Symposium 4：Materials Database and Big Data Technologies

• S6-13 Some New Machine Learning Methods for Materials Data Analysis 2022.01.04
• S6-01 基于机器学习算法的新型功能材料靶向设计 2022.01.04
• S6-02 机器学习辅助的材料模拟 2022.01.04
• S6-03 Deep learning strategy for limited and imbalanced data 2022.01.04
• S6-04 Machine learning as a tool to analyze biological responses to nanomaterials 2022.01.04
• S6-05 The system design and the standard framework of material genome engineering data 2022.01.04
• S6-06 Energy Materials Innovation Based on Big Data and Machine Learning 2022.01.04
• S6-07 Design the metastable austenite-containing advanced steels based on machine learning 2022.01.04
• S6-08 A Brief Introduction to “Atomly” Computational Materials Database 2022.01.04
• S6-09 Data driven biomedical interface material biology 2022.01.04
• S6-10 Fatigue property prediction and alloy design based on transfer learning for extremely small... 2022.01.04
• S6-11 Rippling ferroic phase transition and domain switching in two-dimensional materials 2022.01.04
• S6-12 Machine learning assisted empirical formula augmentation 2022.01.04
• S6-14 Diagram and phase transition features 2022.01.04
• S6-15 Machine Learning Accelerated Materials discovery Using Scarce Data 2022.01.04
• S6-16 CNMGE: the High-Throughput Computing Infrastructure for Materials Simulation and Big-Data M... 2022.01.04
• S6-17 Optimization of ultrashort pulse laser micromachining process based on machine learning and... 2022.01.04
• S6-18 Study of Complex Thermal Transport Enabled by Machine Learning Potential 2022.01.04