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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
NASA NTRS2015

Solar Proton Transport Within an ICRU Sphere Surrounded by a Complex Shield: Ray-trace Geometry

A computationally efficient 3DHZETRN code with enhanced neutron and light ion (Z is less than or equal to 2) propagation was recently developed for complex, inhomogeneous shield geometry described by combinatorial objects. Comparisons were made between 3DHZETRN results and Monte Carlo (MC) simulations at locations within the combinatorial geometry, and it was shown that 3DHZETRN agrees with the MC codes to the extent they agree with each other. In the present report, the 3DHZETRN code is extended to enable analysis in ray-trace geometry. This latest extension enables the code to be used within current engineering design practices utilizing fully detailed vehicle and habitat geometries. Through convergence testing, it is shown that fidelity in an actual shield geometry can be maintained in the discrete ray-trace description by systematically increasing the number of discrete rays used. It is also shown that this fidelity is carried into transport procedures and resulting exposure quantities without sacrificing computational efficiency.

Slaba, Tony C.↗
NASA NTRS2020

Disruptive Technologies and Their Putative Impacts Upon Society and Aerospace- Entering The Virtual Age

Developments in technology over the recent decades have been extraordinary. They include the IT, bio, nano, and now quantum and energetics technology arenas and their many combinatorial interactions and impacts. In the main, these are at the frontiers of the small and in a combinational, synergistic feeding frenzy with each other. They fall under the broad category of Disruptive Technologies and have greatly altered society. The outlook for the runout of these and other technology developments augers mid-term to later alterations in components of the human existence theorem, including the requirement to work for our living and our physiological makeup and longevity (Ref 1). The IT revolution began in the 1950s with the development of solid-state electronics. The biologics revolution began later in the 1960s and 1970s with DNA and genomics, and the nano revolution in the 1990s with self-forming nano systems and carbon nanotubes. Quantum technology is now developing rapidly, aided by enabling nano systems, and the energetics revolution is providing ever more efficient and less expensive renewable energy sources. The IT revolution has produced improvements of an astounding eleven orders of magnitude in computing speed since the late 1950s. As we shift from silicon to biological, optical, nano, molecular, and atomic computing, improvements of some 4 orders of magnitude are evidently possible from either optical or DNA computing [Refs 2and 3], then there are combinatorials. Then there is quantum computing, under development worldwide for an increasing number of applications and proffering phenomenal capabilities. The current fastest computers are considerably beyond human brain speed. Machine intelligence is developing well after decades of inadequate machine capability, now no longer the case, and a detour into expert systems. Researchers in machine intelligence are now pursuing deep learning approaches using neural nets, which are proving to be extremely useful. Some believe the frontier of potential human-level machine intelligence may be found in biomimetics and brain-emulation approaches. There is even a possibility of “emergence”—i.e., when the machine intelligence is complex enough that it “wakes up,” as when human intelligence emerged via evolution during the million-plus years of the hunter-gatherer epoch [ Ref 4]. In fact, some posit that human intelligence can be improved upon and is only a cul-de-sac of what is conceivable. The IT revolution has produced massive changes in human society and economics—from the Internet, enabling the rapid expansion of knowledgeability (and even what is knowable), to an increasingly pervasive trend of “tele-everything.” The extraordinary compilation, storage, and availability of truly massive amounts of information could, when combined with AI and under the mantra of “big data,” greatly improve many of our technical and commercial processes and their content including elucidating new heuristic governing laws.

Dennis M. Bushnell↗
NASA NTRS2022

SatNet: A Benchmark for Satellite Scheduling Optimization

Satellites provide essential services such as networking and weather tracking, and the number of near-earth and deep space satellites are expected to grow rapidly in the coming years. Communications with terrestrial ground stations is one of the critical functionalities of any space mission. Satellite scheduling is a problem that has been scientifically investigated since the 1970s. A central aspect of this problem is the need to consider resource contention and satellite visibility constraints as they require line of sight. Due to the combinatorial nature of the problem, prior solutions such as linear programs and evolutionary algorithms require extensive compute capabilities to output a feasible schedule for each scenario. Machine learning based scheduling can provide an alternative solution by training a model with historical data and generating a schedule quickly with model inference. We present SatNet, a benchmark for satellite scheduling optimization based on historical data from the NASA Deep Space Network. We propose formulation of the satellite scheduling problem as a Markov Decision Process and use reinforcement learning (RL) policies to generate schedules. The nature of constraints imposed by SatNet differ from other combinatorial optimization problems such as vehicle routing studied in prior literature. Our initial results indicate that RL is an alternative optimization approach that can generate candidate solutions of comparable quality to existing state-of-the-practice results. However, we also find that RL policies overfit to the training dataset and do not generalize well to new data, thereby necessitating continued research on reusable and generalizable agents.

