Attention-based solubility prediction of polysulfide and electrolyte analysis for lithium-sulfur batteries.

During the continuous charge and discharge process in lithium-sulfur batteries, one of the next-generation batteries, polysulfides are generated in the battery's electrolyte, and impact its performance in terms of power and capacity by involving the process. The amount of polysulfides in the electrolyte could be estimated by the change of the Gibbs free energy of the electrolyte, [Formula: see text] in the presence of polysulfide. However, obtaining [Formula: see text] of the diverse mixtures of components in the electrolyte is a complex and expensive task that shows itself as a bottleneck in optimization of electrolytes. In this work, we present a machine-learning approach for predicting [Formula: see text] of electrolytes. The proposed architecture utilizes (1) an attention-based model (Attentive FP), a contrastive learning model (MolCLR) or morgan fingerprints to represent chemical components, and (2) transformers to account for the interactions between chemicals in the electrolyte. This architecture was not only capable of predicting electrolyte properties, including those of chemicals not used during training, but also providing insights into chemical interactions within electrolytes. It revealed that interactions with other chemicals relate to the logP and molecular weight of the chemicals.

Design of New Inorganic Crystals with the Desired Composition Using Deep Learning.

New solid-state materials have been discovered using various approaches from atom substitution in density functional theory (DFT) to generative models in machine learning. Recently, generative models have shown promising performance in finding new materials. Crystal generation with deep learning has been applied in various methods to discover new crystals. However, most generative models can only be applied to materials with specific elements or generate structures with random compositions. In this work, we developed a model that can generate crystals with desired compositions based on a crystal diffusion variational autoencoder. We generated crystal structures for 14 compositions of three types of materials in different applications. The generated structures were further stabilized using DFT calculations. We found the most stable structures in the existing database for all but one composition, even though eight compositions among them were not in the data set trained in a crystal diffusion variational autoencoder. This substantiates the prospect of the generation of an extensive range of compositions. Finally, 205 unique new crystal materials with energy above hull <100 meV/atom were generated. Moreover, we compared the average formation energy of the crystals generated from five compositions, two of which were hypothetical, with that of traditional methods like atom substitution and a generative model. The generated structures had lower formation energy than those of other models, except for one composition. These results demonstrate that our approach can be applied stably in various fields to design stable inorganic materials based on machine learning.

Smartphone-Based Point-of-Care Urinalysis Under Variable Illumination.

Urine tests are performed by using an off-the-shelf reference sheet to compare the color of test strips. However, the tabular representation is difficult to use and more prone to visual errors, especially when the reference color-swatches to be compared are spatially apart. Thus, making it is difficult to distinguish between the subtle differences of shades on the reagent pads. This manuscript represents a new arrangement of reference arrays for urine test strips (urinalysis). Reference color swatches are grouped in a doughnut chart, surrounding each reagent pad on the strip. The urine test can be evaluated using naked eye by referring to the strip with no additional sheet necessary. Along with this new strip, an algorithm for smartphone based application is also proposed as an alternative to deliver diagnostic results. The proposed colorimetric detection method evaluates the captured image of the strip, under various color spaces and evaluates ten different tests for urine. Thus, the proposed system can deliver results on the spot using both naked eye and smartphone. The proposed scheme delivered accurate results under various environmental illumination conditions without any calibration requirements, exhibiting performances suitable for real-life applications and an ease for a common user.