Special lssues
Table of Content

Integrated Computational Materials Engineering (ICME) Principles Guided Machine Learning for the Discovery, Development, and Deployment of Next Generation Structural Materials

Submission Deadline: 30 November 2024 Submit to Special Issue

Guest Editors

Dr. Junsheng Wang, Beijing Institute of Technology, China
Dr. Yuhong Zhao, University of Science and Technology Beijing, China
Dr. Yuling Lang, CITIC Dicastal Co., Ltd, China
Dr. Chi Zhang, Shenyang University of Technology, China
Dr. Mingshan Zhang, North China University of Science and Technology, China
Dr. Jiehua Li, Montanuniversität Leoben, Austria
Dr. Dongzhi Sun, sunCAE Consulting, Germany
Dr. Ruifeng Dou, University of Science and Technology Beijing, China


For decades, new materials Discovery, Development, and Deployment (3D) heavily depend on Experiences, Experiments, and Expenses (3E), which are not environmentally sustainable and socially desirable in nature. Recently, Integrated Computational Materials Engineering (ICME) has been pushing our knowledge boundary into the virtual design philosophy prior to actual manufacturing. However, the tedious process for establishing new physically based mathematical models at every manufacturing step and laborious work for verifying every model has prohibited many newcomers from entering this prospect field. As a result, this special issue is focusing on lifting the burden for developing a perfect ICME model for every material and instead welcome any new machining learning algorithms to solve the new materials Discovery, Development, and Deployment (3D) problems together with Integrated Computational Materials Engineering (ICME) Principles.


All articles concerning high strength aluminum alloys, magnesium alloys, titanium alloys, nickel-based superalloys, and their predictive models are welcome. This is an excellent opportunity for materials and computer scientists to present latest work on all aspects of materials Discovery, Development, and Deployment,including any non-destructive testing such as x-ray computed tomography and process optimization such as multiscale modeling for evaluating the properties for end applications.


High strength Al, Mg, Ti, and Ni-based alloys
Integrated Computational Materials Engineering (ICME)
Alloy design using first principles, Thermodynamics, ICME or AI
Machine learning using BP, DNN, CNN, ELM, SVM, RF, and etc
Artificial intelligence using OpenAI GPT, MeshGPT, AlphaFold, Uni-Fold, YOLO, UniDiff, R-CNN, etc
Image segmentation using AI-based feature extractions such as RNN, CNN, DNN, Transformer etc
In-situ observation using nano or micro scale X-ray Computed Tomography (XCT)
Kinetic predictions using cellular automata, phase field, Lattice-Boltzmann Methods (LBM)
Atomistic modeling using Density Functional Theory (DFT), Quantum Monte Carlo or Molecular Dynamics
Additive manufacturing using SLM, SLA, DLP, LCD, CLIP, TPP, and etc…
Process modeling, using CFD, FEA, LBM, etc

Share Link