Latest Advances in Manufacturing and Machine Learning of Bulk Metallic Glasses

Abstract

In this review, two interrelated areas are focused on for the development of novel amorphous metallic alloys, namely, materials processing and machine learning techniques for the design of new alloy compositions. Findings, barriers, and opportunities are described, targeting powder production and sintering, additive manufacturing, and postprocessing techniques, followed by the latest developments in artificial intelligence algorithms for both the design of new alloys and for alloy classification tasks.

From ultra-fast growth to avalanche growth in devitrifying glasses

“…We note that avalanche-mediated spontaneous aging has been confirmed for the same glass over this entire range. 24 All time scales are expressed in terms of the Brownian time τB = 3πη⟨σ⟩ 3 /kBT, with unit viscosity η.…”

“…6(a). The displacement events are not always well separated like the avalanche events observed in bulk CG states, but they are nevertheless qualitatively , where the proportion of crystalline particles in the originally glassy portion of the simulation box, PX,g, the normalized virial pressure βP⟨σ⟩ 3 and the scaled energy βU are also given. It is clear that these are correlated with each other, much like the avalanche dynamics of an ordinary, mature bulk glass.…”

“…Devitrification can be both a functional impediment for glassy materials [1][2][3] and a means to achieve tailored crystal growth. [4][5][6] Given the slow rate of diffusive transport in glasses and deeply supercooled melts, the mechanisms by which crystals can grow are largely different from what is expected from classical nucleation theory [7][8][9][10][11][12][13][14] and remain a field of active interest, [15][16][17][18] but a complete mechanistic understanding of the process remains elusive.…”