Over the past few years, we have seen fundamental breakthroughs in core problems in machine learning, largely driven by advances in deep neural networks. At the same time, the amount of data collected in a wide array of scientific domains is dramatically increasing in both size and complexity. Taken together, this suggests many exciting opportunities for deep learning applications in scientific settings. But a significant challenge to this is simply knowing where to start. The sheer breadth and diversity of different deep learning techniques makes it difficult to determine what scientific problems might be most amenable to these methods, or which specific combination of methods might offer the most promising first approach. In this survey, we focus on addressing this central issue, providing an overview of many widely used deep learning models, spanning visual, sequential and graph structured data, associated tasks and different training methods, along with techniques to use deep learning with less data and better interpret these complex models --- two central considerations for many scientific use cases. We also include overviews of the full design process, implementation tips, and links to a plethora of tutorials, research summaries and open-sourced deep learning pipelines and pretrained models, developed by the community. We hope that this survey will help accelerate the use of deep learning across different scientific domains.
Theory-guided Data Science: A New Paradigm for Scientific Discovery from Data
University of Minnesota（明尼苏达大学）；University of Cincinnati（辛辛那提大学）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）；Northeastern University（东北大学）；North Carolina State University（北卡罗来纳州立大学）
AutoSciDACT: Automated Scientific Discovery through Contrastive Embedding and Hypothesis Testing
Department of Physics, Massachusetts Institute of Technology（麻省理工学院物理系）；The NSF AI Institute for Artificial Intelligence and Fundamental Interactions（国家科学基金会人工智能与基本相互作用研究所）；Department of Physics, University of Wisconsin, Madison（威斯康星大学麦迪逊分校物理系）
Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；University of Science and Technology of China（中国科学技术大学）；Tsinghua University（清华大学）；The University of Hong Kong（香港大学）；Haihe Laboratory of Sustainable Chemical Transformations（海河可持续化学转化实验室）；Shanghai University（上海大学）；University College London（伦敦大学学院）
SelfAI: A self-directed framework for long-horizon scientific discovery
University of Electronic Science and Technology of China（电子科技大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Xian Jiaotong University（西安交通大学）；Yale University（耶鲁大学）；Washington University in St. Louis（圣路易斯华盛顿大学）；Monash University（莫纳什大学）
From AI for Science to Agentic Science: A Survey on Autonomous Scientific Discovery
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Zhejiang University（浙江大学）；Fudan University（复旦大学）；University of British Columbia（不列颠哥伦比亚大学）；Tongji University（同济大学）；The Chinese University of Hong Kong（香港中文大学）；Shanghai Jiaotong University（上海交通大学）；Stony Brook University（石溪大学）；Lingang Laboratory（临港实验室）；Tsinghua University（清华大学）
Unsupervised Machine Learning for Scientific Discovery: Workflow and Best Practices
Department of Neuroscience, Baylor College of Medicine（神经科学系，贝勒医学院）；Department of Applied and Computational Mathematics and Statistics, University of Notre Dame（应用与计算数学和统计学系，圣母大学）；Department of Statistics, Columbia University（统计学系，哥伦比亚大学）；Zuckerman Institute & Irving Institute, Columbia University（Zuckerman研究所与Irving研究所，哥伦比亚大学）
Toward a Team of AI-made Scientists for Scientific Discovery from Gene Expression Data
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of California（加州大学）；Huazhong Agricultural University（华中农业大学）；Huazhong University of Science and Technology（华中科技大学）
DrSR: LLM based Scientific Equation Discovery with Dual Reasoning from Data and Experience
School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences（中国科学院大学先进交叉学科学院）；C 2 DL, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；School of Mathematical Sciences, University of Chinese Academy of Sciences（中国科学院大学数学科学学院）
Structured Scaling of AI Discovery Across Diverse Scientific Domains
Wizard Intelligence Learning Lab, Stanford University（智能巫师学习实验室，斯坦福大学）；Peking University（北京大学）；Tsinghua University（清华大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
Cyber Academia-Chemical Engineering (CA-ChemE): A Living Digital Town for Self-Directed Research Evolution and Emergent Scientific Discovery
State Key Laboratory of Heavy Oil Processing, College of Carbon Neutrality Future Technology, China University of Petroleum (Beijing), Beijing 102249, China（石油加工国家重点实验室，碳中和未来技术学院，中国石油大学（北京），北京102249）
The Use of AI-Robotic Systems for Scientific Discovery
Chalmers University of Technology（查尔姆斯理工大学）；The University of Manchester（曼彻斯特大学）；KTH Royal Institute of Technology（皇家理工学院）；Vanderbilt University（范德比大学）；University of Cambridge（剑桥大学）
