Symposium E (FMGE-AMDS Joint): Materials Big Data and AI for Science

• How can theory guide data science? 2024.11.26
• Computational Materials Discovery in the Age of Automation 2024.11.26
• Artiffcial Intelligence Design of Lithium Battery Electrolytes 2024.11.26
• Bridging physics-informed and data-driven materials designs to catalyze deep decarbonization 2024.11.26
• Data Resource Construction for Condensed matter quantum material science and AI empowerment 2024.11.26
• Artiffcial intelligence-enhanced atom probe microscopy: Local chemical ordering analysis 2024.11.26
• Machine learning empowered material atomic structural understanding 2024.11.26
• Domain Knowledge Embedded Materials Data Mining 2024.11.26
• Small and Large Atomic/Language Models for Materials Science 2024.11.26
• MatPilot: an LLM-enabled AI Materials Scientist under the Framework of Human-Machine Collaboration 2024.11.26
• Steel design based on a large language model 2024.11.26
• Genetic Engineering of Polymer Materials: Small Data, Model Interpretability, and Large Language... 2024.11.26
• Empowering Material Knowledge Extraction with Large Language Models 2024.11.26
• Enhancing Materials Research through LLM: Building a Comprehensive Platform and Facilitating Kno... 2024.11.26
• Microstructure and mechanism information-guided multimodal data analysis and design for steel ma... 2024.11.26
• The Design and Application of Noble Metal-Based Superalloys Driven by the Synergy of Physical Mo... 2024.11.26
• Alloy Design Based on Artiffcial Intelligence and Machine Learning 2024.11.26
• Integration and Development of Physical Models and Artiffcial Intelligence in Alloy Design 2024.11.26
• Multi-objective intelligent optimization design and development of new superalloys 2024.11.26
• Deep learning strengthening mechanism for high ffdelity inverse design of microstructure 2024.11.26