Symposium 4：Materials Database and Big Data Technologies

• S-4-01 Active Learning Guided Materials Synthesis and Full-map Understanding 2021.03.19
• S-4-03 MGE Database and Application in Materials Science 2021.03.19
• S-4-04 Simple Descriptor Derived from Symbolic Regression Accelerating the Discovery of New Perov... 2021.03.19
• S-4-05 Biomedical Coating Combination Optimized by High Throughput Biochip and Cell Image Machine... 2021.03.19
• S-4-06 An AI Framework and Program for Rule-based Formula Discovery from Small and Noised Data 2021.03.19
• S-4-07 Automated Pipeline For Superalloy Property Extraction by Text Mining from Materials Scienc... 2021.03.19
• S-4-08 A General Method to Determine Universal Formula for Given Database 2021.03.19
• S-4-09 Digging out Precious Information Hidden Behind Indirect Experimental Data of Fiber-reinfor... 2021.03.19
• S-4-10 Property Prediction of Maraging Steels: Machine Learning vs. Physical Metallurgical Modelling 2021.03.19
• S-4-11 Artificial Intelligence and Data-driven Materials Exploration 2021.03.19
• S-4-12 Machine Learning-driven Discovery of New Thermal Transport Mechanisms in Porous Materials 2021.03.19
• S-4-13 Design Practice and Method Modeling of Composite Material Chip Metadata Based on MGE Data ... 2021.03.19
• S-4-14 Machine Learning for A Small Amount of Samples Labeled 2021.03.19
• S-4-15 Machine Learned Force Field for Simulating Structural Phase Transformation under High Pres... 2021.03.19
• S-4-16 A Generic and Extensible Prediction Method for Martensitic Transformation Temperature Comb... 2021.03.19
• S-4-17 Screen and Optimize Material Key Factors through Fuel Rod Performance Analysis 2021.03.19
• S-4-18 The Most Comprehensive Database of Ionic Transport Characteristics to Date 2021.03.19
• S-4-19 Rapid Establishment and High Returns of Material Genomic Data Platforms 2021.03.19
• S-4-20 Matgen: A Material Design Platform Integrating High-throughput Calculation, Automated Work... 2021.03.19
• S-4-21 From MGI to Material Digital Research 2021.03.19
• S-4-22 Materials Intelligent Design: Software, Databases and Case Studies 2021.03.19
• S-4-23 Standards System for Materials Genome Engineering Data 2021.03.19
• S-4-24 Superalloy Database in Materials Genome Engineering 2021.03.19
• S-4-25 Cluster-formula-embedded Machine Learning for Design of Multi-component β-Ti Alloys with ... 2021.03.19
• S-4-26 Microstructure Segmentation and Quantification of Advanced Steels Combining EBSD and Deep ... 2021.03.19
• S-4-27 Deep Learning and Shape Aware Based Image Segmentation for Polycrystalline Micrographic image 2021.03.19
• S-4-28 Design and Optimization of Thermal Conductivity of UO2 Composite Fuel Based on Finite Elem... 2021.03.19
• S-4-29 Data Mining of Constitutive Relationships in Ferroelectricity Based on Machine Learning 2021.03.19
• S-4-30 A Method Based on Deep Learning for Statistics of Dendrite Spacing in Single Crystal Super... 2021.03.19
• S-4-31 Prediction of Tc Upper Limits for Superconducting Materials Assisted by Machine Learning 2021.03.19
• S-4-32 Machine Learning Analysis of Tunnel Magnetoresistance of Magnetic Tunnel Junctions with Di... 2021.03.19
• S-4-33 Solid-state Chemistry of Actinides 2021.03.19
• S-4-34 A Fast Phase-field Model for Polycrystalline Solidification and the Cross-scale Simulation... 2021.03.19
• S-4-35 Active Learning to Accelerate the Search for New Materials with Emphasis on Domain Knowledge 2021.03.19