Guest Editors
Dr. Lin Qiu, University of Science and Technology Beijing, China
Dr. Hanying Zou, University of Science and Technology Beijing, China
Prof. Yanhui Feng, University of Science and Technology Beijing, China
Prof. Huansheng Ning, University of Science and Technology Beijing, China
Dr. Cheng Chen, University of Science and Technology Beijing, China
Summary
Recently, rapidly advancing computer technology has opened up a novel approach to materials research and results in the explosion of machine learning-assisted on material field, including the classical machine learning algorithms (such as Genetic algorithms and Random forests) and deep learning (such as Convolutional Neural network, Generative Adversarial Network and Transformer). They have played an important role in various aspects of the materials for new material design, material synthesis, structural analysis, properties prediction, simulation research, and structural optimization. Subsequently, it significantly speeds up research progress and unearths more latent features from the computational perspective, such as structure-property relationships. Therefore, more method innovations will change the research mode of materials with the development of computer technology. This special issue aims to discuss the application of machine learning technology in materials, including the update of computational methods, new modeling methods, more accurate performance prediction models, analysis methods of material images, etc. We cordially invite you to contribute to this special issue. Both original research and topical reviews will be considered and the topic scope of reviews require a discussion with Guest Editors.
The topics to be discussed in this special issue but are not limited to:
• Prediction of crystal structure and molecular properties
• Prediction of chemical order and structure of materials (such as alloy nanoparticle and catalyst)
• Interatomic potential function design
• Correlation between molecular structure with chemical and physical properties
• Prediction of chemical reaction parameters (reaction rate, reaction temperature, etc.)
• Prediction and analysis of material transitions
• Characterization of 2D image data (SEM images, photographs of material surfaces, etc.) and image-based model reconstruction
• Establishment of Image-Structure-Performance Correlation
• Small sample data augmentation based on image generation
• Applications of the machine learning search tool for material design and new physics discover.
Keywords
Machine Learning, Artificial Intelligence, Data-driven Prediction, Image Analysis, Material Design and Discover
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