This project explores the formulation of smart coatings - advanced paints with enhanced functionalities such as self-healing, UV resistance, corrosion protection, antimicrobial action, and thermochromism- using a Genetic Algorithm (GA)-based computational approach.
Our Python model simulates the optimization of coating formulations by balancing performance and cost. This in-silico approach drastically reduces R&D time and expenses, unlocking rapid innovation for the paints and coatings industry.
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AI-Driven Optimization of coating properties:
- Durability
- Self-Healing
- UV Resistance
- Corrosion Resistance
- Miscibility
- Cost
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Customizable Fitness Function to prioritize specific industrial needs
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Evolutionary Techniques: Selection, Crossover, Mutation
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Early Stopping Mechanism for efficient convergence
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Material Database: A dictionary containing multiple base materials and their normalized properties across 9+ parameters.
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Fitness Function: Weighted scoring system based on desirable traits and cost penalties.
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Genetic Algorithm Process:
- Initialize random population of formulations
- Evaluate fitness of each formulation
- Select top-performing "parents"
- Apply crossover and mutation
- Iterate for multiple generations
- Return the optimal formulation
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Self-Healing Coatings https://coatings.specialchem.com/centers/self-healing-coatings/how-does-it-work
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Thermochromic & Photochromic Coatings https://www.sciencedirect.com/topics/earth-and-planetary-sciences/thermochromic-coating
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Polymer Chain Dynamics & Nanoparticles https://pubs.acs.org/doi/full/10.1021/acs.jpcb.7b02502
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Smart Coating Market Analysis https://www.mordorintelligence.com/industry-reports/smart-coatings-market
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Thermochromic Phase Transitions & Chromophores https://www.mdpi.com/2310-2861/7/3/77
- Interdisciplinary Insight: Blending polymer chemistry with computational algorithms creates impactful industry solutions.
- Research Depth: Gained strong grounding in literature related to self-healing, thermochromic, antimicrobial, and photocatalytic coatings.
- GA Implementation: Hands-on application of genetic algorithms helped deepen understanding of evolutionary computation and real-world constraints.
- Property Trade-offs: Optimizing for cost often compromises performance—a key industrial design insight.
- Sustainability Emphasis: Exposure to eco-conscious formulation practices shaped our vision for future material design.
- Integrate ML-based property prediction models for faster optimization
- Expand the material database to include bio-based or green alternatives
- Validate computational results via pilot-scale formulation and testing
- Apply similar modeling to smart textiles, wearables, and biosensors
- Collaborate with industry partners for real-world deployment
- Aarav Gupta (230008001)
- Mohak Dadhich (230008023)
- Prakrut Moon (230008024)
- Priyanshu Patel (230008026)
This might find funny but we made a logo for our project too
