A philosophy for defining what constitutes a virtual high-throughput screen is discussed, and the choices which influence decisions at each stage of the 'computational funnel' are investigated, including an indepth discussion of the generation of molecular libraries. Additionally advice on the storing, analysis and visualization of data is given, based upon extensive experience in the group.

Small structural changes in organic molecules can have a large influence on solid-state crystal packing, and this often thwarts attempts to produce isostructural series of crystalline solids. For metal-organic frameworks and covalent organic frameworks, this has been addressed by using strong, directional intermolecular bonding to create families of isoreticular solids. Here we show that an organic directing solvent, 1,4-dioxane, has a dominant effect on the lattice energy for a series of organic cage molecules. Inclusion of dioxane directs the crystal packing for these cages away from their lowest-energy polymorphs to form isostructural, 3-dimensional diamondoid pore channels. This is a unique function of the size, chemical function, and geometry of 1,4-dioxane, and hence a non-covalent auxiliary interaction assumes the role of directional coordination bonding or covalent bonding in extended crystalline frameworks. For a new cage, CC13, a dual, interpenetrating pore structure is formed which doubles the gas uptake and the surface area in the resulting dioxanedirected crystals.

Here, the employment of multilayer perceptrons, a type of artificial neural network, is proposed as part of a computational funneling procedure for high‐throughput organic materials design. Through the use of state of the art algorithms and a large amount of data extracted from the Harvard Clean Energy Project, it is demonstrated that these methods allow a great reduction in the fraction of the screening library that is actually calculated. Neural networks can reproduce the results of quantum‐chemical calculations with a large level of accuracy. The proposed approach allows to carry out large‐scale molecular screening projects with less computational time. This, in turn, allows for the exploration of increasingly large and diverse libraries.

Machine learning ground state QM/MM for accelerated computation of exciton dynamics.