Imaging Cu2O nanocube hollowing in solution by quantitative in situ X-ray ptychography.

Understanding morphological changes of nanoparticles in solution is essential to tailor the functionality of devices used in energy generation and storage. However, we lack experimental methods that can visualize these processes in solution, or in electrolyte, and provide three-dimensional information. Here, we show how X-ray ptychography enables in situ nano-imaging of the formation and hollowing of nanoparticles in solution at 155 °C. We simultaneously image the growth of about 100 nanocubes with a spatial resolution of 66 nm. The quantitative phase images give access to the third dimension, allowing to additionally study particle thickness. We reveal that the substrate hinders their out-of-plane growth, thus the nanocubes are in fact nanocuboids. Moreover, we observe that the reduction of Cu2O to Cu triggers the hollowing of the nanocuboids. We critically assess the interaction of X-rays with the liquid sample. Our method enables detailed in-solution imaging for a wide range of reaction conditions.

Exchange Spin Coupling from Gaussian Process Regression.

Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling beyond linear regression, which has not been studied yet. We employ Gaussian process regression, since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates ("error bars") along with predicted values. We compare a range of descriptors and kernels for 257 small dicopper complexes and find that a simple descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated descriptors when it comes to extrapolating toward larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated descriptors lies in their ability to linearize structure-property relationships, to the point that a simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the simple descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable descriptor and highlighting the interesting question of the role of chemical intuition vs systematic or automated selection of features for machine learning in chemistry and material science.