Organic photovoltaic (OPV) materials are promising candidates for cheap, printable solar cells. However, there are a very large number of potential donors and acceptors, making selection of the best materials difficult. Here, we show that machine-learning approaches can leverage computationally expensive DFT calculations to estimate important OPV materials properties quickly and accurately. We generate quantitative relationships between simple and interpretable chemical signature and one-hot descriptors and OPV power conversion efficiency (PCE), open circuit potential (V_oc), short circuit density (J_sc), highest occupied molecular orbital (HOMO) energy, lowest unoccupied molecular orbital (LUMO) energy, and the HOMO–LUMO gap. The most robust and predictive models could predict PCE (computed by DFT) with a standard error of ±0.5 for percentage PCE for both the training and test set. This model is useful for pre-screening potential donor and acceptor materials for OPV applications, accelerating design of these devices for green energy applications.

A major health concern of the 21^st century is the rise of multi-drug resistant pathogenic microbial species. Recent technological advancements have led to considerable opportunities for low-dimensional materials (LDMs) as potential next-generation antimicrobials. LDMs have demonstrated antimicrobial behaviour towards a variety of pathogenic bacterial and fungal cells, due to their unique physicochemical properties. This review provides a critical assessment of current LDMs that have exhibited antimicrobial behaviour and their mechanism of action. Future design considerations and constraints in deploying LDMs for antimicrobial applications are discussed. It is envisioned that this review will guide future design parameters for LDM-based antimicrobial applications.