift and evolution in microalgae cultivation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Hybrid geological modeling: Combining machine learning and multiple-point statistics

Accurately modeling and constructing a geologically realistic subsurface model remains an outstanding problem as the morphology controls the flow behaviors. Particularly, one of the pattern-based methods, namely cross-correlation based simulation, has been proved to be an effective way to reconstruct a realistic model, at both small and large scales. However, conditioning to point data in the large-scale problems is still a crucial issue in these algorithms, since there is always a trade-off between the quality of the realizations and the degree of point data reproduction. Specifically, it is not practical to build a training image (TI) which includes all the possibilities and variabilities. Therefore, finding a pattern that can represent the point data and, at the same time, preserving the connectivities is difficult. This leads to producing highly-connected realizations with a significant mismatch or poor models with a reasonable degree of point data reproduction. To accurately reproduce the densely distributed hard data, pixel-based methods can also produce some unrealistic artifacts around the hard data. In this paper, to overcome this challenge, however, we use pattern-based methods as they often produce more disconnected geobodies when dealing with dense hard data, and proposed a hybrid algorithm using the pattern-based methods and convolutional neural network (CNN). The trained CNN model is utilized to improve the quality of conditioning to point data for the original realizations generated by the pattern-based algorithm. As such, the mismatch locations are identified, and the same regions are used in the training of CNN to mimic the procedure through which a missing region can be filled. To evaluate the performance of the proposed hybrid algorithm, it is tested on cases with different dimensions and different numbers of facies. Then, the newly improved realizations are compared with the initial realizations generated by the pattern-based algorithm. The comparison is also conducted by the flow simulation test. And it indicates that the proposed hybrid algorithm can better reproduce the point data, while the connectivities are better preserved.

58 GEOSCIENCES↗
DOE OSTI2025

Intrepid MCMC: Metropolis-Hastings with exploration

In engineering examples, one often encounters the need to sample from unnormalized distributions with complex shapes that may also be implicitly defined through a physical or numerical simulation model, making it computationally expensive to evaluate the associated density function. For such cases, MCMC has proven to be an invaluable tool. Random-walk Metropolis Methods (also known as Metropolis-Hastings (MH)), in particular, are highly popular for their simplicity, flexibility, and ease of implementation. However, most MH algorithms suffer from significant limitations when attempting to sample from distributions with multiple modes (particularly disconnected ones). Here, in this paper, we present Intrepid MCMC - a novel MH scheme that utilizes a simple coordinate transformation to significantly improve the mode-finding ability and convergence rate to the target distribution of random-walk Markov chains while retaining most of the simplicity of the vanilla MH paradigm. Through multiple examples, we showcase the improvement in the performance of Intrepid MCMC over vanilla MH for a wide variety of target distribution shapes. We also provide an analysis of the mixing behavior of the Intrepid Markov chain, as well as the efficiency of our algorithm for increasing dimensions. A thorough discussion is presented on the practical implementation of the Intrepid MCMC algorithm. Finally, its utility is highlighted through a Bayesian parameter inference problem for a two-degree-of-freedom oscillator under free vibration.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2023

The protist Aurantiochytrium has universal subtelomeric rDNAs and is a host for mirusviruses

Viruses are the most abundant biological entities in the world's oceans, where they play important ecological and biogeochemical roles. Metagenomics is revealing new groups of eukaryotic viruses, although disconnected from known hosts. Among these are the recently described mirusviruses, which share some similarities with herpesviruses.50 years ago, "herpes-type" viral particles were found in a thraustochytrid member of the labyrinthulomycetes, a diverse group of abundant and ecologically important marine eukaryotes, but could not be further characterized by methods then available. Long-read sequencing has allowed us to connect the biology of mirusviruses and thraustochytrids. We sequenced the genome of the genetically tractable model thraustochytrid Aurantiochytrium limacinum ATCC MYA-1381 and found that its 26 linear chromosomes have an extraordinary configuration. Subtelomeric ribosomal DNAs (rDNAs) found at all chromosome ends are interspersed with long repeated sequence elements denoted as long repeated-telomere and rDNA spacers (LORE-TEARS). We identified two genomic elements that are related to mirusvirus genomes. The first is a ~300-kbp episome (circular element 1 [CE1]) present at a high copy number. Strikingly, the second, distinct, mirusvirus-like element is integrated between two sets of rDNAs and LORE-TEARS at the left end of chromosome 15 (LE-Chr15). Similar to metagenomically derived mirusviruses, these putative A. limacinum mirusviruses have a virion module related to that of herpesviruses along with an informational module related to nucleocytoplasmic large DNA viruses (NCLDVs). CE1 and LE-Chr15 bear striking similarities to episomal and endogenous latent forms of herpesviruses, respectively, and open new avenues of research into marine virus-host interactions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

