AI & Quantum Machine Learning for Solid-State Battery Material Discovery
Screen Smarter. Predict Faster. Enter the Quantum Era of Materials Research.
About This Course
This advanced 3-day workshop bridges materials science, deep learning, and quantum computing to transform how solid-state battery materials are discovered. Participants will move beyond trial-and-error experimentation into data-driven, high-throughput screening workflows.
Starting with large-scale crystallographic mining from the Materials Project, the workshop advances to Graph Neural Networks capable of predicting stability and ionic conductivity without expensive DFT simulations. The final day introduces Quantum Machine Learning, where hybrid quantum-classical models simulate molecular energy states—opening pathways toward next-generation computational chemistry.
The workshop is optimized for research environments, emphasizing publication-ready and grant-aligned methodologies.
Aim
To train researchers in AI-driven crystal screening, Graph Neural Network modeling, and foundational Quantum Machine Learning for accelerating solid-state electrolyte discovery.
Workshop Objectives
- Extract and process massive crystallographic datasets from the Materials Project database.
- Deploy Graph Neural Networks (GNNs) to predict the ionic conductivity and thermodynamic stability of novel solid electrolytes.
- Understand the mathematical and structural differences between classical ML and Quantum ML for chemical simulations.
- Run a foundational Quantum Machine Learning algorithm in the cloud to model molecular energy states.
Workshop Structure
📅 Day 1 — AI-Driven Crystal Structure Screening
- Focus: Data extraction and classical machine learning for material properties
- Transition from trial-and-error chemistry to inverse material design; understanding crystallographic features
- Hands-On :
- Connecting to the Materials Project API via Python
- Querying thousands of crystal structures for potential solid electrolytes
- Using the matminer library to featurize crystal data (converting lattice structures into machine-readable features)
📅 Day 2 — Graph Neural Networks (GNNs) for Electrolyte Prediction
- Focus: Deep learning applied directly to molecular and crystal graphs
- Why traditional ML fails for complex 3D structures; how GNNs learn atomic bonds and interactions to predict stability and ionic conductivity
- Hands-On:
- Loading a pre-trained Graph Neural Network model in Google Colab
- Feeding novel, unseen solid-state electrolyte compositions into the model
- Predicting thermodynamic stability (Formation Energy) without running computationally heavy DFT calculations
📅 Day 3 — Entering the Quantum Era: QML for Chemical Simulation
- Focus: The frontier of computational materials science
- Limits of classical computing for electron interactions; introduction to qubits, quantum gates, and the Variational Quantum Eigensolver (VQE)
- Hands-On:
- Introduction to PennyLane or Qiskit quantum computing frameworks
- Building a simple parameterized quantum circuit (quantum neural network layer)
- Running a hybrid quantum-classical simulation to estimate ground state energy of a battery-relevant molecule (e.g., Lithium Hydride – LiH)
Who Should Enrol?
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Professors, PhD scholars, postdocs, and researchers in materials science, chemistry, physics, and energy storage.
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Basic Python familiarity required.
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Foundational knowledge of crystallography or electrochemistry recommended.
Important Dates
Registration Ends
02/23/2026
IST 4 : 30 PM
Workshop Dates
02/23/2026 – 02/25/2026
IST 5 : 30 PM
Workshop Outcomes
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Extract and featurize large-scale crystallographic datasets.
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Apply GNNs for predicting electrolyte stability and conductivity proxies.
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Understand practical differences between classical ML and QML in chemistry.
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Run foundational hybrid quantum simulations in the cloud.
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Build an end-to-end workflow: Database → GNN Screening → Quantum Validation.
Fee Structure
Student
₹2499 | $75
Ph.D. Scholar / Researcher
₹3499 | $85
Academician / Faculty
₹4499 | $95
Industry Professional
₹6499 | $115
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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