Klavs F. Jensen

We report a synthesis of highly luminescent (CdSe)ZnS composite quantum dots with CdSe cores ranging in diameter from 23 to 55 Å. The narrow photoluminescence (fwhm ≤ 40 nm) from these composite dots spans most of the visible spectrum from blue through red with quantum yields of 30−50% at room temperature. We characterize these materials using a range of optical and structural techniques. Optical absorption and photoluminescence spectroscopies probe the effect of ZnS passivation on the electronic structure of the dots. We use a combination of wavelength dispersive X-ray spectroscopy, X-ray photoelectron spectroscopy, small and wide angle X-ray scattering, and transmission electron microscopy to analyze the composite dots and determine their chemical composition, average size, size distribution, shape, and internal structure. Using a simple effective mass theory, we model the energy shift for the first excited state for (CdSe)ZnS and (CdSe)CdS dots with varying shell thickness. Finally, we characterize the growth of ZnS on CdSe cores as locally epitaxial and determine how the structure of the ZnS shell influences the photoluminescence properties.

Advancements in neural machinery have led to a wide range of algorithmic solutions for molecular property prediction. Two classes of models in particular have yielded promising results: neural networks applied to computed molecular fingerprints or expert-crafted descriptors and graph convolutional neural networks that construct a learned molecular representation by operating on the graph structure of the molecule. However, recent literature has yet to clearly determine which of these two methods is superior when generalizing to new chemical space. Furthermore, prior research has rarely examined these new models in industry research settings in comparison to existing employed models. In this paper, we benchmark models extensively on 19 public and 16 proprietary industrial data sets spanning a wide variety of chemical end points. In addition, we introduce a graph convolutional model that consistently matches or outperforms models using fixed molecular descriptors as well as previous graph neural architectures on both public and proprietary data sets. Our empirical findings indicate that while approaches based on these representations have yet to reach the level of experimental reproducibility, our proposed model nevertheless offers significant improvements over models currently used in industrial workflows.

The synthesis of complex organic molecules requires several stages, from ideation to execution, that require time and effort investment from expert chemists. Here, we report a step toward a paradigm of chemical synthesis that relieves chemists from routine tasks, combining artificial intelligence-driven synthesis planning and a robotically controlled experimental platform. Synthetic routes are proposed through generalization of millions of published chemical reactions and validated in silico to maximize their likelihood of success. Additional implementation details are determined by expert chemists and recorded in reusable recipe files, which are executed by a modular continuous-flow platform that is automatically reconfigured by a robotic arm to set up the required unit operations and carry out the reaction. This strategy for computer-augmented chemical synthesis is demonstrated for 15 drug or drug-like substances.