A5029666367- Machine Learning in Materials Science
- Computational Drug Discovery Methods
- Geochemistry and Geologic Mapping
- Grey System Theory Applications
- Metal Extraction and Bioleaching
- Advanced Welding Techniques Analysis
- Fatigue and fracture mechanics
- Conducting polymers and applications
- Perovskite Materials and Applications
- Electronic Packaging and Soldering Technologies
- Gas Sensing Nanomaterials and Sensors
- Non-Destructive Testing Techniques
- Aluminum Alloy Microstructure Properties
- Hydrogen embrittlement and corrosion behaviors in metals
- Industrial Vision Systems and Defect Detection
- Electronic and Structural Properties of Oxides
- Advanced Electron Microscopy Techniques and Applications
- Dyeing and Modifying Textile Fibers
- Dye analysis and toxicity
Shanghai University
2021-2024
Minor alloying is an effective method to improve the performance of lead-free solder alloys. In this study, we propose a complementary Machine Learning (ML) strategy for minor design alloys with enhanced creep resistance. Two ML models, leveraging compositional and knowledge-aware features, respectively, were constructed predict stress exponent Sn–Ag–Cu (SAC)-based Five new designed experimentally evaluated by screening virtual sample space consisting critical elements, including Bi, In, Ni,...
Machine learning (ML) accelerates the rational design and discovery of materials, where feature plays a critical role in ML model training. We propose low-cost electron probability waves (EPW) descriptor based on electronic structures, which is extracted from high-symmetry points Brillouin zone. In task distinguishing ferromagnetic or antiferromagnetic material, it achieves an accuracy (ACC) at 0.92 area under receiver operating characteristic curve (AUC) 0.83 by 10-fold cross-validation....