ICMLC 2027 will be held in Shenzhen, China on February 26-March 01, 2027. The ICMLC 2027 organizing committees invite you to submit the papers to special session. Each special session will be arranged for around 2 hours on Feb. 27 or 28's afternoon. Now the information on special session as following:

Topic: Artificial Intelligence for Scientific Discovery and Data-Driven Modeling (AI4Science)

Organizers | 组织者

Co-organizers | 联合组织者

Lu Li, Sun Yat-sen University, China
Davide Bianchi, Sun Yat-sen University
Zhidong Zhang, Sun Yat-sen University
Jiwang Ma, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)
Chao Xiong, Hunan University Of Technology and Business

 

Artificial Intelligence for Science (AI4Science) is rapidly transforming the way scientific knowledge is generated, analyzed, and applied. By integrating advances in machine learning, deep learning, foundation models, scientific computing, and domain knowledge, AI-driven approaches are accelerating discovery across a wide range of disciplines, including materials science, chemistry, physics, biology, environmental science, and engineering. This special session aims to bring together researchers from both AI and scientific domains to discuss novel methodologies, theoretical foundations, computational frameworks, and real-world applications that enable data-driven scientific discovery. Particular emphasis will be placed on trustworthy, interpretable, and efficient AI methods that can address challenges such as limited data availability, uncertainty quantification, multimodal scientific data integration, and physics-informed learning. The session seeks to foster interdisciplinary collaboration and provide a platform for presenting cutting-edge research that advances the synergy between artificial intelligence and scientific investigation.

The scopes as following:
• Machine learning for scientific discovery
• Foundation models and large language models for science
• Physics-informed and scientific machine learning
• Data-driven causal discovery
• AI for materials science and materials discovery
• AI for chemistry, molecular design, and drug discovery
• AI applications in physics and engineering sciences
• AI for climate, environmental, and Earth system sciences
• AI for medical imaging, and health related applications
• Multimodal learning for scientific data
• Geometric deep learning and graph neural networks for science
• Neural operators and scientific computing
• Uncertainty quantification and trustworthy AI for science
• Explainable and interpretable AI in scientific applications
• Digital twins and data-driven simulation

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Welcome to submit more proposal on ICMLC 2027 special session, please download:
 Proposals Submission Guidelines.

Learn more details or submit the proposal, please contact us:
Ms. Doris Ge
Email: icmlc@vip.126.com
Tel: +86-13709044746