Comprehensive image dataset for enhancing object detection in chemical experiments.

The application of image recognition in chemical experiments has the potential to enhance experiment recording and risk management. However, the current scarcity of suitable benchmarking datasets restricts the applications of machine vision techniques in chemical experiments. This data article presents an image dataset featuring common chemical apparatuses and experimenter's hands. The images have been meticulously annotated, providing detailed information for precise object detection through deep learning methods. The images were captured from videos filmed in organic chemistry laboratories. This dataset comprises a total of 5078 images including diverse backgrounds and situations surrounding the objects. Detailed annotations are provided in accompanying text files. The dataset is organized into training, validation, and test subsets. Each subset is stored within independent folders for easy access and utilization.

Semi-local machine-learned kinetic energy density functional with third-order gradients of electron density.

A semi-local kinetic energy density functional (KEDF) was constructed based on machine learning (ML). The present scheme adopts electron densities and their gradients up to third-order as the explanatory variables for ML and the Kohn-Sham (KS) kinetic energy density as the response variable in atoms and molecules. Numerical assessments of the present scheme were performed in atomic and molecular systems, including first- and second-period elements. The results of 37 conventional KEDFs with explicit formulae were also compared with those of the ML KEDF with an implicit formula. The inclusion of the higher order gradients reduces the deviation of the total kinetic energies from the KS calculations in a stepwise manner. Furthermore, our scheme with the third-order gradient resulted in the closest kinetic energies to the KS calculations out of the presented functionals.