Harnessing Cloud Architecture for Crystal Structure Prediction Calculations
Accurate and rapid crystal structure predictions have the potential to transform the development of new materials, particularly in fields with highly complex molecular structures (such as in drug development). In this work we present a novel cloud-computing crystal structure prediction (CSP) platform with the capability of scheduling hundreds of thousands CPU cores and integrating cutting-edge computational chemistry algorithms. This new cloud-computing based CSP platform has been applied to three crystalline drug substances of increasing complexity. The lattice energies of the experimental crystal structures are all within 4.0 kJ/mol of the lowest energy predicted structures. On the basis of the results of this work, the algorithm improvement and the mass computational power of cloud computing can reduce the whole CSP process to just 1–3 weeks for Z′ = 1 systems and less than 5 weeks for significantly more complex systems. Furthermore, it is possible to simultaneously perform calculations for multiple molecules if desired. As a result of these improvements, CSP calculations can potentially be applied in conjunction with state-of-the-art experimental screening techniques to reduce the risk of finding new solid forms after product launch provided that a sufficient number of stoichiometries and space groups are explored.
Structural identification of vasodilator binding sites on the SUR2 subunit
Toward accurate and efficient dynamic computational strategy for heterogeneous catalysis: Temperature-dependent thermodynamics and kinetics for the chemisorbed on-surface CO
Boosting the predictive performance with aqueous solubility dataset curation