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Register a new userTuning the glass-forming ability of metallic glasses through energetic frustration
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The design of multi-functional BMGs is limited by the lack of a quantitative understanding of the variables that control the glass-forming ability (GFA) of alloys. Both geometric frustration (e.g. differences in atomic radii) and energetic frustration (e.g. differences in the cohesive energies of the atomic species) contribute to the GFA. We perform molecular dynamics simulations of binary Lennard-Jones mixtures with only energetic frustration. We show that there is little correlation between the heat of mixing and critical cooling rate $R_c$, below which the system crystallizes, except that $Delta H_{rm mix} < 0$. By removing the effects of geometric frustration, we show strong correlations between $R_c$ and the variables $epsilon_- = (epsilon_{BB}-epsilon_{AA})/(epsilon_{AA}+epsilon_{BB})$ and ${overline epsilon}_{AB} = 2epsilon_{AB}/(epsilon_{AA}+epsilon_{BB})$, where $epsilon_{AA}$ and $epsilon_{BB}$ are the cohesive energies of atoms $A$ and $B$ and $epsilon_{AB}$ is the pair interaction between $A$ and $B$ atoms. We identify a particular $f_B$-dependent combination of $epsilon_-$ and ${overline epsilon}_{AB}$ that collapses the data for $R_c$ over nearly $4$ orders of magnitude in cooling rate.
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We perform molecular dynamics simulations to compress binary hard spheres into jammed packings as a function of the compression rate $R$, size ratio $alpha$, and number fraction $x_S$ of small particles to determine the connection between the glass-forming ability (GFA) and packing efficiency in bulk metallic glasses (BMGs). We define the GFA by measuring the critical compression rate $R_c$, below which jammed hard-sphere packings begin to form random crystal structures with defects. We find that for systems with $alpha gtrsim 0.8$ that do not de-mix, $R_c$ decreases strongly with $Delta phi_J$, as $R_c sim exp(-1/Delta phi_J^2)$, where $Delta phi_J$ is the difference between the average packing fraction of the amorphous packings and random crystal structures at $R_c$. Systems with $alpha lesssim 0.8$ partially de-mix, which promotes crystallization, but we still find a strong correlation between $R_c$ and $Delta phi_J$. We show that known metal-metal BMGs occur in the regions of the $alpha$ and $x_S$ parameter space with the lowest values of $R_c$ for binary hard spheres. Our results emphasize that maximizing GFA in binary systems involves two competing effects: minimizing $alpha$ to increase packing efficiency, while maximizing $alpha$ to prevent de-mixing.
Various combinations of characteristic temperatures, such as the glass transition temperature, liquidus temperature, and crystallization temperature, have been proposed as predictions of the glass forming ability of metal alloys. We have used statistical approaches from machine learning to systematically explore a wide range of possible characteristic temperature functions for predicting glass forming ability in the form of critical casting diameter, $D_{max}$. Both linear and non-linear models were used to learn on the largest database of $D_{max}$ values to date consisting of 747 compositions. We find that no combination of temperatures for features offers a better prediction of $D_{max}$ in a machine learning model than the temperatures themselves, and that regression models suffer from poor performance on standard machine learning metrics like root mean square error (minimum value of $3.3 pm 0.1$ $mm$ for data with a standard deviation of 4.8 $mm$). Examination of the errors vs. database size suggest that a larger database may improve results, although a database significantly larger than that used here would likely be required. Shifting a focus from regression to categorization models learning from characteristic temperatures can be used to weakly distinguish glasses likely to be above vs. below our databases median $D_{max}$ value of 4.0 $mm$, with a mean F1 score of $0.77 pm 0.02$ for this categorization. The overall weak results on predicting $D_{max}$ suggests that critical cooling rate might be a better target for machine learning model prediction.
The effect of dopants on the metallic glass forming ability is usually considered based on analysis of changes in the liquid structure or thermodynamics. What is missing in such considerations is an analysis of how a dopant changes the properties of the crystal phases which can form instead of the glass. In order to illuminate this aspect we performed molecular dynamics simulations to study the effects of Mg and Sm dopants on the crystal nucleation in Al. The simulation data were found to be consistent with the experimental observations that addition of Mg to Al does not lead to vitrification but addition of only 8% Sm does. The significant effect of Sm doping was related to the intolerance of Al to this dopant. This leads to increase in the solid-liquid interfacial free energy, and therefore, to increase in the nucleation barrier and to dramatic decrease in the nucleation rate. The intolerance mechanism also significantly affects the growth kinetics.
Motivated by the mean field prediction of a Gardner phase transition between a normal glass and a marginally stable glass, we investigate the off-equilibrium dynamics of three-dimensional polydisperse hard spheres, used as a model for colloidal or granular glasses. Deep inside the glass phase, we find that a sharp crossover pressure $P_{rm G}$ separates two distinct dynamical regimes. For pressure $P < P_{rm G}$, the glass behaves as a normal solid, displaying fast dynamics that quickly equilibrates within the glass free energy basin. For $P>P_{rm G}$, instead, the dynamics becomes strongly anomalous, displaying very large equilibration time scales, aging, and a constantly increasing dynamical susceptibility. The crossover at $P_{rm G}$ is strongly reminiscent of the one observed in three-dimensional spin-glasses in an external field, suggesting that the two systems could be in the same universality class, consistently with theoretical expectations.
Due to high viscosity, glassy systems evolve slowly to the ordered state. Results of molecular dynamics simulation reveal that the structural ordering in glasses becomes observable over experimental (finite) time-scale for the range of phase diagram with high values of pressure. We show that the structural ordering in glasses at such conditions is initiated through the nucleation mechanism, and the mechanism spreads to the states at extremely deep levels of supercooling. We find that the scaled values of the nucleation time, $tau_1$ (average waiting time of the first nucleus with the critical size), in glassy systems as a function of the reduced temperature, $widetilde{T}$, are collapsed onto a single line reproducible by the power-law dependence. This scaling is supported by the simulation results for the model glassy systems for a wide range of temperatures as well as by the experimental data for the stoichiometric glasses at the temperatures near the glass transition.
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