Call for Papers

International Conference on AI-Driven Materials, Energy and Smart Technologies (ICAIMEST)

The International Conference on AI-Driven Materials, Energy and Smart Technologies (ICAIMEST) invites researchers, academicians, industry professionals, and practitioners to submit original and unpublished research contributions. The conference aims to provide a global platform to discuss recent advances, innovations, and emerging challenges at the intersection of Artificial Intelligence, advanced materials, sustainable energy, and smart technological systems.

ICAIMEST focuses on interdisciplinary research that leverages intelligent computational approaches to accelerate breakthroughs in materials discovery, energy sustainability, and smart engineering solutions. Accepted and presented papers will be considered for publication in reputed indexed conference proceedings (subject to publisher approval and review process).

Conference Themes / Tracks

Authors are invited to submit papers in (but not limited to) the following areas:

Track 1: AI for Materials Science
  • AI/ML for materials discovery and design
  • Computational materials modeling and simulation
  • Data-driven materials characterization
  • Nanomaterials, functional materials, smart materials
  • Materials informatics and digital twins
  • Additive manufacturing and intelligent fabrication
Track 2: Energy Systems and Sustainability
  • Renewable energy technologies (solar, wind, hydrogen, etc.)
  • Energy storage systems and batteries
  • Smart grids and energy management
  • AI in energy forecasting and optimization
  • Sustainable and green energy materials
  • Power electronics and electrification technologies
Track 3: Smart Technologies and Intelligent Systems
  • IoT-enabled smart infrastructure
  • Cyber-physical and embedded systems
  • Intelligent sensing and automation
  • Robotics and autonomous systems
  • Smart cities and digital transformation
  • Industry 4.0 / 5.0 innovations
Track 4: Core AI & Data Technologies
  • Machine learning and deep learning methods
  • Computer vision and pattern recognition
  • Natural language processing
  • Big data analytics and cloud computing
  • Edge AI and federated learning
  • Explainable and trustworthy AI
Track 5: Emerging Interdisciplinary Applications
  • AI in healthcare technologies
  • Environmental monitoring and sustainability
  • Smart transportation and mobility
  • AI for climate and societal impact
  • Ethical and responsible AI deployment