Programme Parameters
Graduate / Intermediate
4 Weeks (40 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
The Battery Genome Project: AI for Energy Storage explores how data and machine learning are accelerating the search for better batteries — central to the clean-energy transition. You learn how the enormous design space of battery materials and chemistries is being tackled with materials informatics: predicting properties, screening candidate electrode and electrolyte materials, and optimising formulations far faster than experiment alone. The course also covers AI for battery performance and lifetime prediction from cycling data. Connecting materials science to machine learning, it shows the data-driven future of energy storage. You finish able to reason about AI-driven battery discovery. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course applies AI to energy storage — using machine learning and materials data to accelerate the discovery, design and optimisation of next-generation batteries.
📋 Course Objectives
1. Explain the battery-materials design space.
2. Predict material properties with machine learning.
3. Screen electrode and electrolyte candidates.
4. Predict battery performance and lifetime from data.
5. Connect materials informatics to energy storage.
👥 Who Should Enroll?
• Materials scientists and battery researchers
• Data scientists in energy and materials
• Clean-energy and R&D professionals
• Students of materials informatics
🚀 Key Learning Outcomes
• An understanding of AI in battery discovery.
• The ability to reason about materials-informatics workflows.
• A foundation in data-driven energy storage.
• 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 The Battery Genome Project Ai For Energy Storage Foundations
Develop a comprehensive understanding of artificial intelligence and machine learning fundamentals, including supervised and unsupervised learning techniques, to apply to energy storage problems • Analyze mathematical concepts, such as linear algebra and calculus, to understand the underlying principles of AI and energy storage modeling • Design and implement basic AI models using Python and relevant libraries to solve energy storage-related problems
Data Engineering, Preprocessing, and Feature Pipelines
Configure and manage large datasets related to energy storage using data engineering techniques, including data ingestion, processing, and storage • Evaluate and preprocess energy storage data to ensure quality, integrity, and relevance for AI model training • Develop and implement feature pipelines to extract relevant features from energy storage data, enhancing AI model performance
Model Architecture, Algorithm Design, and The Battery Genome Project Ai For Energy Storage Methods
Design and implement deep learning architectures, such as convolutional neural networks and recurrent neural networks, to model complex energy storage systems • Analyze and compare different algorithmic approaches, including reinforcement learning and transfer learning, to optimize energy storage performance • Develop and evaluate custom AI models using techniques like ensemble learning and gradient boosting to improve energy storage forecasting and optimization
Training, Hyperparameter Optimization, and Evaluation
Train and fine-tune AI models using large energy storage datasets, optimizing hyperparameters to achieve high performance and generalizability • Evaluate and compare the performance of different AI models using metrics like accuracy, precision, and recall, to select the best approach for energy storage problems • Implement and analyze techniques like cross-validation and walk-forward optimization to ensure robust and reliable AI model performance
Deployment, MLOps, and Production Workflows
Deploy trained AI models in production environments, integrating with existing energy storage systems and infrastructure • Develop and implement MLOps pipelines to automate model training, deployment, and monitoring, ensuring continuous improvement and reliability • Configure and manage model serving and inference workflows, optimizing for low latency, high throughput, and scalability
Ethics, Bias Mitigation, and Responsible AI Practices
Analyze and identify potential biases in energy storage datasets and AI models, developing strategies to mitigate and address these issues • Develop and implement techniques like data augmentation and adversarial training to enhance AI model robustness and fairness • Evaluate and ensure compliance with regulatory requirements and industry standards for responsible AI development and deployment
Industry Integration, Business Applications, and Case Studies
Develop and implement AI-powered energy storage solutions for real-world industry applications, such as grid management and electric vehicle charging • Analyze and evaluate the economic and environmental impact of AI-driven energy storage solutions, identifying opportunities for cost reduction and sustainability • Design and propose business cases for AI-powered energy storage solutions, including market analysis, competitive landscape, and revenue projections
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | Scikit-learn |
Frequently Asked Questions
Scholar Feedback & Reviews
Based on 0 scholar submissions
Rating Breakdown
No verified reviews published yet. Be the first to share your academic experience.
Leave Scholar Feedback
Your rating will help prospective scholars. Ratings below 3 stars are routed privately to the faculty mentor for immediate response.
