Author Correction: Deposits from evaporating emulsion drops.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Deposits from evaporating emulsion drops.

The processes in which droplets evaporate from solid surfaces, leaving behind distinct deposition patterns, have been studied extensively for variety of solutions. In this work, by combining different microscopy techniques (confocal fluorescence, video and Raman) we investigate pattern formation and evaporation-induced phase change in drying oil-in-water emulsion drops. This combination of techniques allows us to perform drop shape analysis while visualizing the internal emulsion structure simultaneously. We observe that drying of the continuous water phase of emulsion drops on hydrophilic surfaces favors the formation of ring-like zones depleted of oil droplets at the contact line, which originate from geometrical confinement of oil droplets by the meniscus. From such a depletion zone, a "coffee ring" composed of surfactant molecules forms as the water evaporates. On all surfaces drying induces emulsion destabilization by coalescence of oil droplets, commencing at the drop periphery. For hydrophobic surfaces, the coalescence of the oil droplets leads to a uniform oil film spreading out from the initial contact line. The evaporation dynamics of these composite drops indicate that the water in the continuous phase of the emulsion drops evaporates predominantly by diffusion through the vapor, showing no large differences to the evaporation of simple water drops.

wACSF-Weighted atom-centered symmetry functions as descriptors in machine learning potentials.

We introduce weighted atom-centered symmetry functions (wACSFs) as descriptors of a chemical system's geometry for use in the prediction of chemical properties such as enthalpies or potential energies via machine learning. The wACSFs are based on conventional atom-centered symmetry functions (ACSFs) but overcome the undesirable scaling of the latter with an increasing number of different elements in a chemical system. The performance of these two descriptors is compared using them as inputs in high-dimensional neural network potentials (HDNNPs), employing the molecular structures and associated enthalpies of the 133 855 molecules containing up to five different elements reported in the QM9 database as reference data. A substantially smaller number of wACSFs than ACSFs is needed to obtain a comparable spatial resolution of the molecular structures. At the same time, this smaller set of wACSFs leads to a significantly better generalization performance in the machine learning potential than the large set of conventional ACSFs. Furthermore, we show that the intrinsic parameters of the descriptors can in principle be optimized with a genetic algorithm in a highly automated manner. For the wACSFs employed here, we find however that using a simple empirical parametrization scheme is sufficient in order to obtain HDNNPs with high accuracy.