Conspectus The ongoing revolution of the natural sciences by the advent of machine learning and artificial intelligence sparked significant interest in the material science community in recent years. The intrinsically high dimensionality of the space of realizable materials makes traditional approaches ineffective for large-scale explorations. Modern data science and machine learning tools developed for increasingly complicated problems are an attractive alternative. An imminent climate catastrophe calls for a clean energy transformation by overhauling current technologies within only several years of possible action available. Tackling this crisis requires the development of new materials at an unprecedented pace and scale. For example, organic photovoltaics have the potential to replace existing silicon-based materials to a large extent and open up new fields of application. In recent years, organic light-emitting diodes have emerged as state-of-the-art technology for digital screens and portable devices and are enabling new applications with flexible displays. Reticular frameworks allow the atom-precise synthesis of nanomaterials and promise to revolutionize the field by the potential to realize multifunctional nanoparticles with applications from gas storage, gas separation, and electrochemical energy storage to nanomedicine. In the recent decade, significant advances in all these fields have been facilitated by the comprehensive application of simulation and machine learning for property prediction, property optimization, and chemical space exploration enabled by considerable advances in computing power and algorithmic efficiency. In this Account, we review the most recent contributions of our group in this thriving field of machine learning for material science. We start with a summary of the most important material classes our group has been involved in, focusing on small molecules as organic electronic materials and crystalline materials. Specifically, we highlight the data-driven approaches we employed to speed up discovery and derive material design strategies. Subsequently, our focus lies on the data-driven methodologies our group has developed and employed, elaborating on high-throughput virtual screening, inverse molecular design, Bayesian optimization, and supervised learning. We discuss the general ideas, their working principles, and their use cases with examples of successful implementations in data-driven material discovery and design efforts. Furthermore, we elaborate on potential pitfalls and remaining challenges of these methods. Finally, we provide a brief outlook for the field as we foresee increasing adaptation and implementation of large scale data-driven approaches in material discovery and design campaigns.

The design of molecular catalysts typically involves reconciling multiple conflicting property requirements, largely relying on human intuition and local structural searches. However, the vast number of potential catalysts requires pruning of the candidate space by efficient property prediction with quantitative structure–property relationships. Data-driven workflows embedded in a library of potential catalysts can be used to build predictive models for catalyst performance and serve as a blueprint for novel catalyst designs. Herein we introduce kraken, a discovery platform covering monodentate organophosphorus­(III) ligands providing comprehensive physicochemical descriptors based on representative conformer ensembles. Using quantum-mechanical methods, we calculated descriptors for 1558 ligands, including commercially available examples, and trained machine learning models to predict properties of over 300000 new ligands. We demonstrate the application of kraken to systematically explore the property space of organophosphorus ligands and how existing data sets in catalysis can be used to accelerate ligand selection during reaction optimization.

Nanoparticle/liquid crystal (NP/LC) composites can be stabilized by hydrogen bonding, a relatively strong, yet reversible interaction allowing for the annealing of defects.

Ethylene oxide oligomers and polymers, free and tethered to gold nanoparticles, were dispersed in blue phase liquid crystals (BPLC). Gold nanospheres (AuNPs) and nanorods (AuNRs) were functionalized with thiolated ethylene oxide ligands with molecular weights ranging from 200 to 5000 g/mol. The BPLC mixture (ΔTBP ~6 °C) was based on the mesogenic acid heterodimers, n-hexylbenzoic acid (6BA) and n-trans-butylcyclohexylcarboxylic acid (4-BCHA) with the chiral dopant (R)-2-octyl 4-[4-(hexyloxy)benzoyloxy]benzoate. The lowest molecular weight oligomer lowered and widened the BP range but adding AuNPs functionalized with the same ligand had little effect. Higher concentrations or molecular weights of the ligands, free or tethered to the AuNPs, completely destabilized the BP. Mini-AuNRs functionalized with the same ligands lowered and widened the BP temperature range with longer mini-AuNRs having a larger effect. In contrast to the AuNPs, the mini-AuNRs with the higher molecular weight ligands widened rather than destabilized the BP, though the lowest MW ligand yielded the largest BP range, (ΔTBP > 13 °C). The different effects on the BP may be due to the AuNPs accumulating at singular defect sites whereas the mini-AuNRs, with diameters smaller than that of the disclination lines, can more efficiently fill in the BP defects.