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Results for “Glass-forming ability”

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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14 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Hybrid machine learning/physics-based approach for predicting oxide glass-forming ability

Predicting the liquid compositions that will vitrify at experimentally accessible quench rates remains one of the grand challenges in the field of condensed matter physics. This glass-forming ability can be quantified as the critical quench rate needed to suppress crystallization. Knowledge of this critical quench rate also informs which glass composition could be used for new applications. There have been several physical and empirical models presented in the literature to predict the critical quench rate/glass forming ability. These models range from those theoretically derived to those quantified only through experimental characterization. In this work, we instead propose a new method to calculate the critical quench rate using the recently developed toy landscape model combined with machine learning. The toy landscape model accesses the underlying physics that control the vitrification behavior by directly simulating the liquid thermodynamics and kinetics. In conclusion, the results are discussed in terms of industrial impact, physical insights, and how the glass science community can develop improved predictions of glass-forming ability.

Crystallization↗
DOE OSTI2021

Development of high B s Fe–Ni-based metal amorphous nanocomposite by optimization of glass-forming ability

Thermocalc simulations identified compositions with good glass-forming ability (GFA) by locating minima in liquidus temperature, and solidification range in (Fe 70 Ni 30 ) x (B-Si-Nb) 100-x alloys with x = 82% and x = 85%, increased compared to previously developed x = 80% alloys. 3 compositions in the x = 82% system and 1 in the x = 85% system were successfully cast as amorphous magnetic ribbon. Magnetic properties of as-cast alloys showed improvements in Curie temperature and saturation induction, especially for the 85% alloy. Crystallization behavior in the 85% alloy was determined to occur as a 2-step process. Annealing studies showed (Fe 70 Ni 30 ) 82 Nb 2 Si 0 B 16 and (Fe 70 Ni 30 ) 82 Nb 3 Si 0 B 15 alloys to exhibit low coercivity and high B s with annealing. TEM results show significant crystallization in as-cast ribbon. The (Fe 70 Ni 30 ) 85 Nb 0.5 Si 0 B 14.5 alloy as-cast has small, uniformly distributed grains, allowing a useful nanocrystalline alloy to be produced as cast. Lastly, the parameter ΔT xg , the temperature range in which the material can be thermomechanically processed, was in the range of 12–23 °C.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Evaluation of GlassNet for physics-informed machine learning of glass stability and glass-forming ability

Glassy materials form the basis of many modern applications, including nuclear waste immobilization, touch-screen displays, and optical fibers, and also hold great potential for future medical and environmental applications. However, their structural complexity and large composition space make design and optimization challenging for certain applications. Of particular importance for glass processing and design is an estimate of a given composition's glass-forming ability (GFA). However, there remain many open questions regarding the underlying physical mechanisms of glass formation, especially in oxide glasses. It is apparent that a proxy for GFA would be highly useful in glass processing and design, but identifying such a surrogate property has proven itself to be difficult. While glass stability (GS) parameters have historically been used as a GFA surrogate, recent research has demonstrated that most of these parameters are not accurate predictors of the GFA of oxide glasses. Here, in this work, we explore the application of an open-source pre-trained neural network model, GlassNet, that can predict the characteristic temperatures necessary to compute GS with reasonable performance and assess the feasibility of using these physics-informed machine learning (PIML)-predicted GS parameters to estimate GFA. In doing so, we track the uncertainties at each step of the computation—from the original ML prediction errors to the compounding of errors during GS estimation, and finally to the final estimation of GFA. While GlassNet exhibits reasonable accuracy on all individual properties, we observe a large compounding of error in the combination of these individual predictions for the PIML prediction of GS, finding that random forest models offer similar accuracy to GlassNet. We also break down the performance of GlassNet on different glass families and find that the error in GS prediction is correlated with the error in crystallization peak temperature prediction. Lastly, we utilize this finding to assess the relationship between top-performing GS parameters and GFA for two ternary glass systems: sodium borosilicate and sodium iron phosphate glasses. We conclude that to obtain true ML predictive capability of GFA, significantly more data needs to be collected.

