AMDS Symposium G: Data Driven Materials Design

• SevenNet: a pretrained universal machine learning force fields 2024.11.26
• Intelligent design of Al alloy by integrating CALPHAD, machine learning and key experiments 2024.11.26
• High-throughput calculations of 2D heterostructures 2026.11.26
• Advancing the Development of Polymer Composites Using Artificial Intelligence 2024.11.26
• Data-driven Exploration for Li Ionic Conductor 2024.11.26
• Web-based Interface for Search and Analysis of Materials Data: ChemDX and MatDX 2026.11.26
• Material Genome Engineering in Catalysis 2024.11.26
• Computational Designs of Heterogeneous and Photoactive Catalysts: Multi-scale Simulations 2024.11.26
• Overcoming Small Dataset Challenges in Semantic Segmentation of Metallographic Microscopy Images 2024.11.26
• Design and development of high performance aluminum alloy based on integrated computing and mach... 2024.11.26
• New AI Algorithms for Materials Science 2024.11.26
• KAN Made Learning Physics Laws Simple 2024.11.26
• Finding New High-Temperature Superconductors Based on Crystal Graph Neural Networks 2024.11.26
• Innovative Applications of AI to Mechanical/Materials Processing 2024.11.26
• “What you need is pre-attention”: Small-data machine learning with center-environment features 2024.11.26
• Deep learning application for modeling the heat treatment condition-microstructure-property relat... 2024.11.26
• Design and Screening of Functional Organic Molecules for Perovskite Solar Cells via Machine Lear... 2024.11.26
• MGED-Assistant：Smart Material Database Powered by LLM 2024.11.26
• Noise-aware active learning to develop high-temperature shape memory alloys with large latent heat 2024.11.26