Generative AI and Interdisciplinary Applications

Dear Colleagues,

Generative Artificial Intelligence (GenAI) now produces coherent text, photorealistic images, atom-level molecular blueprints, and working engineering layouts. These systems already accelerate protein engineering, guide chip layout optimization, draft medical reports, compose educational material, and power social science simulations. Large language models, diffusion and flow-matching networks, and structure-based generators already help researchers form hypotheses, expand data sets, speed up simulation, and support creative thinking. At the same time, pressing questions remain regarding their robustness, generalization, evaluation protocols, bias, privacy, intellectual-property rights, and governance.

This Topic welcomes original research that advances GenAI algorithms and their application in various fields. We welcome studies that integrate GenAI into interdisciplinary applications, including research at the intersection of GenAI with natural sciences, engineering, life and health sciences, social sciences, arts, or humanities. In addition, we encourage submission regarding the responsible use of GenAI regarding safety, ethics, regulation, and responsible deployment. By gathering these contributions, we aim to provide a clear snapshot of GenAI across disciplines and to outline practical directions for future work.

Topics of interest include but are not limited to the following:

• Novel generative model architectures, algorithms, objectives, and optimization strategies
• Multimodal GenAI for heterogeneous data encoding, modeling, and generation
• Social simulation, agent-based modeling, economics, and policy analysis with GenAI
• Human-centered GenAI for education, creativity, language learning, and culture studies
• GenAI for life science, health sciences, and natural science
• GenAI for information retrieval, recommendation, and content generation
• GenAI for medical imaging, report generation, and clinical decision support
• Materials generation and inverse design for energy, catalysis, and manufacturing
• GenAI for environmental and climate modeling, remote sensing, and agriculture
• GenAI for software such as text-to-code generation and automated software engineering
• Mathematical foundations, evaluation metrics, calibration, and uncertainty estimation
• Safety, bias mitigation, privacy preservation, and legal or ethical considerations in responsible GenAI

Dr. Jisheng Dang
Prof. Dr. Wenjie Wang
Dr. Yongqi Li
Prof. Dr. Juncheng Li
Topic Editors