AI-Driven Solid-State Battery Discovery & Health Prediction
Accelerate Battery Innovation with AI-Driven Materials Discovery and Predictive Modeling
About This Course
This three-day hands-on workshop introduces AI-driven approaches for solid-state battery materials discovery and battery health prediction. Participants will work with research-grade materials databases and real battery cycling datasets to explore materials data mining, machine learning, physics-informed modeling, and battery degradation analysis.
Aim
To equip participants with practical AI and machine-learning workflows for solid-state battery materials discovery, electrolyte-property prediction, physics-informed battery modeling, and SoH/RUL prediction using real scientific datasets.
Workshop Objectives
- Understand solid-state battery technologies and solid electrolyte properties.
- Retrieve and curate battery-material data from Materials Project and OQMD.
- Generate materials descriptors using Pymatgen and Matminer.
- Develop machine-learning models for electrolyte-property prediction.
- Apply XGBoost, Random Forest and SHAP for prediction and explainability.
- Understand foundational Physics-Informed Neural Network (PINN) workflows.
- Analyze battery cycling data and degradation indicators.
- Develop SoH and RUL prediction workflows using time-series and LSTM modeling.
Workshop Structure
Day 1: Solid-State Battery Fundamentals & Materials Data Preparation
- Fundamentals of lithium-ion and solid-state batteries
- Solid electrolytes, ionic conductivity, stability, and key performance factors
- Introduction to AI and materials informatics in battery research
- Materials Project and OQMD for battery-material data
- Material structure processing using Pymatgen
- Feature and descriptor generation using Matminer
- Building an AI-ready solid-electrolyte dataset
- Basic visualization and screening of candidate materials
Hands-On: Create and explore an AI-ready solid-electrolyte materials dataset.
Day 2: Machine Learning & Explainable AI for Materials Discovery
- Data preprocessing and feature selection
- Ionic-conductivity and materials-property prediction
- Random Forest and XGBoost modeling
- Model validation using MAE, RMSE, R², and cross-validation
- Predicted vs actual performance analysis
- Explainable AI using SHAP
- Identification of important material descriptors
- AI-based ranking of promising solid-electrolyte candidates
- Introduction to physics-informed machine learning for battery research
Hands-On: Build, evaluate, and interpret an explainable AI model for solid-electrolyte screening.
Day 3: Battery Health, SOH & Remaining Useful Life Prediction
- Introduction to battery cycling and degradation datasets
- Capacity fade and battery-aging analysis
- Feature engineering from voltage, current, capacity, temperature, and cycle data
- State of Health (SOH) estimation and prediction
- Baseline ML models for battery-health prediction
- Time-series modeling using LSTM
- Battery degradation forecasting
- Remaining Useful Life (RUL) estimation
- Comparison of machine-learning and deep-learning models
- Research interpretation and identification of future experimental directions
Hands-On: Develop an AI workflow for SOH prediction, degradation forecasting, and RUL estimation.
Who Should Enrol?
- MSc/M.Tech students in materials science, chemistry, physics, chemical engineering, energy engineering, or related fields
- PhD scholars and postdoctoral researchers in battery, materials, energy-storage, and computational research
- Faculty members, academicians, and research scientists
- Computational materials and machine-learning researchers
- Battery, EV, energy-storage, and BMS professionals
- Industry R&D professionals working in battery technologies and advanced energy systems
Important Dates
Registration Ends
September 7, 2026
IST 04:30 PM
Workshop Dates
September 7, 2026 – September 9, 2026
IST 05:00 PM
Workshop Outcomes
- Extract and curate solid-state electrolyte data from research-grade materials databases.
- Generate materials descriptors and features using Pymatgen and Matminer.
- Develop and validate machine-learning models for electrolyte-property prediction.
- Interpret ML predictions using feature importance and SHAP-based explainable AI.
- Apply foundational PINN workflows to physics-informed battery modeling.
- Process and analyze battery cycling data to identify degradation and health indicators.
- Develop SoH and RUL prediction models using time-series and LSTM-based approaches.
- Build reproducible AI workflows in Google Colab for battery and materials research.
- Interpret model outputs in the context of battery materials, energy storage, and EV applications.
Recording fee
Participants in India
₹2,499
Participants outside India
$50
- All session recordings
- Study material
- Certificate after completion
- 1-year access
The live sessions of this workshop are over, so the live fees no longer apply. The recordings are being uploaded. They appear in your dashboard as soon as they are ready, and we will e-mail you then.
Frequently asked questions
Will I get the recording?
Yes. Paid participants get the session recordings and study material in their NanoSchool dashboard (My registrations → Recorded Lectures / Study Material).
What if I miss a live session?
Watch the recording in your dashboard and send your questions to the mentor through the support desk.
Do I get a certificate?
Yes, an e-certificate of participation after the workshop, with a QR code anyone can verify and an "Add to LinkedIn" button.
Which fee category applies to me?
Choose the category that matches your current role: Student, Ph.D. Scholar / Researcher, Academician / Faculty or Industry Professional.
How can I pay?
By card, UPI or PayPal on the payment page; international bank transfer is also available there.
What is the refund policy?
See our Returns, Refunds & Cancellation policy: Returns, Refunds & Cancellation policy