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The glass transition temperatures of common binary oxides, including those with low glass-forming ability, are estimated using pair distribution functions (PDFs) from ab initio molecular dynamics simulations. The computed glass transition temperatures for good glass-formers such as silica (SiO2), germania (GeO2), and boron oxide (B2O3) are in agreement with measured values. These calculations are then used to compute the glass transition temperatures of alumina (Al2O3), tantala (Ta2O5), and telluria (TeO2), which are known to exhibit low glass-forming ability. For Al2O3 and Ta2O5, we also compute the simulated caloric curve from molecular dynamics simulations using two-body empirical force fields. Finally, we discuss the possibility of extracting the glass transition temperature by measuring the thermal broadening of the PDFs from scattering measurements. 

Chemistry is considered as one of the more promising applications to science of near-term quantum computing. Recent work in transitioning classical algorithms to a quantum computer has led to great strides in improving quantum algorithms and illustrating their quantum advantage. Because of the limitations of near-term quantum computers, the most effective strategies split the work over classical and quantum computers. There is a proven set of methods in computational chemistry and materials physics that has used this same idea of splitting a complex physical system into parts that are treated at different levels of theory to obtain solutions for the complete physical system for which a brute force solution with a single method is not feasible. These methods are variously known as embedding, multi-scale, and fragment techniques and methods. We review these methods and then propose the embedding approach as a method for describing complex biochemical systems, with the parts not only treated with different levels of theory, but computed with hybrid classical and quantum algorithms. Such strategies are critical if one wants to expand the focus to biochemical molecules that contain active regions that cannot be properly explained with traditional algorithms on classical computers. While we do not solve this problem here, we provide an overview of where the field is going to enable such problems to be tackled in the future.