Analyzing Atomic Interactions in Molecules as Learned by Neural Networks.

While machine learning (ML) models have been able to achieve unprecedented accuracies across various prediction tasks in quantum chemistry, it is now apparent that accuracy on a test set alone is not a guarantee for robust chemical modeling such as stable molecular dynamics (MD). To go beyond accuracy, we use explainable artificial intelligence (XAI) techniques to develop a general analysis framework for atomic interactions and apply it to the SchNet and PaiNN neural network models. We compare these interactions with a set of fundamental chemical principles to understand how well the models have learned the underlying physicochemical concepts from the data.

Influence of Different Measuring Backgrounds on the Classification of Multilayer Polyolefin Films Using a Near-Infrared Handheld Spectrometer.

The low thickness of plastic films poses a challenge when using near-infrared (NIR) spectroscopy as it affects the spectral quality and classification. This research focuses on offering a solution to the challenge of classifying multilayer plastic film materials with a focus on polyolefin multilayer plastics. It presents the importance of spectral quality on accurate classification.

Interferon subverts an AHR-JUN axis to promote CXCL13 T cells in lupus.

Systemic lupus erythematosus (SLE) is prototypical autoimmune disease driven by pathological T cell-B cell interactions. Expansion of T follicular helper (T) and T peripheral helper (T) cells, two T cell populations that provide help to B cells, is a prominent feature of SLE. Human T and T cells characteristically produce high levels of the B cell chemoattractant CXCL13 (refs.