New Year Offer End Date: 30th April 2024
Program Virtual Workshop
The Battery Genome Project: AI for Energy Storage
From Quantum to AI to Diffusion—Design Better Battery Materials Faster
Skills you will gain:
About Workshop:
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.
What you will learn?
📅 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
Mentor Profile
Fee Plan
StudentINR 2499/- OR USD 65
Ph.D. Scholar / ResearcherINR 3499/- OR USD 75
Academician / FacultyINR 4499/- OR USD 85
Industry ProfessionalINR 6499/- OR USD 105
Important Dates
Registration Ends
15 Jan 2026 AT IST : 4:30 PM
15 Jan 2026 AT IST : 4:30 PM
Workshop Dates 15 Jan 2026 to 17 Jan 2026 AT IST : 5:30 PM
Get an e-Certificate of Participation!
Intended For :
- 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
Career Supporting Skills
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.