View article

Deformation localization as exemplified by earthquakes, landslides, shear banding in solids, and failure of engineering components is of utmost importance. In practice, differentiating the mechanical behavior in such generative narrow bands from the rest part, with difference by orders of magnitude in characteristic size, flow strength, temperature, and shearing rate, is both experimentally and computationally formidable. Here we propose a machine-learning-based constitutive modeling framework to overcome this barrier borne from conventional top-down continuum modelling approach. The model enables us to realize ultra-fine resolutions for deformation in those narrow bands with high efficiency. Taking metallic glasses (MGs) as an example, our model captures well shear localization in BMGs across a broad range of temperatures (0 K to its melting point of∼ 1000 K) and strain rates (10− 4 to 10 8/s). We verify …