Wilson, Brian↗
DOE OSTI2021

Experimental Fabrication of Porous Additive Manufactured Material

Nuclear industries can benefit from materials that can perform well mechanically and thermally in high temperature and corrosion environments. Functionally graded materials (FGM) are materials manufactured with complex spatial, structural, and chemical compositions, creating components predesigned with tailored microstructural and mechanical properties. Porosity-graded FGMs are designed to introduce pores into a component’s structure as a mechanism to increase bulk or surface material performance, such as stress accommodation and negative thermal conductivity. Additive manufacturing (AM) techniques such as laser energy net shape (LENS) and wire arc additive manufacturing (WAAM), excel in high energy density, point-to-point metal deposition, and present in combination with other combinatorial approaches, exciting methods to manufacture porous FGM components. This project is also examining plasma jet printing as another means for method evaluations for smaller scale graded components. This study explores AM FGM process parameters and combinatorial fabrication methodologies for stainless steel components functionally graded in porosity levels. Preliminary characterization results are provided to attest the feasibility of the combinational fabrication techniques.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Self-Driving Microscopy for AI/ML-Enabled Physics Discovery and Materials Optimization

Materials are the bedrock of economy and foundation for all real-world technologies. The viability of space travel, grid energy storage, solar to fuels conversion, methane removal, and photovoltaic energy solutions hinge on the discovery and optimization of novel materials and rapid scaling toward manufacturing. The last 20 years have seen an exponential growth in the theoretical predictive capability for crystalline materials and small molecules. However, it is only in the last five years that we have seen the rapid expansion of high-throughput synthesis enabled by laboratory robotics and microfluidics, as well as a resurgence of combinatorial synthesis (Abolhasani and Kumacheva 2023; Epps and Abolhasani 2021; Jiang et al. 2022; Rajan 2008; Soldatov et al. 2021; Szymanski et al. 2023). Combinatorial synthesis, microfluidics, and ultimately dip-pen megalibraries have demonstrated the ability to “write” multicomponent nanomaterials at high throughput scale, generating millions of material examples in the 3D, 4D, and 5D composition spaces (Chen et al. 2016, 2019; Jibril et al. 2022).

36 MATERIALS SCIENCE↗
DOE OSTI2026

CDRL: Certification-Driven Reinforcement Learning for Neutrino Flavor Model Discovery

Many scientific discovery problems require searching combinatorial hypothesis spaces under complex domain constraints. Reinforcement learning (RL) offers a promising approach, but existing methods rely on scalar rewards that provide limited information about why candidate solutions fail, leading agents to repeatedly explore invalid regions. We introduce Certification-Driven Reinforcement Learning (CDRL), a framework that leverages structured feedback from symbolic reasoning tools. When a candidate violates domain constraints, these tools produce certificates identifying the actions responsible for failure. CDRL converts these certificates into reusable constraints that eliminate classes of invalid solutions and guide exploration toward valid regions. We evaluate CDRL on neutrino flavor model discovery in theoretical particle physics, where the hypothesis space exceeds $10^{26}$ possible models, and compare it with the state-of-the-art RL approach previously used for this task. Across three theory spaces, CDRL achieves up to 1.95$\times$ higher valid model rates and up to 6.33$\times$ higher neutrino model rates while evaluating up to 4$\times$ fewer candidates. We further extract 40 interpretable rules from search trajectories using a post-hoc decision-tree framework and show that reusing them as soft constraints yields gains of up to 2$\times$ in valid model rates and 3$\times$ in neutrino model discovery across all three theory spaces. These results suggest that CDRL uncovers reusable structure in combinatorial search spaces and provides a general framework for scientific model discovery.

