Discovery Science 2026 (International Conference on Discovery Science) is a ICORE B / QUALIS B1 conference held in Mainz, Germany on 2026-10-05. The paper submission deadline is 2026-06-15 (extended). Acceptance notifications are sent on 2026-07-21.
We invite submissions to the 29th International Conference on Discovery Science, welcoming paper submissions from all areas relevant to artificial intelligence and data science.
The Discovery Science 2026 conference provides an open forum for intensive discussions and exchange of new ideas among researchers working in different areas of Artificial Intelligence and the data sciences, focusing on discovering and advancing scientific knowledge. Its scope includes developing and analyzing methods for discovering scientific knowledge, coming from machine learning, data mining, intelligent data analysis, and big data analytics, as well as their application in various domains of physical, life, environmental, natural and social sciences. This year, we particularly invite submissions dealing with methodological contributions to the automation or partial automation of discovery in various domains of the natural and human sciences, from a methodological perspective.
Discovery Science 2026 is part of the AI4Science Week and co-located with the 2nd International Conference on Artificial Intelligence for Science (AI4Sci 2026), which features separate AI4Physics, AI4Chemistry&Materials, AI4LifeSciences, and AI4Humanities&SocialSciences tracks dealing with applications of AI and Machine Learning. You are welcome to submit abstracts in any of those tracks as well, especially if your contribution focuses on the application of existing techniques to a specific field of scientific inquiry. Contrary to the main track of Discovery Science 2026, note that AI4Sci tracks are non-archival and also suited to ongoing or already published work, similarly to workshops co-located with other major conferences.
Aims & Scope
The current call for papers is tentative, and will be updated during the year. This year's conference focuses on the following topics:
Automated and semi-automated applications of Machine Learning and Artificial
Intelligence in the discovery of scientific knowledge
Benchmarking methodologies for the discovery of scientific knowledge
Agentic AI methods and applications in scientific discovery
Retrieval-Augmented Generation methods for the automation, both partial and full, of the scientific process
Hypothesis generation methodologies, both causal and observational
Causal discovery for the scientific process
Methods for the integration of Machine Learning and AI in scientific laboratories, both at the hardware and software level
Computational equation discovery and Symbolic Regression
Methods and applications for safe and trustworthy AI (e.g. private, fair, transparent, sustainable, efficient) in the sciences, esp. social sciences and humanities
While the intended emphasis of the conference is on the above topics, we also value and welcome contributions dealing with more general Machine Learning, Data Mining and AI topics:
Machine Learning, including supervised learning, unsupervised learning, semi-supervised learning, self-supervised learning
Active learning, online learning, transfer learning, continual learning etc.
Reinforcement learning
AutoML, Meta-Learning, Planning to Learn
Representation learning for vision, text, audio, language, and other data modalities
Knowledge Discovery and Data Mining
Anomaly and Outlier Detection
Causal Modeling and reasoning
Neuro-symbolic learning & hybrid AI systems (logic & formal reasoning, etc.)
Physics-informed machine learning
Data and Knowledge Visualization
Explainable AI and Interpretable Machine Learning
Human-Machine Interaction for Knowledge Discovery and Management
AI and High-performance Computing, Grid and Cloud Computing
Optimisation
AI Creativity
Learning from Complex Data
Data Streams, Evolving Data, Change Detection & Concept drift
Time-Series Analysis
Spatial, Temporal and Spatio-temporal Data Analysis
Unstructured Data Analysis (textual and web data)
Learning on graphs and topological deep learning
Complex Network Analysis
Process Discovery and Analysis
Evaluation of Models and Predictions in Discovery Setting
Applications of the above techniques in scientific domains, such as physical sciences (e.g., materials sciences, particle physics), life sciences (e.g., biology, medicine, neuroscience etc.), environmental sciences, natural and social sciences.
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