36 MATERIALS SCIENCE↗
DOE OSTI2021

In Situ Scattering Studies of Crystallization Kinetics in a Phase-Separated Zr–Cu–Fe–Al Bulk Metallic Glass

In this work, thermal stability and the crystallization kinetics of a phase-separated Zr-Cu-Fe-Al bulk metallic glass were investigated using in situ high-energy synchrotron X-ray and neutron diffraction, as well as small-angle synchrotron X-ray scattering. It was revealed that this glass with excellent glass-forming ability possesses a two-step crystallization behavior. The crystalline products and their evolution sequence are more complicated than a homogeneous Zr-Cu-Al glass with average glass-forming ability. The experimental results indicate that a finely distributed nanometer-sized cubic Zr 2 Cu phase forms first and then transforms to a tetragonal Zr 2 Cu phase, while the matrix transforms to an orthorhombic Zr3Fe phase. The strength of the Zr-Cu-Fe-Al composite containing cubic Zr 2 Cu phase and glass matrix increases, and the plasticity also improves compared to the as-cast Zr-Cu-Fe-Al bulk metallic glass. Our results suggest that the formation of multiple and complex crystalline products would be the characteristics of the Zr-Cu-Fe-Al glass with better glass-forming ability. Our study may shed light on the synthesis of bulk-sized glass-nanocrystals composites of high strength and good plasticity.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Viscosity anomaly of a metallic glass-forming liquid under high pressure

Viscosity, as a critical property closely associated with the glass-forming ability of a liquid, has been extensively studied with varying temperatures. However, its pressure dependence has not been well explored yet due to experimental difficulties. Here, we measured the viscosity of a metallic glass-forming liquid, Zr 46 Cu 37.6 Ag 8.4 Al 8 , at pressures up to 6.1 GPa above the melting points by falling-sphere viscometry with ultrafast synchrotron x-ray imaging. Overall, the viscosity increases with pressure, while surprisingly, there is an abrupt drop between 3.2 GPa and 3.7 GPa, indicating the possible existence of a pressure-induced liquid-to-liquid transition. Pressure could change the short- and medium-range orders in the multicomponent glass-forming liquid, as suggested by the different crystalline outcomes after cooling to room temperature at high- and low-pressure ranges. In conclusion, our work extended the viscosity investigation of metallic glass-forming liquids to the high-pressure regime, which will expand our understanding of liquid-liquid transitions and metallic glass formation.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Development of High Bs Fe-Ni Based Metal Amorphous Nanocomposite by Optimization of Glass Forming Ability

A growing interest in electric vehicles challenges soft magnetic materials to improve efficiency and power density. Metal amorphous nanocomposites (MANCs) have lower coercivities and eddy current losses, allowing for greater efficiency and higher switching frequency. The later allows for high motor speeds and improved power density. However, commercially available and laboratory demonstrated MANCs have shortcomings. Fe-based MANCs, such as FINEMET have excellent magnetic properties, but mechanical properties limit their application in high speed electric motors (HSMs). Fe-Co based MANCs, such as HITPERM, have high saturation inductions and good mechanical properties, but elevated magnetostrictive losses. Co-based MANCs and more recent Fe-Ni based MANCs have low losses and good mechanical properties, but lower saturation inductions (1-1.2 T), that limit power density in HSMs. Recently, much work has explored improving saturation induction in MANCs by increasing the content of magnetic elements, which comes at the cost of glass-forming ability (GFA). These efforts where generally limited to trial and error testing. Additionally, all work to date has focused on Fe-based alloys. In this work, a method of using Thermocalc simulation to locate near-eutectic compositions has been applied to Fe-Ni based alloys. Minima in liquidus temperature and solidification range were found for a ternary composition range and used to identify compositions that retain good GFA as the percentage of magnetic elements is increased. The (Fe 70 Ni 30 ) x (B-Si-Nb) 100-x alloy system for x=82% and x=85% was explored by Thermocalc simulations. This is an increase in magnetic element content compared to previously developed x=80% alloys. 3 compositions in the x=82% system, and 1 alloy in the x=85% system where identified and successfully cast as amorphous ribbon. The amorphous nature of the ribbon was confirmed by a bend test and XRD. Magnetic testing was performed by PPMS to measure saturation induction and Curie temperature of the amorphous material. The Curie temperatures increased to 407-438 °C for the x=82% alloys, and 462 °C for the x=85% alloy. Saturation induction increased to 1.28-1.36 T for the x=82% alloy and 1.48 T for the x=85% alloy, which is a significant improvement over the ~370 °C Curie temperature and 1.2 T saturation of previous Fe-Ni alloy. Crystallization behavior was studied by XRD for the x=85% alloy, showing that crystallization process follows a 2-step process of Amorphous → BCC+FCC+Amorphous → BCC+FCC+Fe 3 B+Fe 23 B 6 . Post annealing magnetic properties were studied by strip testing, and saturation induction as high as 1.48 T was seen after optimal annealing. TEM was used to study structure of as cast and annealed material. 2 alloys with x=82% compositions were identified with an optimal crystalline size of 10-20 nm, while the x=85% alloy had this structure in the as cast state. This opens the possibility of using the alloy in the as cast state, without requiring annealing. In conclusion, alloys with good GFA and improved magnetic properties were identified by Thermocalc simulation.

