Tackling the most pressing problems for humanity, such as the climate crisis and the threat of global pandemics, requires accelerating the pace of scientific discovery. While science has traditionally relied on trial and error and even serendipity to a large extent, the last few decades have seen a surge of data-driven scientific discoveries. However, in order to truly leverage large-scale data sets and high-throughput experimental setups, machine learning methods will need to be further improved and better integrated in the scientific discovery pipeline. A key challenge for current machine learning methods in this context is the efficient exploration of very large search spaces, which requires techniques for estimating reducible (epistemic) uncertainty and generating sets of diverse and informative experiments to perform. This motivated a new probabilistic machine learning framework called GFlowNets, which can be applied in the modeling, hypotheses generation and experimental design stages of the experimental science loop. GFlowNets learn to sample from a distribution given indirectly by a reward function corresponding to an unnormalized probability, which enables sampling diverse, high-reward candidates. GFlowNets can also be used to form efficient and amortized Bayesian posterior estimators for causal models conditioned on the already acquired experimental data. Having such posterior models can then provide estimators of epistemic uncertainty and information gain that can drive an experimental design policy. Altogether, here we will argue that GFlowNets can become a valuable tool for AI-driven scientific discovery, especially in scenarios of very large candidate spaces where we have access to cheap but inaccurate measurements or to expensive but accurate measurements. This is a common setting in the context of drug and material discovery, which we use as examples throughout the paper.
DynGFN: Towards Bayesian Inference of Gene Regulatory Networks with GFlowNets
University of Toronto（多伦多大学）；Vector Institute（向量研究所）；Mila - Quebec AI Institute（Mila-魁北克人工智能研究所）；University Health Network（大学健康网络）；Université de Montréal（蒙特利尔大学）；Valence Labs（Valence实验室）
Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation
Technical University of Munich（慕尼黑工业大学）；Mila - Quebec AI Institute（米拉 - 魁北克人工智能研究所）；ServiceNow Research（ServiceNow研究院）；Université de Montréal（蒙特利尔大学）；Helmholtz AI（亥姆霍兹人工智能研究所）
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）
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）
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）
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）
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））
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）
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）
