  Directly manipulating the atomic structure to achieve a specific property is
a long pursuit in the field of materials. However, hindered by the disordered,
non-prototypical glass structure and the complex interplay between structure
and property, such inverse design is dauntingly hard for glasses. Here,
combining two cutting-edge techniques, graph neural networks and swap Monte
Carlo, we develop a data-driven, property-oriented inverse design route that
managed to improve the plastic resistance of Cu-Zr metallic glasses in a
controllable way. Swap Monte Carlo, as sampler, effectively explores the glass
landscape, and graph neural networks, with high regression accuracy in
predicting the plastic resistance, serves as decider to guide the search in
configuration space. Via an unconventional strengthening mechanism, a
geometrically ultra-stable yet energetically meta-stable state is unraveled,
contrary to the common belief that the higher the energy, the lower the plastic
resistance. This demonstrates a vast configuration space that can be easily
overlooked by conventional atomistic simulations. The data-driven techniques,
structural search methods and optimization algorithms consolidate to form a
toolbox, paving a new way to the design of glassy materials.