BrainXcan identifies brain features associated with behavioral and psychiatric traits using large-scale genetic and imaging data

Advances in brain MRI have enabled many discoveries in neuroscience. Case-control comparisons of brain MRI features have highlighted potential causes of psychiatric and behavioral disorders. However, due to the cost and difficulty of collecting MRI data, most studies have small sample sizes, limiting their reliability. Furthermore, reverse causality complicates interpretation because many observed brain differences are the result rather than the cause of the disease. Here we propose a method (BrainXcan) that leverages the power of large-scale genomewide association studies (GWAS) and reference brain MRI data to discover new mechanisms of disease etiology and validate existing ones. BrainXcan tests the association with genetic predictors of brain MRI-derived features and complex traits to pinpoint relevant brain-wide and region-specific features. Requiring only genetic data, BrainXcan allows us to test a host of hypotheses on mental illness, across many MRI modalities, using public data resources. For example, our method shows that reduced axonal density across the brain is associated with schizophrenia risk, consistent with the disconnectivity hypothesis. We also find that the hippocampus volume is associated with schizophrenia risk, highlighting the potential of our approach. Taken together, our results show the promise of BrainXcan to provide insights into the biology of GWAS traits.

Association study↗
DOE OSTI2025

Cataloging US state policy patterns towards microgrid deployment

Frequent extreme weather events have called for rigorous and timely efforts for alternative non-wire solutions. These efforts are getting more widespread to offer a perfect alternative as the conventional grid becomes progressively less resilient. One of these solutions is microgrids that can disconnect from the grid and offer grid resilience during an outage. While this technology is still finding its footing in the industry, states across the US are employing policy patterns and forms of instruments to support its deployment. This study includes a systemic review of the US by conducting a binary analysis of all 50 states (including Washington D.C, excluding other US territories) using seven variables. The results show four major policy approaches to microgrids: i) supporting microgrids through a definitive legislative activity leading to further policy action; ii) direct efforts from the public utilities commissions without a concrete legislative push; iii) initiatives from institutions other than the commissions; and lastly, iv) self-initiated community and private consumer efforts. The results help understand what policy instruments are being used in each of these patterns to support this niche technology that still faces regulatory challenges.

Furqan, Maham↗
DOE OSTI2020

Transport of coexisting Ni-Cu sulfide liquid and silicate melt in partially molten peridotite

