AI4Research: A Survey of Artificial Intelligence for Scientific Research

2025-07-02

AI Summary

The field of AI-for-Scientific-Research (AI4Research) lacks a systematic, comprehensive survey, hindering domain-wide understanding and progress. To address this, we propose the first holistic classification framework covering the entire scientific research lifecycle, with emphasis on large language model–driven capabilities—such as logical reasoning and experimental automation—exemplified by models like OpenAI-o1 and DeepSeek-R1. Our methodology integrates rigorous yet scalable technical pathways, synthesizing multidisciplinary datasets, toolchains, and resource repositories. We establish an open-source AI4Research compendium, including annotated application scenarios, benchmark corpora, and platform tools. Furthermore, we conduct a critical analysis of societal implications and governance challenges. This work delivers foundational theoretical insights, actionable implementation guidelines, and infrastructural support for intelligent scientific discovery, thereby fostering cross-disciplinary collaboration and innovation.

Technology Category

• Humans and AI: Other Foundations of Human Computation & AI
• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Recent advancements in artificial intelligence (AI), particularly in large language models (LLMs) such as OpenAI-o1 and DeepSeek-R1, have demonstrated remarkable capabilities in complex domains such as logical reasoning and experimental coding. Motivated by these advancements, numerous studies have explored the application of AI in the innovation process, particularly in the context of scientific research. These AI technologies primarily aim to develop systems that can autonomously conduct research processes across a wide range of scientific disciplines. Despite these significant strides, a comprehensive survey on AI for Research (AI4Research) remains absent, which hampers our understanding and impedes further development in this field. To address this gap, we present a comprehensive survey and offer a unified perspective on AI4Research. Specifically, the main contributions of our work are as follows: (1) Systematic taxonomy: We first introduce a systematic taxonomy to classify five mainstream tasks in AI4Research. (2) New frontiers: Then, we identify key research gaps and highlight promising future directions, focusing on the rigor and scalability of automated experiments, as well as the societal impact. (3) Abundant applications and resources: Finally, we compile a wealth of resources, including relevant multidisciplinary applications, data corpora, and tools. We hope our work will provide the research community with quick access to these resources and stimulate innovative breakthroughs in AI4Research.

Problem

• Surveying AI applications in scientific research
• Addressing lack of comprehensive AI4Research overview
• Providing taxonomy, gaps, and resources for AI4Research

Innovation

• Systematic taxonomy for AI4Research tasks
• Focus on automated experiments rigor scalability
• Compiled multidisciplinary applications data tools