Jha, Piyush [Georgia Tech., Atlanta; Georgia Tech]↗
DOE OSTI2022

Codesigning Alloy Compositions of CdSe y Te 1− y Absorbers and Mg x Zn 1− x O Contacts to Increase Solar Cell Efficiency

Thin‐film solar cells such as CdTe are a major commercial photovoltaic technology, with more than 25 GW installed worldwide and levelized costs of electricity competitive with fossil fuels. Further progress may result from integrating CdSe y Te 1− y absorbers with Mg x Zn 1− x O contacts, but the device efficiency is difficult to maximize due to coupled dependence on chemical composition of both alloys. Herein, a high‐throughput approach is demonstrated to codesign chemical compositions in alloyed Mg x Zn 1− x O/CdSe y Te 1− y thin‐film solar cells, using combinatorial libraries of PV devices with orthogonal composition gradients in CdSe y Te 1− y absorbers and Mg x Zn 1− x O contacts. It is found that the solar cell performance is a strong and coupled function of both elemental compositions, with efficiency up to 17.7% ( V OC = 836 mV, fill factor = 69%, J SC = 30.6 mA cm −2 ) at atomic compositions of Mg/(Mg + Zn) ≈18% and average Se/(Se + Te) ≈4%. These performance trends among >100 devices are explained by >100 ns lifetime of photoexcited charge carriers at the Mg x Zn 1− x O/CdSe y Te 1− y interface where strong Se accumulation is also observed. This study reports the optimal compositions of the commercially relevant Mg x Zn 1− x O/CdSe y Te 1− y solar cells and demonstrates a general approach to codesigning performance of alloyed thin‐film solar cells and other optoelectronic devices.

14 SOLAR ENERGY↗
DOE OSTI2020

High-throughput characterization of Ag–V–O nanostructured thin-film materials libraries for photoelectrochemical solar water splitting

Ag–V–O thin-film materials libraries, with both composition (Ag 22-77 V 23-78 O x ) and thickness (123–714 nm) gradients were fabricated using combinatorial reactive magnetron co-sputtering aiming on establishing relations between composition, structure, and functional properties. As-deposited libraries were annealed in air at 300 °C for 10 h. High-throughput characterization methods of composition, structure and functional properties were used to identify photoelectrochemically active regions. The phases AgV 6 O 15 , Ag 2 V 4 O 11 , AgVO 3 , and Ag 4 V 2 O 7 were observed throughout the composition gradient. The photoelectrochemical properties of Ag–V–O films are dependent on composition and morphology. An enhanced photocurrent density (~300–554 μA/cm 2 ) was obtained at 30 to 45 at.% Ag along the thickness gradient. Thin films of these compositions show a nanowire morphology, which is an important factor for the enhancement of photoelectrochemical performance. The photoelectrochemically active regions were further investigated by high-throughput synchrotron-X-ray diffraction and transmission electron microscopy (Ag 32 V 68 O x ) which confirmed the presence of Ag 2 V 4 O 11 as the dominating phase along with the minor phases AgV 6 O 15 and AgVO 3 . This enhanced photoactive region shows bandgap values of ~2.30 eV for the direct and ~1.87 eV for the indirect bandgap energies. Finally, the porous nanostructured films improve charge transport and are hence of interest for photoelectrochemical water splitting.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Discovery of amivantamab (JNJ-61186372), a bispecific antibody targeting EGFR and MET

A bispecific antibody (BsAb) targeting the epidermal growth factor receptor (EGFR) and mesenchymal–epithelial transition factor (MET) pathways represents a novel approach to overcome resistance to targeted therapies in patients with non–small cell lung cancer. In this study, we sequentially screened a panel of BsAbs in a combinatorial approach to select the optimal bispecific molecule. The BsAbs were derived from different EGFR and MET parental monoclonal antibodies. Initially, molecules were screened for EGFR and MET binding on tumor cell lines and lack of agonistic activity toward MET. Hits were identified and further screened based on their potential to induce untoward cell proliferation and cross-phosphorylation of EGFR by MET via receptor colocalization in the absence of ligand. After the final step, we selected the EGFR and MET arms for the lead BsAb and added low fucose Fc engineering to generate amivantamab (JNJ-61186372). The crystal structure of the anti-MET Fab of amivantamab bound to MET was solved, and the interaction between the two molecules in atomic details was elucidated. Amivantamab antagonized the hepatocyte growth factor (HGF)-induced signaling by binding to MET Sema domain and thereby blocking HGF β-chain—Sema engagement. The amivantamab EGFR epitope was mapped to EGFR domain III and residues K443, K465, I467, and S468. Furthermore, amivantamab showed superior antitumor activity over small molecule EGFR and MET inhibitors in the HCC827-HGF in vivo model. Based on its unique mode of action, amivantamab may provide benefit to patients with malignancies associated with aberrant EGFR and MET signaling.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