Transport of coexisting sulfide and silicate melts in partially molten peridotite contributes to the redistribution of chalcophile elements within the upper mantle as well as the genesis of magmatic Ni-Cu sulfide deposits, but has not been investigated systematically. Using laboratory experiments, theoretical calculations, and X-ray synchrotron microtomography, this study documents the topology and considers controls on the extraction of two immiscible liquids during partial melting of mantle peridotite. Under hydrostatic conditions, the measured dihedral angle at silicate melt-mineral-mineral junctions is 13.7–21.3°. Silicate melt is distributed along grain edges forming incompletely interconnected melt channels that disconnected by some dead ends at melt fractions ~7–9 vol%. Application of theoretically predicted permeability (k ~10 -14 -10 -16 m 2 ) permits estimation of the extraction velocity of silicate melt of 0.7-11.1 μm/day within a single interconnected melt channel. In the absence of silicate melt, isolated sulfide droplets (3.77 vol%) show a sulfide-olivine-olivine dihedral angle of 91.5–101.3°. However, in the presence of silicate melt, sulfide droplets (average size ~2.53±2.14μm, 1σ) are partially surrounded by silicate melt and stranded in triple junctions or melt pockets due to the limitation of the smallest dimension (0.3μm) of melt channels. Thus, the extraction of sulfide liquid is highly restricted by these dead ends and the smallest dimension of melt channels during porous flow of silicate melt. In contrast, during large-strain shear deformation (strain ~1.6–2.5), initially stranded sulfide droplets were elongated and extracted with silicate melt into liquid-rich sheets with a length of several hundred microns, constantly oriented at 14.3±4.5°to the shear plane and antithetic to the shear direction. The angle is lower than that (18-30°) of those sheets containing sulfide liquid only, indicating that silicate melt dominates liquid-rich sheets. Driven by stress, silicate melt-dominated liquid-rich sheets open the appropriately oriented grain boundaries between silicate minerals, thus providing an efficient pathway for the extraction of sulfide liquid during deformation. When such sheet-like channels remain open, sulfide droplets (> millimeter-scale) can be potentially mobile through high strain domains of the upper mantle, contributing to the addition of chalcophile elements and the fertilization of the lithospheric mantle.

high P-T experiment↗
DOE OSTI2020

Impact analysis of DERs on bulk power system stability through the parameterization of aggregated DER_a model for real feeders

With an ever increasing percentage of distributed energy resources (DERs) connected behind the meter in the distribution system, it is becoming increasingly important to equip transmission planners with the visibility of DER dynamic performance in distribution system. Not having visibility of the disconnection of DERs with the occurrence of transmission events, could result in an erroneous view of the stability of bulk power system. Here, in this paper, a parameterized aggregated model (DER_a) serves as a representation of the distribution-level dynamics of real residential feeders, which is used for analysis of bulk power system stability. The parameters are obtained by executing dynamic Monte Carlo simulations. Faults are then induced at the substation level causing the DERs to trip which subsequently enables the parameterization of the low and high voltage breakpoints (v l0 , v l1 , v h0 , and v h1 ) of the DER_a model’s partial voltage trip block. These parameters are then utilized to study the effectiveness of the DER_a model to represent the behavior of the aggregated DERs’ response and their impact on the bulk power system. The case study shows the ability of the positive-sequence DER_a model to provide an accurate estimation of DERs that are susceptible to trip due to 3-$\phi$ and 1-$\phi$ faults of transmission.

14 SOLAR ENERGY↗
DOE OSTI2025

Designing a repository in domal salt: The influence of design variants in different modelling environments

To understand the long-term environmental impact of disposing radioactive waste of in a deep geological repository and to optimise its design, performance assessments are used. In this study, four teams (COVRA, GRS, Quintessa, and DOE) modified the previously developed generic repository of DECOVALEX task F2 to identify commonalities and differences between the teams for specific changes in repository design. The teams tested six design modifications: (1) Replacing concrete abutments with run-of-mine salt; (2) Replacing the salt seal with a concrete abutment and using run-of-mine salt instead for the two concrete abutments in each drift seal; (3) Halving the size of the infrastructure area; (4) Using run-of-mine salt instead of gravel for backfilling the infrastructure area; (5) Disposal of spent nuclear fuel without the POLLUX-10 containers (6); Lower initial saturation of the spent nuclear fuel and vitrified waste disposal drifts. Despite different modelling strategies used, models agreed that a smaller infrastructure area has a limited effect on radionuclide transport. Responses to the absence of the two concrete abutments in each seal, the use of single large concrete abutments (200 m each), or the use of run-of-mine salt in the infrastructure area differ between teams due to differing modelling assumptions. Based on these results, the estimated efficacy of containment depends strongly on the model assumptions of each team. More specifically, it appears to depend on the compaction model used and therefore on the backfill material used in different areas of the repository. However, the drift seal appears to be a critical design element in all models, effectively limiting radionuclide transport by hydrologically disconnecting sections of the repository. Additional beneficial design choices include the use of dry salt in disposal drifts to limit radionuclide transport and reducing the infrastructure area costs and minimizing host rock damage.

