Structure Generation

Structure generation, the automated creation of organized data structures from various inputs, aims to accelerate scientific discovery and design processes across diverse fields. Current research heavily utilizes generative models like diffusion models, variational autoencoders (VAEs), and generative adversarial networks (GANs), often integrated with transformer networks and graph neural networks, to generate structures ranging from molecules and crystal lattices to architectural floorplans and textual representations (tables, mind maps). These advancements are improving the efficiency and effectiveness of tasks such as materials discovery, drug design, and architectural planning, while also enhancing text comprehension through structured summaries. The ability to generate valid and diverse structures is a key focus, often incorporating physics-based constraints or geometry enhancements to improve the quality and applicability of the generated outputs.