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Enjoy the Lectures!

I am planning to prepare a lecture series on
Computational Physics, Chemistry, and Biology.

Below you can look into the topics that will be covered in this series.

Links will be updated as soon as the videos are ready (weekly).

Module 1
The Fundamentals
  1. Mathematics: Fundamentals - I
  2. Mathematics: Fundamentals - II
  3. Statistics: Fundamentals - I
  4. Statistics: Fundamentals - II
  5. Proteins & Membrane structure
  6. Solid state physics
Functions
Calculus
Vectors
Linear algebra
Matrices
Commplex numbers
Vector calculus
Series expansion
Operator algebra
Bra-Ket notations
Types of statistics
Understanding data
Population vs. samples
Data distributions
Central tendencies
The Gaussian distribution
Measure of dispersion
Measure of shape
Statistical distances
Outliers
Principal Component Analysis
Probability theory
Hypothesis testing
ANOVA
Chi-square test
Bayesian statistics
Amino acids
peptide linkages
primary, secondary and tertiary structures
Interaction of proteins and ligands
lipids and membranes
Crystal systems and Lattices
Point groups and space groups
Unit cell
Super cell
wigner - Seitz cell
Reciprocal space
k-points and Brillouin zone
X-Ray diffraction: Bragg's law and Laue's law
Defects in crystals
Module 2
Classical Molecular Dynamics
  1. Introduction to Classical Mechanics
  2. Newtonian Mechanics
  3. Lagrangian Mechanics
  4. Hamiltonian Mechanics
  5. Equilibrium Statistical Mechanics
  6. Non-Equilibrium Statistical Mechanics
  7. Classical Molecular Dynamics
  8. Classical Force Fields
  9. Free Energy Calculations & sampling Methods
  10. Entropy corrections
  11. Minimum Energy Paths of Transition
  12. QM/MM methods
  13. Hands on CMD soft matter
  14. Hands on CMD hard matter
The classical regime
Mass and velocity
Formalisms of classical mechanics
Applicability
Limitations
Degrees of freedom
1D motion of a single particle
Phase space and phase portrait
Dynamics in phase space
Linear dynamical systems
Autonomous dynamical systems
Conservative vs. dissipated systems
Limitations of Newtonian formalism
Action principles
The Lagrangian
Newtonian to Lagrangian
Advantages and Limitations
Legendre transformations
Lagrangian to Hamiltonian
Hamilton's equation
Constants of motion
Poisson's brackets
Hamiltonian for a general dynamical system
Free mmotion of a particle on a line
Solving a Hamiltonian
Canonical transformations
Origin of dynamical chaos
Ergodicity
Noether's Theorem
Dynamical symmetry groups
Introduction
Probability Distributions
Ensembles
Thermodynamics & Statistical Mechanics Relation
Phase Transitions
The Langevin Model
Linear Response Theory
Applicability
Short & Long range interactions
Integrators
Thermodynamic Ensembles
Thermostats and Barostats
Boundary Conditions
Parallel MD
Structure of a Force Field
Intramolecular Terms
Intermolecular Terms
Special Terms
Popular Force Fields
Parametrizing a Force Field
Beyond empirical Force Field
Solvation/Transfer Free Energy
Binding Free Energy
Conformational Free Energy
Calculation of Free Energy
Brute Force Method
Thermodynamical Integration
Free Energy Perturbation
Umbrella Sampling
Potential of Mean Force
Steered Molecular Dynamics
Adaptive Biased Force
Replica Exchange/Parallel Tempering
Harmonic and Quasi-harmonic techniques
Step-by-step reconstruction
Normal Mode Analysis
Interaction Entropy
Chain-of-states/Plain Elastic Bands
Nudge Elastic Bands
Implementation of NEB
Hybrid QM/MM Methods
Subtractive QM/MM Methods
Additive QM/MM Methods
Capping bonds at QM/MM boundary
Module 3
ab initio Molecular Dynamics
  1. Introduction to Quantum Mechanics
  2. Copenhagen vs. Bohemian interpretations
  3. Schrödinger, Heisenberg, and Dirac formalisms
  4. Approximation Methods
  5. Wave function based methods (Hartree-Fock method and beyond)
  6. Density based methods (Density Functional Theory and beyond)
  7. ab initio Molecular Dynamics
  8. Hand on AIMD: Structural relaxation
  9. Hands on AIMD: Simulations of liquids and solids
  10. Hand on AIMD: Slab models and Interfaces
  11. Introduction to Electronic Structure Theory
  12. Hands on Electronic Structure Calculations
Module 4
Python Programming
  1. Introduction to Python programming language
  2. Data types and variables
  3. Conditionals
  4. Loops
  5. Hands on basic python programming
  6. Python Libraries
  7. Numpy, Pandas and Matplotlib
  8. Hands on python programming with libraries
Module 5
Machine Learning
  1. Introduction to Machine Learning
  2. Regression problems
  3. Classification Problems
  4. Tree based models
  5. Support Vector Machines
  6. Bagging and Boosting techniques
  7. Bayesian Statistics and Bayes’ Theorem: The Naïve Bayes Algorithm
  8. Hands on ML: Regression
  9. Hands on ML: Classification
  10. Machine Learning in Physics, Chemistry and Biology
  11. Machine Learning Force Fields
Module 6
Deep Learning
  1. Introduction to Deep Learning
  2. Artificial neurons
  3. Activation function
  4. Neural Networks
  5. ANN, RNN, CNN and GNN
  6. Image processing
  7. Generative Adversarial Networks (GANs)
  8. Variational Auto-Encoders (VAEs)
  9. Neural Networks in Physics, Chemistry and Biology

© 2026 Pritish Joshi

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