Atomistic Simulations for Energy Applications

ESL7390 — IIT Delhi

Author

Arihant Bhandari

Published

2026

Welcome

These are the lecture notes for ESL7390: Atomistic Simulations for Energy Applications, offered at the Department of Energy Science and Engineering, IIT Delhi.

Course Objectives

By the end of this course, students will be able to:

  1. Understand the theoretical foundations of classical and quantum atomistic simulation methods.
  2. Set up, run, and analyze molecular dynamics (MD) simulations.
  3. Apply density functional theory (DFT) to compute electronic structure and material properties.
  4. Train and evaluate machine learning interatomic potentials (MLIPs).
  5. Apply these methods to problems in energy materials — batteries, catalysts, and solar cells.

How to Use These Notes

Each chapter contains:

  • Theory — derivations and key results with numbered equations
  • Examples — worked problems with Python/ASE/GPAW code
  • Exercises — problems to attempt before the next lecture

Equations are numbered per-chapter (e.g., Eq. 2.3 is the third equation in Chapter 2). Figures, tables, and sections can all be cross-referenced — hover over any reference to see a preview.

Software

We will primarily use:

  • ASE — Atomic Simulation Environment
  • GPAW — DFT code built on ASE
  • LAMMPS — Classical MD
  • MACE — Machine learning potentials

Notation

Table 1: Commonly used symbols
Symbol Meaning
\(\mathbf{r}_i\) Position vector of atom \(i\)
\(\mathbf{p}_i\) Momentum of atom \(i\)
\(m_i\) Mass of atom \(i\)
\(U(\mathbf{r})\) Potential energy surface
\(\hat{H}\) Hamiltonian operator
\(N\) Number of atoms/particles
\(k_B\) Boltzmann constant
\(\hbar\) Reduced Planck constant

See Table 1 for a summary of notation used throughout the notes.