The reliability of organic molecular crystal structure prediction has improved tremendously in
recent years. Crystal structure predictions for small, mostly rigid molecules are quickly …

Neural network potentials (NNPs) are an innovative approach for calculating the potential
energy and forces of a chemical system. In principle, these methods are capable of …

A seventh blind test of crystal structure prediction was organized by the Cambridge
Crystallographic Data Centre featuring seven target systems of varying complexity: a silicon …

While density-functional theory (DFT) remains one of the most widely used tools in
computational chemistry, most functionals fail to properly account for the effects of London …

A seventh blind test of crystal structure prediction has been organized by the Cambridge
Crystallographic Data Centre. The results are presented in two parts, with this second part …

Molecular crystals play a central role in a wide range of scientific fields, including
pharmaceuticals and organic semiconductor devices. However, they are challenging …

Electronic structure methods offer in principle accurate predictions of molecular properties,
however, their applicability is limited by computational costs. Empirical methods are …

We present a high-throughput, end-to-end pipeline for organic crystal structure prediction
(CSP)─ the problem of identifying the stable crystal structures that will form from a given …

Solid form selection and design of crystalline small molecule active pharmaceutical
ingredients (APIs) would benefit from computational prediction and rationalization of the …

Hybrid density functional theory (DFT) remains intractable for large periodic systems due to
the demanding computational cost of exact exchange. We apply the tensor hypercontraction …