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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Encoding trade-offs and design toolkits in quantum algorithms for discrete optimization: coloring, routing, scheduling, and other problems

Challenging combinatorial optimization problems are ubiquitous in science and engineering. Several quantum methods for optimization have recently been developed, in different settings including both exact and approximate solvers. Addressing this field of research, this manuscript has three distinct purposes. First, we present an intuitive method for synthesizing and analyzing discrete (i.e., integer-based) optimization problems, wherein the problem and corresponding algorithmic primitives are expressed using a discrete quantum intermediate representation (DQIR) that is encoding-independent. This compact representation often allows for more efficient problem compilation, automated analyses of different encoding choices, easier interpretability, more complex runtime procedures, and richer programmability, as compared to previous approaches, which we demonstrate with a number of examples. Second, we perform numerical studies comparing several qubit encodings; the results exhibit a number of preliminary trends that help guide the choice of encoding for a particular set of hardware and a particular problem and algorithm. Our study includes problems related to graph coloring, the traveling salesperson problem, factory/machine scheduling, financial portfolio rebalancing, and integer linear programming. Third, we design low-depth graph-derived partial mixers (GDPMs) up to 16-level quantum variables, demonstrating that compact (binary) encodings are more amenable to QAOA than previously understood. We expect this toolkit of programming abstractions and low-level building blocks to aid in designing quantum algorithms for discrete combinatorial problems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Kinematic flow from the flow of cuts

The wavefunction coefficients of conformally coupled scalars in power-law FRW cosmologies satisfy differential equations governed by a set of simple combinatorial rules known as the kinematic flow. In this paper we derive the kinematic flow, expressed using a set of differential forms referred to as the cut basis, from a geometric perspective, relying solely on the cosmological hyperplane arrangement and without invoking bulk physics. Each element of the cut basis corresponds to the positive geometry associated to an independent cut of the physical FRW-form and can be labeled by decorating (minors of) the truncated Feynman graph with an acyclic orientation. We provide a straightforward prescription to associate a logarithmic differential form to each element of the cut basis by considering its corresponding decorated graph. Moreover, we show that the residues of the physical FRW-form are canonical forms of certain graphical zonotopes labeled by the same set of decorated graphs. These zonotopes control the cut combinatorics -- flow of cuts -- of the physical FRW-form and the cut basis (by construction). Using the theory of relative twisted cohomology and intersection theory, we derive a closed form formula for the differential equations of the cut basis. We also introduce combinatorial rules that compute the kinematic differential of any basis element without explicit calculation. The combinatorics of our differential equations is a natural consequence of the flow of cuts and is equivalent (up to rescaling) to the kinematic flow for the recently studied time integral basis. In particular, our differential equations decouple into exponentially many sectors, one for each way of cutting a subset of edges of the graph.

General Relativity and Quantum Cosmology↗
DOE OSTI2020

Progress in Understanding the Origins of Excellent Corrosion Resistance in Metallic Alloys: From Binary Polycrystalline Alloys to Metallic Glasses and High Entropy Alloys

Some of the factors responsible for good corrosion resistance of select polycrystalline and emerging alloys in chloride solutions are discussed with a goal of providing some perspectives on the current status and future directions. Traditional metallic glass alloys, single phase high entropy alloys (HEAs), early metallic glasses, and high entropy metallic glasses are all emerging corrosion-resistant alloys (CRAs) that utilize traditional strategies for improved corrosion resistance as well as take advantage of some other novel beneficial attributes. These materials enjoy many degrees of freedom as far as choice of both composition and structure, providing great flexibility in the pursuit of superior corrosion resistance. The new materials depart from classical solvent-solute type polycrystalline binary or ternary alloys. Thus, such emerging materials provide significant opportunities to achieve even greater improvements in corrosion resistance in harsh environments. Several examples of the unique corrosion properties of selected materials in the context of modern theories of corrosion are discussed herein. Discussion is restricted to solid-solution binary or ternary polycrystalline alloys, several metallic glass alloys, and single phase HEAs. A common feature of many CRAs is that composition and microstructure often affect both passivity and resistance to localized corrosion that can be divided into initiation, stabilization, and propagation stages. Enormous complexities in protective oxide structures and chemistries and the large number of combinatorial possibilities in newer materials such as HEAs preclude trial-and-error approaches and perhaps even combinatorial experimental design. Computational materials methodologies will be required in the search for new corrosion-resistant alloys in these material classes. The search must consider the best scientific insights available regarding how major and minor alloy additions, as well as various microstructural attributes, contribute to corrosion mitigation. Additional scientific insights, as they emerge, will enable choices beyond the reliance on high concentrations of alloying elements that are known to affect passivity breakdown and pit stabilization. A challenge is to connect the “basic attributes” of an alloy with its properties. The strength of this connection will likely require new scientific principles enabling deep multiphysics insights in order to link feature(s) such as composition and metallurgical phases to the desired corrosion properties. Application of data informatics will likely also play a role given the plethora of variables that are important in corrosion and the difficulty in assessing all relationships. Here, the opportunity exists to accelerate the design of emerging materials for high corrosion resistance.

36 MATERIALS SCIENCE↗
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↗