Programme Parameters
Graduate / Intermediate
6 Weeks (60 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Master Reinforcement Learning for Battery & Material Science brings sequential decision-making to the discovery of new materials. You build on RL fundamentals and apply them to materials problems: guiding autonomous experimentation and design-of-experiments, navigating vast materials search spaces toward target properties, and optimising battery and energy-material formulations. The course connects RL’s explore-exploit power to the costly, sequential nature of materials research. You finish able to reason about applying RL to a battery or materials-discovery problem. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course applies reinforcement learning to battery and materials science — using RL to guide materials discovery, experiment design and optimisation in energy materials.
📋 Course Objectives
1. Apply RL fundamentals to materials problems.
2. Guide autonomous experimentation with RL.
3. Navigate materials search spaces to targets.
4. Optimise battery and material formulations.
5. Balance exploration and exploitation in discovery.
👥 Who Should Enroll?
• Materials and battery researchers
• ML scientists in materials
• Energy-materials R&D professionals
• Students of computational materials
🚀 Key Learning Outcomes
• An understanding of RL for materials science.
• An accelerated-discovery perspective.
• A materials-RL project.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
AI Fundamentals, Mathematics, and Reinforcement Learning For Battery & Material Science Foundations
Implement Artificial Intelligence with Learning for practical ai fundamentals, mathematics, and reinforcement learning for battery & material science foundations applications and outcomes. • Design Master with Reinforcement for practical ai fundamentals, mathematics, and reinforcement learning for battery & material science foundations applications and outcomes. • Analyze Artificial Intelligence with Learning for practical ai fundamentals, mathematics, and reinforcement learning for battery & material science foundations applications and outcomes.
Data Engineering, Preprocessing, and Feature Pipelines
Implement Artificial Intelligence with Learning for practical data engineering, preprocessing, and feature pipelines applications and outcomes. • Design Master with Reinforcement for practical data engineering, preprocessing, and feature pipelines applications and outcomes. • Analyze Artificial Intelligence with Learning for practical data engineering, preprocessing, and feature pipelines applications and outcomes.
Model Architecture, Algorithm Design, and Reinforcement Learning For Battery & Material Science Methods
Implement Artificial Intelligence with Learning for practical model architecture, algorithm design, and reinforcement learning for battery & material science methods applications and outcomes. • Design Master with Reinforcement for practical model architecture, algorithm design, and reinforcement learning for battery & material science methods applications and outcomes. • Analyze Artificial Intelligence with Learning for practical model architecture, algorithm design, and reinforcement learning for battery & material science methods applications and outcomes.
Training, Hyperparameter Optimization, and Evaluation
Implement Artificial Intelligence with Learning for practical training, hyperparameter optimization, and evaluation applications and outcomes. • Design Master with Reinforcement for practical training, hyperparameter optimization, and evaluation applications and outcomes. Gain hands-on experience and produce real-world projects. • Analyze Artificial Intelligence with Learning for practical training, hyperparameter optimization, and evaluation applications and outcomes.
Deployment, MLOps, and Production Workflows
Implement Artificial Intelligence with Learning for practical deployment, mlops, and production workflows applications and outcomes. • Design Master with Reinforcement for practical deployment, mlops, and production workflows applications and outcomes. Gain hands-on experience and produce real-world projects. • Analyze Artificial Intelligence with Learning for practical deployment, mlops, and production workflows applications and outcomes.
Ethics, Bias Mitigation, and Responsible AI Practices
Implement Artificial Intelligence with Learning for practical ethics, bias mitigation, and responsible ai practices applications and outcomes. • Design Master with Reinforcement for practical ethics, bias mitigation, and responsible ai practices applications and outcomes. • Analyze Artificial Intelligence with Learning for practical ethics, bias mitigation, and responsible ai practices applications and outcomes.
Industry Integration, Business Applications, and Case Studies
Implement Artificial Intelligence with Learning for practical industry integration, business applications, and case studies applications and outcomes. • Design Master with Reinforcement for practical industry integration, business applications, and case studies applications and outcomes. • Analyze Artificial Intelligence with Learning for practical industry integration, business applications, and case studies applications and outcomes.
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Artificial Intelligence |
| Covered Tool / Platform | Learning |
| Covered Tool / Platform | Master |
| Covered Tool / Platform | Reinforcement |
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