Krimer, Yuval↗
DOE OSTI2024

Enhanced Radiation Damage Tolerance of Amorphous Interphase and Grain Boundary Complexions in Cu-Ta

Amorphous interfacial complexions are particularly resistant to radiation damage and have been primarily studied in alloys with good glass-forming ability, yet recent reports suggest that these features can form even in immiscible alloys such as Cu-Ta under irradiation. In this work, the mechanisms of damage production and annihilation due to primary knock-on atom collisions are investigated for amorphous interphase and grain boundaries in a Cu-Ta alloy using atomistic simulations. Amorphous complexions, in particular amorphous interphase complexions that separate Cu and Ta grains, result in less residual defect damage than their ordered counterparts. Stemming from the nanophase chemical separation in this alloy, the amorphous complexions exhibit a highly heterogeneous distribution of atomic excess volume, as compared to a good glass former like Cu-Zr. Complexion thickness, a tunable structural descriptor, plays a vital role in damage resistance. Thicker interfacial films are more damage-tolerant because they alter the defect production rate due to differences in intrinsic displacement threshold energies during the collision cascade. Overall, the findings of this work highlight the importance of interfacial engineering in enhancing the properties of materials operating in radiation-prone environments and the promise of amorphous complexions as particularly radiation damage-tolerant microstructural features.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Atomic and Electronic Structure in MgO–SiO 2

Understanding disordered structure is difficult due to insufficient information in experimental data. Here, we overcome this issue by using a combination of diffraction and simulation to investigate oxygen packing and network topology in glassy (g-) and liquid (l-) MgO–SiO 2 based on a comparison with the crystalline topology. We find that packing of oxygen atoms in Mg 2 SiO 4 is larger than that in MgSiO 3 , and that of the glasses is larger than that of the liquids. Moreover, topological analysis suggests that topological similarity between crystalline (c)- and g-(l-) Mg 2 SiO 4 is the signature of low glass-forming ability (GFA), and high GFA g-(l-) MgSiO 3 shows a unique glass topology, which is different from c-MgSiO 3 . We also find that the lowest unoccupied molecular orbital (LUMO) is a free electron-like state at a void site of magnesium atom arising from decreased oxygen coordination, which is far away from crystalline oxides in which LUMO is occupied by oxygen’s 3s orbital state in g- and l-MgO–SiO 2 , suggesting that electronic structure does not play an important role to determine GFA. We finally concluded the GFA of MgO–SiO 2 binary is dominated by the atomic structure in terms of network topology.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

The ab initio non-crystalline structure database: empowering machine learning to decode diffusivity

Non-crystalline materials exhibit unique properties that make them suitable for various applications in science and technology, ranging from optical and electronic devices and solid-state batteries to protective coatings. However, data-driven exploration and design of non-crystalline materials is hampered by the absence of a comprehensive database covering a broad chemical space. In this work, we present the largest computed non-crystalline structure database to date, generated from systematic and accurate ab initio molecular dynamics (AIMD) calculations. We also show how the database can be used in simple machine-learning models to connect properties to composition and structure, here specifically targeting ionic conductivity. These models predict the Li-ion diffusivity with speed and accuracy, offering a cost-effective alternative to expensive density functional theory (DFT) calculations. Furthermore, the process of computational quenching non-crystalline structures provides a unique sampling of out-of-equilibrium structures, energies, and force landscape, and we anticipate that the corresponding trajectories will inform future work in universal machine learning potentials, impacting design beyond that of non-crystalline materials. In addition, combining diffusion trajectories from our dataset with models that predict liquidus viscosity and melting temperature could be utilized to develop models for predicting glass-forming ability.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Composition-dependent fracture energy in metallic glasses

