Effect of ion irradiation on structural properties of Cu64Zr36 metallic glass

By means of molecular dynamics simulations, we study the low energy ion irradiation effect on the short-range order (SRO) of metallic glass structures. Breaking the topological SRO in a controlled manner may lead to desirable modifications of deformation behavior of glass structures. These changes in SRO were also found to be stable during the thermal annealing at temperatures below the glass transition temperature (T-g). The tensile deformation simulations performed for the Cu-Zr glass nanorods show that irradiated nanorods become softer compared to the pristine ones, changing the yielding mechanism from a slip via formation of a single shear band to formation of a broad necking region

Controlled softening of Cu64Zr36 metallic glass by ion irradiation

We study the effect of irradiation with 5–20 keV recoils on the topological and chemical short-range order of Cu 64Zr36 metallic glass using molecular dynamics simulations. We show that within the cascade region, the structural backbone of stiff Cu-centered icosahedral units is destroyed, leading to locally softened areas. Under mechanical load, the formation of shear transformation zones is thus promoted in the damaged area. Our results suggest that irradiation is a means to introduce nucleation sites for multiple shear bands and thus prevents catastrophic failure due to the presence of a single critical shear band

Hybrid quantum-classical machine learning for generative chemistry and drug design

Abstract Deep generative chemistry models emerge as powerful tools to expedite drug discovery. However, the immense size and complexity of the structural space of all possible drug-like molecules pose significant obstacles, which could be overcome with hybrid architectures combining quantum computers with deep classical networks. As the first step toward this goal, we built a compact discrete variational autoencoder (DVAE) with a Restricted Boltzmann Machine (RBM) of reduced size in its latent layer. The size of the proposed model was small enough to fit on a state-of-the-art D-Wave quantum annealer and allowed training on a subset of the ChEMBL dataset of biologically active compounds. Finally, we generated 2331 novel chemical structures with medicinal chemistry and synthetic accessibility properties in the ranges typical for molecules from ChEMBL. The presented results demonstrate the feasibility of using already existing or soon-to-be-available quantum computing devices as testbeds for future drug discovery applications