GraMeR: Gra ph Me ta R einforcement learning for multi-objective influence maximization

Influence maximization (IM) is a combinatorial problem of identifying a subset of seed nodes in a network (graph), which when activated, provide a maximal spread of influence in the network for a given diffusion model and a budget for seed set size. IM has numerous applications such as viral marketing, epidemic control, sensor placement and other network-related tasks. However, its practical uses are limited due to the computational complexity of current algorithms. Recently, deep reinforcement learning has been leveraged to solve IM in order to ease the computational burden. However, there are serious limitations in current approaches, including narrow IM formulation that only consider influence via spread and ignore self-activation, low scalability to large graphs, and lack of generalizability across graph families leading to a large running time for every test network. In this work, we address these limitations through a unique approach that involves: (1) Formulating a generic IM problem as a Markov decision process that handles both intrinsic and influence activations; (2)incorporating generalizability via meta-learning across graph families. There are previous works that combine deep reinforcement learning with graph neural network, but this work solves a more realistic IM problem and incorporates generalizability across graphs via meta reinforcement learning. Extensive experiments are carried out in various standard networks to validate performance of the proposed Graph Meta Reinforcement learning (GraMeR) framework. Finally, the results indicate that GraMeR is multiple orders faster and generic than conventional approaches when applied on small to medium scale graphs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Strength mechanisms and tunability in Al-Ce-Mg ternary alloys enabled by additive manufacturing

Al-Ce-based alloys are promising candidates for additive manufacturing (AM) due to their hot-cracking resistance and because they do not require heat treatment to obtain precipitation strengthening. Rapid solidification rates enabled by AM methods can lead to enhanced mechanical properties; however, the strengthening mechanisms over large composition ranges were unclear. Here, combinatorial synthesis by directed-energy deposition (DED) and hardness measurements were used to rapidly map the composition-dependent strength of the ternary Al-Ce-Mg system. Tensile testing and microstructure characterization of selected compositions were performed to elucidate the compositional dependence of the strengthening mechanisms. Al 11 Ce 3 precipitates were present in all cases, and the maximum hardness (1.25 GPa) was measured for the Al-8Ce-10Mg composition. A combination of (i) Hall-Petch strengthening, based on the FCC-matrix-phase cell size; (ii) particle strengthening, based on Al 11 Ce 3 volume fraction and size; and (iii) solid-solution strengthening, based on Mg composition of the matrix phase, were used to account for the measured strengths. Vickers hardness is shown to correlate well with ultimate tensile strength in these alloys, highlighting the value of surface-based techniques for rapid screening.

36 MATERIALS SCIENCE↗
DOE OSTI2025

High-throughput multimodal exploration of a nanocrystalline Cu-Ag library

Sputter-deposited, nanocrystalline Cu-Ag thin films produced across a broad compositional and deposition-parameter space were evaluated to unravel the process-structure-property relationships important for creating hard, conductive electrical contacts and coatings. Combinatorial deposition involving pulsed direct current magnetron sputtering of elemental targets enabled swift examination of nearly the full range of alloy compositions and a relevant portion of deposition atomistics. Several high-throughput characterization modalities were employed to evaluate the chemistry, structure, and properties of the films. The resultant hardness, modulus, film density, crystal texture, and resistivity were analyzed in terms of key deposition characteristics (incident atom kinetic energy and incidence angle) predicted by binary-collision, kinematic Monte Carlo simulations. The study revealed improved hardness, parabolic resistivity dependence on composition, and compositional and process dependencies of film tarnishing. The results are discussed in the context of variations in microstructure and film density. Transmission electron microscopy and X-ray diffraction demonstrate several forms of compositional variation including solute segregation to grain boundaries as well as periodic, intragranular compositional modulations. Annealing of a Cu-rich alloy film exhibiting grain boundary segregation showed that this as-deposited, compositional variation is not stable above 100 °C. Finally, the Cu-Ag system is shown to have potential for hard, conductive, tarnish-resistant and room temperature-stable nanocrystalline thin films across the composition space.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Metabolic flux optimization of iterative pathways through orthogonal gene expression control: Application to the β-oxidation reversal

