Material Optimization

Material optimization aims to accelerate the discovery and design of materials with desired properties, addressing the high cost and time associated with traditional methods. Current research heavily utilizes machine learning, employing Bayesian optimization, variational autoencoders, and symbolic learning to efficiently explore vast material design spaces and predict optimal material compositions and structures. These advancements are significantly impacting materials science by enabling faster development cycles and the discovery of novel materials for applications ranging from energy storage to biomedical devices.