The Battery Genome Project: AI for Energy Storage
From Quantum to AI to Diffusion—Design Better Battery Materials Faster
About This Course
This 3-day hands-on workshop equips learners with practical skills to run atomic-scale simulations and data-driven screening for battery materials discovery. Participants will compute electronic properties using DFT, build an ML pipeline to rank candidate materials at scale using Materials Project data, and simulate Li-ion diffusion with molecular dynamics to connect transport behavior with charging-rate performance. Every day includes a clear deliverable (DOS plot, top-5 shortlist, Arrhenius diffusion plot).
Aim
To train participants to predict, screen, and validate next-gen battery materials using an end-to-end workflow: DFT (Quantum ESPRESSO) → ML ranking (Materials Project + Python) → ion-transport insights (LAMMPS MD).
Workshop Objectives
- Understand where DFT, ML, and MD fit in battery materials discovery.
- Run and interpret DFT outputs (band/DOS) relevant to conductivity and stability proxies.
- Build a Materials Project → Python dataset pipeline for materials informatics.
- Engineer descriptors (pymatgen/matminer), train/evaluate ML models, and rank candidates.
- Simulate ion diffusion in LAMMPS and compute diffusion coefficients across conditions.
- Translate results into synthesis prioritization and performance-relevant insights.
Workshop Structure
📅 Day 1 — Atomic-Scale Modeling with DFT (Quantum ESPRESSO)
- Battery materials discovery workflow: where DFT fits and what it predicts
- DFT essentials for energy materials: electronic structure, band structure basics, DOS interpretation
- Practical setup: pseudopotentials, k-points, convergence, input file structure
- Hands-on: Configure and run a Quantum ESPRESSO calculation for a candidate anode crystal structure and extract electronic outputs
- Deliverable: A Density of States (DOS) plot with a short interpretation of conductivity relevance
📅 Day 2 — Machine Learning Screening at Scale (Materials Project + Python)
- Materials informatics pipeline: descriptors → model → ranking → validation strategy
- Data sourcing via Materials Project API and building a usable training dataset
- Property prediction: feature engineering with pymatgen/matminer, model selection, evaluation basics
- Hands-on: Mine a large materials dataset, build features, train a Random Forest model to predict a target proxy (e.g., voltage/capacity-related)
- Deliverable: Top 5 predicted candidate materials shortlist for synthesis prioritization
📅 Day 3 — Ion Transport & Charging-Speed Insights (LAMMPS Molecular Dynamics)
- Why ion diffusion governs rate performance: diffusion coefficient, transport physics basics
- MD setup fundamentals: force fields/potentials, temperature control, simulation workflow
- Turning trajectories into insight: diffusion coefficient estimation and temperature dependence
- Hands-on: Run a simple LAMMPS Li-ion diffusion simulation and compute diffusion coefficients across conditions
- Deliverable: An Arrhenius plot projecting diffusion-driven performance across temperatures
Who Should Enrol?
- UG/PG/PhD students, researchers, and professionals in Materials Science, Chemistry, Physics, Chemical Engineering, Energy, or related fields.
- Basic Python familiarity is helpful; no prior DFT/MD experience required (guided notebooks + templates provided).
- Interest in batteries, energy storage, computational materials, or materials informatics
Important Dates
Registration Ends
01/15/2026
IST 04:30 PM
Workshop Dates
01/15/2026 – 01/17/2026
IST 05:30 PM
Workshop Outcomes
- Set up and execute Quantum ESPRESSO calculations (pseudopotentials, k-points, convergence).
- Generate and interpret a DOS plot for conductivity relevance.
- Collect materials data via Materials Project API and create ML-ready datasets.
- Train a baseline model (e.g., Random Forest), evaluate it, and produce a ranked shortlist.
- Run a Li-ion diffusion MD workflow and compute diffusion coefficients + Arrhenius trends.
- Build a reusable workflow: DFT → ML screening → transport validation.
Meet Your Mentor(s)
Gurpreet Kaur
Mrs. Gurpreet Kaur holds an MCA degree from Punjab Technical University (2010) and has over 7 years of IT industry experience as a Senior Software Developer in various companies. Her expertise lies in front-end technologies, data structures, and algorithms (DSA).
Fee Structure
Student
₹2499 | $65
Ph.D. Scholar / Researcher
₹3499 | $75
Academician / Faculty
₹4499 | $85
Industry Professional
₹6499 | $105
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
Join Our Hall of Fame!
Take your research to the next level with NanoSchool.
Publication Opportunity
Get published in a prestigious open-access journal.
Centre of Excellence
Become part of an elite research community.
Networking & Learning
Connect with global researchers and mentors.
Global Recognition
Worth ₹20,000 / $1,000 in academic value.
View All Feedbacks →