Assessing Deep Generative Models in Chemical Composition Space

The computational discovery of novel materials has been one of the main motivations behind research in theoretical chemistry for several decades. Despite much effort, this is far from a solved problem, however. Among other reasons, this is due to the enormous space of possible structures and compositions that could potentially be of interest. In the case of inorganic materials, this is exacerbated by the combinatorics of the periodic table since even a single-crystal structure can in principle display millions of compositions. Consequently, there is a need for tools that enable a more guided exploration of the materials design space. Here, generative machine learning models have recently emerged as a promising technology. In this work, we assess the performance of a range of deep generative models based on reinforcement learning, variational autoencoders, and generative adversarial networks for the prototypical case of designing Elpasolite compositions with low formation energies. By relying on the fully enumerated space of 2 million main-group Elpasolites, the precision, coverage, and diversity of the generated materials are rigorously assessed. Additionally, a hyperparameter selection scheme for generative models in chemical composition space is developed

Active discovery of organic semiconductors

The versatility of organic molecules generates a rich design space for organic semiconductors (OSCs) considered for electronics applications. Offering unparalleled promise for materials discovery, the vastness of this design space also dictates efficient search strategies. Here, we present an active machine learning (AML) approach that explores an unlimited search space through consecutive application of molecular morphing operations. Evaluating the suitability of OSC candidates on the basis of charge injection and mobility descriptors, the approach successively queries predictive-quality first-principles calculations to build a refining surrogate model. The AML approach is optimized in a truncated test space, providing deep methodological insight by visualizing it as a chemical space network. Significantly outperforming a conventional computational funnel, the optimized AML approach rapidly identifies well-known and hitherto unknown molecular OSC candidates with superior charge conduction properties. Most importantly, it constantly finds further candidates with highest efficiency while continuing its exploration of the endless design space

Finding the Right Bricks for Molecular Legos: A Data Mining Approach to Organic Semiconductor Design

Improving charge carrier mobilities in organic semiconductors is a challenging task that has hitherto primarily been tackled by empirical structural tuning of promising core compounds. Knowledge-based methods can greatly accelerate such local exploration, while a systematic analysis of large chemical databases can point toward promising design strategies. Here, we demonstrate such data mining by clustering an in-house database of >64,000 organic molecular crystals for which two charge-transport descriptors, the electronic coupling and the reorganization energy, have been calculated from first principles. The clustering is performed according to the Bemis–Murcko scaffolds of the constituting molecules and according to the side groups with which these molecular backbones are functionalized. In both cases, we obtain statistically significant structure–property relationships with certain scaffolds (side groups) consistently leading to favorable charge-transport properties. Functionalizing promising scaffolds with favorable side groups results in engineered molecular crystals for which we indeed compute improved charge-transport properties

Multiplicity among T Tauri stars in OB and T associations: implications for binary star formation

We present first results of a survey for companions among X-ray selected pre-main sequence stars, most of them being weak-line T Tauri stars (WTTS). These T Tauri stars have been identified in the course of optical follow-up observations of sources from the ROSAT All Sky Survey associated with star forming regions. The areas surveyed include the T associations of Chamaeleon and Lupus as well as Upper Scorpius, the latter being part of the Scorpius Centaurus OB association (Sco OB 2). Using SUSI at the NTT under subarcsec seeing conditions we observed 195 T Tauri stars through a 1\mum ("Z") filter and identified companions to 31 of them (among these 12 subarcsec binaries). Based on statistical arguments we conclude that almost all of them are indeed physical (i.e. gravitationally bound) binary or multiple systems. For 10 systems located in Upper Scorpius and Lupus, we additionally obtained spatially resolved near-infrared photometry in the J, H, and K bands with the MPIA 2.2m telescope at ESO, La Silla. The near-infrared colours of the secondaries are consistent with those of dwarfs and are clearly distinct from those of late type giant stars. Based on astrometric measurements of some binaries we show that the components of these binaries are common proper motion pairs, very likely in a gravitationally bound orbit around each other. We find that the overall binary frequency among T Tauri stars in a range of separations between 120 and 1800 AU is in agreement with the binary frequency observed among main sequence stars in the solar neighbourhood. However, we note that within individual regions the spatial distribution of binaries -- within a distinct range of separation -- is non-uniform. In particular, in Uppe