It is a great honor and pleasure for us to invite you to attend the 2027 International Conference on Materials AI Discovery (MAID 2027), which is organized by the PolyU-SCUT Centre for Advanced and Green Composite Materials, The Hong Kong Polytechnic University and co-organized by the Hong Kong Society of Mechanical Engineers (HKSME). The conference will be held in Hong Kong, China from August 13 to 15, 2027.
With the theme of "AI-Driven Discovery of Novel Materials and Chemical Space Exploration", MAID 2027 will serve as an international forum for presenting and exchanging technological advances and research results in the broad fields of AI-driven materials discovery, computational materials science, and intelligent manufacturing. It aims to bring together leading researchers, engineers, and scientists from around the world to share the latest research findings and engineering practices. We warmly invite prospective authors and participants to join MAID 2027 and contribute to the advancement of AI-driven materials discovery and chemical space exploration.
1) All the registered and presented papers will be submitted to the publisher for review, and papers that meet the requirements will be included in the volume of conference proceeding (online publishing). The publisher will submit articles to Engineering Village, Scopus, Web of Science and other databases for review and indexing after publication.
2) Selected papers with high quality and great extension will be recommended to publish in international journals.
MAID 2027 is scheduled to include high-quality paper presentation sessions revealing the latest research findings, and to engage participants in interesting discussion sessions. Topics of interest for submission include, but are not limited to:
I. AI-Driven Materials Discovery and Design
II. Computational Materials Science & Modeling
III. Autonomous Laboratories & Robotics
IV. Chemical Space Exploration & Screening
V. Sustainable & Green Materials
VI. Intelligent Manufacturing & Process Optimization
VII. Data-Driven Characterization & Analysis
VIII. Machine Learning for Materials Informatics
Authors must adhere to the conference written paper format. Please choose from the templates listed as follow based on your own request:
All papers will be peer reviewed, and each paper will be reviewed by 2β3 experts. It takes 20β30 days for the result to be released. If the paper needs revision, it should be resubmitted for peer review again. Submitted papers are evaluated using a double-blind review process for originality, relevance, and presentation. Reviewers for a paper will be kept anonymous.
Authors can submit manuscripts through the CMT electronic submission system. Please click the following link:
Electronic Submission System β
π The submission system will be open on Oct. 30, 2026.
π‘ Pro Tip: If you encounter any issues while registering for a CMT account or logging in with Google Chrome, we recommend switching to Microsoft Edge. For any further assistance, please contact maid@smehk.org, and the conference secretariat will respond as soon as possible.
First-time users should register an account first, then upload their paper to the system. Previous users can log in to the CMT system directly. If your paper needs to be updated after you receive the submission confirmation, please send the updated paper to the conference email maid@smehk.org directly; please do not update it in the Electronic Submission System.
Abstract Submission Deadline: February 1, 2027
Full Paper Submission: March 1, 2027
Notification of Acceptance: April 1, 2027
Abstract Submission Deadline: April 5, 2027
Notification of Acceptance: May 5, 2027
Registration Deadline: June 5, 2027
Conference Dates: August 13-15, 2027
Each participant needs to register. Early-bird rates are available until March 31, 2027. Please follow the instruction in the notification of acceptance (if applicable) or register as a listener. Free listener slots are available for plenary sessions on August 14, 2027 (no meals included). More information on registration can be found on the Registration page.





