Submission Deadline: 30 September 2025 View: 128 Submit to Special Issue
Prof. Yuhang Jing
Email: jingyh@hit.edu.cn
Affiliation: Harbin Institute of Technology, Harbin 150001, China
Research Interests: Process simulation, machine learning, multiscale, computational mechanics, thermal conduction
Machine learning has shown great potential to accelerate materials research. Compared with conventional computer simulations, machine learning can discover new materials and predict the properties of materials effectively. The aim of this Special Issue, titled Machine Learning in Materials Science, is to highlight the latest applications of machine learning in various areas of materials science. The issue will cover a broad spectrum of machine learning application in materials science, including but not limited to material detection, material analysis, and material design.
We invite original research articles, reviews, and perspectives that focus on machine learning and its application in the field of materials science. Topics of interest include, but are not limited to:
1.Prediction of material properties: Degradation detection, extraction of structure-property relationships, on-the-fly analysis of materials characterization data, development of machine learning potentials or machine learning forcefields.
2.Discovery of new materials: deep-learning-based generative design, discovery of chemical reactions, inverse design of materials, design microstructures that meet the performance requirements of materials; Optimize the process flow of material production.
3. Coupled mechanical-thermal phenomena: Experimental characterization of materials under simultaneous mechanical and thermal loads. Computational methods for coupled mechanics and thermal transport analysis. Theoretical frameworks linking mechanical deformation to thermal transport behavior.
4. Multiscale modeling and Cross-Scale Challenges: Bridging atomic, mesoscopic, and macroscopic scales in materials modeling. Cross-scale coupling of mechanical and thermal properties. Multiscale simulation frameworks and their validation.
We anticipate that this collection will be of great interest to researchers working towards the machine learning in materials science.
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