Predicting pharmaceutical powder flow from microscopy images using deep learning

Abstract

The powder flowability of active pharmaceutical ingredients and excipients is a key parameter in the manufacturing of solid dosage forms used to inform the choice of tabletting methods. Direct compression...

PASCAL: the perovskite automated spin coat assembly line accelerates composition screening in triple-halide perovskite alloys

“…20,[34][35][36][37][38] Furthermore, precision automation enables ac-celeration of materials discovery with machine learning by reducing noise that can slow learning. Already, the community has proven benefits of black-box models and physics informed models to facilitate materials screening [39][40][41] and performance prediction. [42][43][44] Halide perovskites hold particular promise for use in tandem solar cells, which may lower overall costs of solar electricity via a higher power conversion efficiency.…”

Digital biology approach for macroscale studies of biofilm growth and biocide effects with electron microscopy

“…Recently, a solution to this problem—the application of deep learning algorithms—has become increasingly widely adopted. Examples of usages include biomedical applications, 56 analysis of pharmaceutical powders, 57 protein nanowires, 58 catalysts, 59 and analysis in a liquid phase. 60 Besides segmentation and detection tasks, deep learning-based image inpainting has also performed.…”