The interplay between metallic glasses (MGs) mechanical properties, fracture energy (G), and glass-forming ability (GFA) and their dependence on alloy composition remain poorly understood. Here, we perform molecular dynamics simulations to investigate the intrinsic composition-dependence of G in Cu x Zr 100-x MGs (x = 20, 30, 40, 50, 64). The results indicate that the value of G increases with Cu content. In addition, it is revealed that MGs with higher G values display higher Poisson’s ratio (ν) and GFA, suggesting a close correlation between fracture toughness, mechanical properties, and GFA. This correlation between G, ν and GFA can be understood based on the fragility (m) of supercooled liquids, which is directly related to the structural heterogeneity in MGs. Larger m values are related to dynamic slowdown and supercooled liquid stabilization, which enhance GFA and the formation of pronounced structural heterogeneity, comprised of loosely packed regions that favor β-relaxation and the activation of shear transformation zones. Those concurrently promote the expansion of the plastic zone at the crack tip, enhancing the observed value of G. Furthermore, these simulation results shed light on the intrinsic relationship between fracture toughness, mechanical properties, and alloy composition in MGs.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Glass formation during combinatorial sputtering in binary alloys

Glass formation is a complex phenomenon influenced by thermodynamic and kinetic aspects, which are often controlled by extrinsic contributions. While bulk metallic glasses are typically multicomponent alloys, binary alloys offer a simplified approach to studying glass formation. In this study, we fabricated 57 binary alloy systems through combinatorial sputtering, where each alloy system is represented in 66 different alloys. We developed an automated analysis to determine structure and composition using X-ray diffraction and energy-dispersive X-ray spectroscopy for over 3700 alloys. We found that ∼17 % of the alloys form glasses under the estimated cooling rate during sputtering of ∼10 8 K/s. Data analysis revealed that commonly used factors like atomic size ratio and heat of mixing are ineffective in predicting glass formation. However, the crystal structure mismatch of the alloys’ elements emerged as the strongest indicator of glass formation under sputtering conditions of binary alloys. Here, the differences in glass formation under slow cooling rates used for bulk glass formation and the here observed glass formation under rapid cooling rates are discussed.

Binary alloys↗
DOE OSTI2022

Characterization of the Interfacial Orientation and Molecular Conformation in a Glass-Forming Organic Semiconductor

The ability to control structure in molecular glasses has enabled them to play a key role in modern technology; in particular, they are ubiquitous in organic light-emitting diodes. While the interplay between bulk structure and optoelectronic properties has been extensively investigated, few studies have examined molecular orientation near buried interfaces despite its critical role in emergent functionality. Direct, quantitative measurements of buried molecular orientation are inherently challenging, and many methods are insensitive to orientation in amorphous soft matter or lack the necessary spatial resolution. To overcome these challenges, we use polarized resonant soft X-ray reflectivity (pRSoXR) to measure nanometer-resolved, molecular orientation depth profiles of vapor-deposited thin films of an organic semiconductor Tris(4-carbazoyl-9-ylphenyl)amine (TCTA). Our depth profiling approach characterizes the vertical distribution of molecular orientation and reveals that molecules near the inorganic substrate and free surface have a different, nearly isotropic orientation compared to those of the anisotropic bulk. Comparison of pRSoXR results with near-edge X-ray absorption fine structure spectroscopy and optical spectroscopies reveals that TCTA molecules away from the interfaces are predominantly planar, which may contribute to their attractive charge transport qualities. Buried interfaces are further investigated in a TCTA bilayer (each layer deposited under separate conditions resulting in different orientations) in which we find a narrow interface between orientationally distinct layers extending across ≈1 nm. Coupling this result with molecular dynamics simulations provides additional insight into the formation of interfacial structure. This study characterizes the local molecular orientation at various types of buried interfaces in vapor-deposited glasses and provides a foundation for future studies to develop critical structure–function relationships.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