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).

• 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.

• 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

• 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.