High Throughput

High-throughput technologies aim to accelerate scientific experimentation and data analysis by processing vast amounts of data and performing many experiments concurrently. Current research focuses on integrating machine learning, particularly deep learning models like convolutional neural networks, graph neural networks, and transformers, with high-throughput experimental platforms across diverse fields, including materials science, drug discovery, and biological imaging. This allows for efficient analysis of complex datasets, automated experimental design, and the discovery of previously hidden relationships between variables, ultimately leading to faster and more cost-effective scientific advancements and improved technological applications.