About the Journal

Aims & Scope

Artificial Intelligence for Sustainable Materials is an international, peer-reviewed journal dedicated to advancing the application of artificial intelligence (AI), machine learning (ML), and computational technologies in the design, development, and lifecycle management of sustainable materials. The journal aims to accelerate innovation in eco-friendly polymers, composites, nanomaterials, and advanced functional materials by leveraging data-driven approaches to address global challenges in resource efficiency, recyclability, and environmental impact.

The journal welcomes original research, reviews, and technical notes in areas including, but not limited to:

  • AI-driven materials informatics for predicting properties and optimizing performance
  • Machine learning for sustainable polymer design and processing
  • Generative AI and inverse design for novel green material architectures
  • Lifecycle analysis and environmental impact modeling using AI
  • Integration of AI in circular economy and green manufacturing strategies
  • Data analytics for material sustainability metrics and decision-making
  • Applications in renewable energy materials, biopolymers, and biodegradable systems
  • Hybrid approaches combining physics-based modeling and AI for sustainable solutions

The journal seeks to foster interdisciplinary collaboration between materials scientists, computational researchers, and sustainability experts, creating a platform for cutting-edge research that drives green innovation and climate-conscious technologies.

Article Processing Charge (APC)

In this journal, we do not apply APC to process or publish articles.

Access

All articles in the journal are Open Access (OA).

Frequency

This is a quarterly journal in which four issues are published per year.

Copyright notice

Authors retain copyright over their materials and are free to copy, redistribute, remix, transform, and build upon the material since all the papers are published under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.

Archiving

All the articles published by EAI are archived using Portico's e-journal preservation service, together with CLOCKSS and LOCKSS. This ensures that all articles published are preserved and available for future researchers and students.

Privacy statement

The names and email addresses entered in this journal site will be used exclusively for the stated purposes of EAI and will not be made available for any other purpose or to any other party.