Advances in Neural Information Processing Systems 35, AI for Science Workshop
期刊：Crystal Growth & Design
作者：Yuriy Abramov, Bochen Li et al.
時間：2021-08-30

The modern crystal structure prediction (CSP) technologies have proven to be accurate enough to provide a valuable support for a stable form selection in the pharmaceutical industry. We demonstrate that successful applications of the CSP predictions, in part, may be accounted for by favorable uncertainty distribution with the smallest absolute errors in the low relative crystal energy region. Such behavior is dictated by the lowest contribution of the systematic scaling error of dispersion-corrected density functional theory (DFT-D) approaches in this region. These considerations are validated by benchmarking studies of selected popular DFT-D approaches relative to post-Hartree–Fock (post-HF) calculations for representative molecular dimeric configurations in the virtual crystalline states of four pharmaceutical compounds. In addition, discussed are uncertainty distributions of DFT-D predictions of relative energies of eight ROY and five oxalyl dihydrazide (ODH) polymorphs relative to MP2D/HMBI and CCSD(T)/HMBI predictions, respectively.