Balancing relative expression of pathway genes to minimize flux bottlenecks and metabolic burden is one of the key challenges in metabolic engineering. This is especially relevant for iterative pathways, such as reverse β-oxidation (rBOX) pathway, which require control of flux partition at multiple nodes to achieve efficient synthesis of target products. Here, we develop a plasmid-based inducible system for orthogonal control of gene expression (referred to as the TriO system) and demonstrate its utility in the rBOX pathway. Leveraging effortless construction of TriO vectors in a plug-and-play manner, we simultaneously explored the solution space for enzyme choice and relative expression levels. Remarkably, varying individual expression levels led to substantial change in product specificity ranging from no production to optimal performance of about 90% of the theoretical yield of the desired products. We obtained titers of 6.3 g/L butyrate, 2.2 g/L butanol and 4.0 g/L hexanoate from glycerol in E. coli, which exceed the best titers previously reported using equivalent enzyme combinations. Since a similar system behavior was observed with alternative termination routes and higher-order iterations, we envision our approach to be broadly applicable to other iterative pathways besides the rBOX. Here, considering that high throughput, automated strain construction using combinatorial promoter and RBS libraries remain out of reach for many researchers, especially in academia, tools like the TriO system could democratize the testing and evaluation of pathway designs by reducing cost, time and infrastructure requirements.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

High-Throughput Exploration of Lithium-Alloy Protection Layers for High-Performance Lithium-Metal Batteries

To realize high specific capacity Li-metal batteries, a protection layer for the Li-metal anode is needed. We are carrying out combinatorial screening of Li-alloy thin films as the protection layer which can undergo significant lithiation with minimum change in volume and crystal structure. In this paper, we have fabricated lithium-free binary alloy thin film composition spreads of Co 1–x Sn x on Cu layers on Si substrates. The crystallinity of the thin films was tuned by varying the deposition temperature followed by electrochemical lithiation to form Li-alloy ternary thin films. Synchrotron diffraction is used as the main tool to investigate the crystallinity of the films before and after lithiation. Co 3 Sn 2 alloy thin films are found to exhibit significant lithium uptake capacity while maintaining its structural integrity, and are thus a good candidate of the Li-metal protection layer.

25 ENERGY STORAGE↗
DOE OSTI2024

Single-Atom Manganese-Based Catalysts for the Oxidative Dehydrogenation of Propane

Combinatorial screening of 150 supported metal oxide (manganese and additives) catalysts was carried out via a high-throughput synthesis platform and parallel reactors for the oxidative dehydrogenation (ODH) of propane to propylene. Specifically, an organomanganese (0.05-2.5 Mn atoms/nm 2 ) complex was grafted on metal oxide supports (Al 2 O 3 , SiO 2 , TiO 2 , and ZrO 2 ) premodified with either Lewis acid (Al, Ti, Zn, and Zr) or redox-active (Cu, Cr, Ga Ni, V) additives at various surface coverages (25, 50, and 75%). Catalysts were characterized by high-resolution transmission electron microscopy (HRTEM), X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), Raman spectroscopy, and UV-vis spectroscopy. Catalysts 0.05 Mn/V(50%)/Al 2 O 3 and 0.05 Mn/Ni(50%)/ZrO 2 showed the highest combined propane conversion and propylene selectivities (31/41% and 15/85%), with excellent stability at 500 degrees C for 25 h. The presence of Ni in Mn/Ni/ZrO 2 resulted in a 6-fold increase in turnover frequency (TOF) over the Mn/ZrO 2 . HRTEM identified single Mn atoms after 500 degrees C heat treatment. For the Mn/Ni/ZrO 2 system, Mn was incorporated into the support lattice due to the similar ionic radius of Mn 2+ and Zr 4+ , which was also enhanced by the presence of Ni. For the Mn/V/Al 2 O 3 system, highly active MnO was prevalent as observed by Raman. Both V and Mn contributed to an increase in mutual dispersion, but both species remained on the surface. Finally, it is proposed that the highly dispersed atom and interactions between Mn with either Ni or V are responsible for the ODH performance and stability.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Exploring the First High-Entropy Thin Film Libraries: Composition Spread-Controlled Crystalline Structure

