Submission Deadline: 31 July 2025 View: 142 Submit to Special Issue
Dr. Dong Zhai
Email: zhaidong@sdu.edu.cn
Affiliation: Institute of Molecular Science and Engineering, Shandong University, Qingdao City, 266237, China
Research Interests: computational materials, materials genome, functional and catalytic materials, molecular simulation, artificial intelligence-aided materials design
Artificial Intelligence (AI) is transforming computational materials science by enabling faster and more accurate predictions of material properties, accelerating material discovery, and optimizing design processes. AI techniques such as machine learning (ML) and deep learning (DL) help explore vast material spaces, predict material behaviors, and improve production efficiency. These advancements are crucial for addressing challenges in energy, electronics, manufacturing, and sustainability, driving innovation in material design and performance.
Aim and Scope of the Special Issue
This special issue aims to highlight the latest applications of AI in materials science, focusing on how AI is integrated with computational methods to enhance material discovery, property prediction, and process optimization. It covers both theoretical advancements and practical applications across various research fields.
Suggested Themes
• Machine Learning for Property Prediction: AI models for predicting material properties.
• AI in Materials Informatics: AI for managing and analyzing materials data.
• Accelerating Materials Discovery: AI for discovering new materials in various research fields.
• AI for High-Throughput Screening: Using AI to automate and accelerate the screening of large material databases for desirable properties.
• AI-Enhanced Simulations: Improving traditional simulations with AI.
• Explainability and Interpretability in AI Models: Advances in making AI models more transparent and interpretable in the context of materials science.
• Process Optimization: AI in manufacturing and material fabrication.
• Failure Prediction and Reliability: AI for predicting material failure and ensuring reliability.
This issue provides a concise overview of how AI is revolutionizing materials science, offering insights for researchers and professionals seeking to understand the evolving role of AI in the field.
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