Hetero-polycyclic aromatic systems: A data-driven investigation of structure-property relationships.

Polycyclic aromatic systems (PASs) are pervasive compounds that have a substantial impact in chemistry and materials science. Although their specific structure-property relationships hold the key to the design of new functional molecules, a detailed understanding of these relationships remains elusive. To elucidate these relationships, we performed a data-driven investigation of the newly generated COMPAS-2 dataset, which contains ~500k molecules consisting of 11 types of aromatic and antiaromatic rings and ranging in size from one to ten rings.

COMPAS-2: a dataset of cata-condensed hetero-polycyclic aromatic systems.

Polycyclic aromatic systems are highly important to numerous applications, in particular to organic electronics and optoelectronics. High-throughput screening and generative models that can help to identify new molecules to advance these technologies require large amounts of high-quality data, which is expensive to generate. In this report, we present the largest freely available dataset of geometries and properties of cata-condensed poly(hetero)cyclic aromatic molecules calculated to date.

Guided diffusion for inverse molecular design.

The holy grail of materials science is de novo molecular design, meaning engineering molecules with desired characteristics. The introduction of generative deep learning has greatly advanced efforts in this direction, yet molecular discovery remains challenging and often inefficient. Herein we introduce GaUDI, a guided diffusion model for inverse molecular design that combines an equivariant graph neural net for property prediction and a generative diffusion model.