Structure Refinement

Structure refinement focuses on improving the accuracy and robustness of representations of complex structures, whether they are molecules, materials, images, or graphs. Current research emphasizes developing algorithms and models, such as graph contrastive learning and Bayesian approaches, to enhance the reliability of these representations by addressing issues like noise, ambiguity, and adversarial attacks. These advancements are crucial for accelerating scientific discovery in diverse fields, including materials science, drug design, and image processing, by enabling more accurate analysis and prediction. The ultimate goal is to create more reliable and efficient methods for extracting meaningful information from complex data.