Unlocking Scientific Insights: Data Mining, Large Models, and AI-Driven Discovery

Dear Colleagues,

As the volume of scientific data in this field grows exponentially, traditional data analysis methods are increasingly becoming insufficiently able to uncover deep insights and complex patterns. The integration of artificial intelligence (AI) with data mining, large-scale models, and domain-specific scientific research offers an unprecedented opportunity to transform how we process, interpret, and utilize data. AI has the potential to accelerate scientific discovery by automating and enhancing data exploration, hypothesis generation, and predictive modeling.

This Special Issue seeks to explore how innovative AI techniques and methodologies can significantly improve the way scientific data are mined, analyzed, and interpreted. By leveraging the power of large models, AI-driven data mining methods are capable of discovering hidden patterns and extracting actionable knowledge from vast, complex datasets with minimal human intervention. The application of these approaches is crucial for tackling the scientific challenges of the present day, where the volume, complexity, and heterogeneity of data pose substantial hurdles to conventional techniques.

The scope of this Special Issue includes, but is not limited to, the following topics:

• AI-driven data mining techniques: Exploring novel algorithms for identifying patterns, correlations, and anomalies in large scientific datasets, focusing on high-dimensional, noisy, and incomplete data typical in scientific research.
• Large models: The role of large language models (LLMs), deep neural networks, and other advanced AI models in processing, analyzing, and generating scientific insights from diverse sources of data such as text, images, and time-series data.
• AI for scientific domains: Practical applications of AI in key scientific areas, such as drug discovery, climate science, genomics, and materials science.
• Interdisciplinary collaboration: Developing frameworks for integrating AI with domain-specific knowledge to enhance human-AI collaboration and foster cross-disciplinary research.
• Interpretability and explainability: Addressing challenges related to the transparency of AI models, particularly in high-stakes scientific domains where interpretability and accountability are essential.
• Ethical, legal, and social implications: Investigating the ethical considerations surrounding the deployment of AI in scientific research, including fairness, bias, and transparency, and developing best practices for responsible AI use in science.
• Benchmarking and evaluation: Proposing standardized metrics and evaluation methods for assessing AI models in scientific research, with a focus on reproducibility and robustness.
• Future directions in AI for science: Identifying emerging trends and technologies, including the role of AI in personalized medicine, sustainable development, and next-generation scientific discovery.

Dr. Luoyi Fu
Guest Editor

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• artificial intelligence
• deep learning
• AI-driven data mining
• AI for science
• scientific discovery
• large models