DECOVALEX↗
DOE OSTI2022

A machine learning pipeline for membrane segmentation of cryo-electron tomograms

We describe how to use several machine learning techniques organized in a learning pipeline to segment and identify cell membrane structures from cryo electron tomograms. These tomograms are difficult to analyze with traditional segmentation tools. The learning pipeline in our approach starts from supervised learning via a special convolutional neural network trained with simulated data. It continues with semi-supervised reinforcement learning and/or a region merging technique that tries to piece together disconnected components belonging to the same membrane structure. A parametric or non-parametric fitting procedure is then used to enhance the segmentation results and quantify uncertainties in the fitting. Domain knowledge is used in generating the training data for the neural network and in guiding the fitting procedure through the use of appropriately chosen priors and constraints. We demonstrate that the approach proposed here works well for extracting membrane surfaces in two real tomogram datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

A case study of high-temperature polyetherimide film capacitor fabrication

Capacitors, as the basic building block of power electronics and electrical systems, are the major constraint of the increasingly integrated power systems that require new capable polymer dielectric films operating at higher temperatures >125 °C. Despite tremendous research efforts on the lab scale, there remains a considerable barrier and knowledge basis for converting polymeric films to functioning capacitors desired in actual applications. Here, the authors tackled the polyetherimide (PEI) film scale up issues and developed various engineering processes for film de-wrinkling, optimal metallization, static elimination, and capacitor fabrication improvement. The authors discovered various fabrication challenges such as the electrostatic charge, metallization scheme, winding tension, and end electrode disconnects. To fabricate a high yield of PEI capacitor bobbins, it is necessary to utilize static eliminators (radioactive ionizers), moderately thick aluminum metallization (15–30 Ω/sq), and winding tension (60–100 g). It is also effective to evaluate capacitance, dielectric loss, equivalent series resistance, and thermal cycling stability of capacitors. This work sheds some light on converting PEI films and other polymeric films to capacitors on a scale up fabrication effort. The practical learning from film handling to capacitor fabrication in this work provided the necessary knowledge for manufacturing high-temperature polar film capacitors.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Chemical composition based machine learning model to predict defect formation in additive manufacturing

With a goal of exploiting additive manufacturing to improve the manufacturing of existing reactor materials, we developed a chemical composition-based machine learning model to predict the printability of any given alloy in laser powder bed fusion (L-PBF) using experimental data from peer-reviewed literature. We defined printability as the ability to avoid defects like cracking, balling, porosity, and lack of fusion, that are caused by thermal stresses (during solidification or liquation), molten pool disintegration into disconnected small beads or lack of heat input respectively. Our models predict the tendency of balling defect formation and porosity percentage for a given composition, under a given set of processing conditions. To predict the likelihood of balling defect, three models: a random forest classifier, a gradient boost regressor and a neural network were trained on a dataset containing both traditional alloys and high entropy alloys. The neural network model showed the highest accuracy of 92.3 % in predicting the balling defect formation. A random forest regressor, gradient boost regressor and neural network were trained and tested on a dataset of various alloys to predict porosity. The random forest regressor showed the best predictions with an R 2 score of 0.97. The models also revealed the relative importance of the input descriptors on defect-formation tendency. Of particular significance was the identification of carbon as an important element in determining the occurrence of balling and percent porosity in alloys like steel, as well as being moderately important to the percentage porosity in other alloys as well as steel. Manganese was also identified as a key descriptor for the percentage of porosity in steel and other alloys. Manganese’s low thermal conductivity and consistent presence in the dataset is the likely cause for its contribution. Carbon’s role is attributable to its relatively high specific heat and high melting temperature. In conclusion, our model serves as a swift, chemistry-based tool to design experiments and find modified compositions better suited for additive manufacturing.

36 MATERIALS SCIENCE↗