Thin films of two types of high-entropy oxides (HEOs) have been deposited on 76.2 mm Si wafers using combinatorial sputter deposition. In one type of the oxides, (MgZnMnCoNi)O x , all the metals have a stable divalent oxidation state and similar cationic radii. In the second type of oxides, (CrFeMnCoNi)O x , the metals are more diverse in the atomic radius and valence state, and have good solubility in their sub-binary and ternary oxide systems. Therefore, the resulting HEO thin films were characterized using several high-throughput analytical techniques. The microstructure, composition, and electrical conductivity obtained on defined grid maps were obtained for the first time across large compositional ranges. The crystalline structure of the films was observed as a function of the metallic elements in the composition spreads, that is, the Mn and Zn in (MgZnMnCoNi)O x and Mn and Ni in (CrFeMnCoNi)O x . The (MgZnMnCoNi)O x sample was observed to form two-phase structures, except single spinel structure was found in (MgZnMnCoNi)O x over a range of Mn > 12 at. % and Zn < 44 at. %, while (CrFeMnCoNi)O x was always observed to form two-phase structures. Composition-controlled crystalline structure is not only experimentally demonstrated but also supported by density function theory calculation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Influence of Hydrogen and Oxygen on the Structure and Properties of Sputtered Magnesium Zirconium Oxynitride Thin Films

Nitride materials with mixed ionic and covalent bonding character and resulting good charge transport properties are attractive for optoelectronic devices. Recently, Mg-based ternary nitride materials were found to have large dielectric constants and high absorption coefficients with bandgaps appropriate for photovoltaic applications. However, their degenerate carrier concentrations still hinder their possible applications in solar cells and related optoelectronic devices. Therefore, further understanding and engineering of the parameters controlling the material properties of these ternary nitrides is highly desirable. In this paper we report that the structural, optical and electrical properties of magnesium zirconium oxynitride (MZNO) thin films synthesized by combinatorial sputtering with a wide range of cation compositions can be affected by incorporation of oxygen and hydrogen. Excess oxygen improved the crystallinity of MZNO thin films whereas hydrogen attracted oxygen and formed Mg-rich oxide layers at the grain boundaries which in turn reduced the conductivity. On the other hand, optical properties are more sensitive to the composition – both cation and anion ratios – rather than the presence of hydrogen. Compared to cation-stoichiometric MZNO (10 19 –10 20 cm -3 ), substantial reduction of carrier concentration down to ~10 14 cm -3 was achieved under Mg-rich conditions by supplying hydrogen during growth. Photoluminescence measurements showed that the films prepared with hydrogen were optoelectronically active. Overall, this study demonstrates that the material properties of MZN thin films can be significantly influenced by incorporation of oxygen and hydrogen.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Discovering exceptionally hard and wear-resistant metallic glasses by combining machine-learning with high throughput experimentation

Lack of crystalline order in amorphous alloys, commonly called metallic glasses (MGs), tends to make them harder and more wear-resistant than their crystalline counterparts. However, finding inexpensive MGs is daunting; finding one with enhanced wear resistance is a further challenge. Relying on machine learning (ML) predictions of MGs alone requires a highly precise model; however, incorporating high-throughput (HiTp) experiments into the search rapidly leads to higher performing materials even from moderately accurate models. Here, we exploit this synergy between ML predictions and HiTp experimentation to discover new hard and wear-resistant MGs in the Fe–Nb–B ternary material system. Several of the new alloys exhibit hardness greater than 25 GPa, which is over three times harder than hardened stainless steel and only surpassed by diamond and diamond-like carbon. This ability to use less than perfect ML predictions to successfully guide HiTp experiments, demonstrated here, is especially important for searching the vast Multi-Principal-Element-Alloy combinatorial space, which is still poorly understood theoretically and sparsely explored experimentally.

36 MATERIALS SCIENCE↗