AI-Designed Sustainable Conducting Polymers for Green Electronics

• Build a strong understanding of conducting polymers and their applications in green electronics.
• Introduce sustainability principles, eco-design, and regulatory standards for polymer materials.
• Demonstrate AI and ML techniques for predicting polymer properties and guiding material design.
• Provide hands-on experience with datasets, model building, and result interpretation.
• Explore real-world case studies and strategies for translating sustainable polymers from lab to market.
• Promote collaboration, innovation, and knowledge exchange among researchers and professionals.

📅 Day 1: Foundations – Conducting Polymers, Sustainability & AI

• Theme: Materials fundamentals + sustainability context + AI basics
• Introduction to conducting polymers for green electronics
• Overview of PEDOT, polyaniline, polypyrrole, and bio-based systems
• Charge transport mechanisms in polymers
• Applications in sensors, flexible devices, and energy storage
• Environmental challenges of conventional conducting polymers
• Bio-based and biodegradable conducting polymers
• Life Cycle Assessment (LCA) for electronic materials
• Toxicity, recyclability, eco-design principles, and regulatory standards
• AI & Materials Informatics fundamentals for polymer design
• Data sources: experiments, simulations, and literature mining
• Overview of ML, DL, generative AI, and end-to-end AI workflow for materials discovery

📅 Day 2: AI-Driven Design & Optimization

• Theme: Prediction, optimization, and sustainability integration
• Machine learning for conducting polymer property prediction
• Feature extraction: structure, dopants, and processing parameters
• Predicting conductivity, bandgap, stability, and mechanical properties
• Regression and classification models for materials performance
• Model evaluation techniques and uncertainty estimation
• AI-guided monomer and dopant selection
• Process optimization for greener synthesis routes
• Reinforcement and composite design (nanocellulose, graphene, CNTs)
• Reducing energy and chemical footprint using AI
• AI-enabled sustainability assessment and carbon footprint prediction
• Design-for-recycling, circular electronics, and trade-off analysis

📅 Day 3: Hands-On, Case Studies & Translation

• Theme: Practical exposure + real-world applications
• Hands-on AI session (Google Colab / Jupyter)
• Working with conducting polymer datasets
• Building ML models to predict conductivity or stability
• Visualization and interpretation of results
• Decision-making for sustainable material selection
• Case studies: AI-designed polymers for flexible and wearable electronics
• Sustainable sensors, bioelectronics, and energy harvesting applications
• Generative AI for novel polymer structure design
• Scaling sustainable conducting polymers from lab to market
• IP strategy, patents, and technology transfer
• Industry–academia collaboration models and funding opportunities
• Open discussion & participant interaction

• Understand conducting polymers and their applications in green electronics.
• Apply sustainability principles and eco-design in polymer development.
• Use AI/ML tools for polymer property prediction and design optimization.
• Gain hands-on experience with datasets, modeling, and result interpretation.
• Learn strategies for scaling and commercializing sustainable polymers.

• PhD Scholars & Research Students in Materials Science, Polymer Science, Electronics, Chemistry, or related fields.
• Academicians & Faculty Members interested in sustainable materials, green electronics, and AI-driven polymer research.
• Industry Professionals & R&D Experts working in electronics, polymer development, energy storage, or materials innovation.
• Professionals with basic knowledge of polymers, electronics, or AI/ML who wish to apply AI for sustainable material design.