Lecture 33 of 50Week 8 Statistical Mechanics
Introduction to Statistical Mechanics
Prof. Sai Gautam Gopalakrishnan · IISc Bangalore
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- Materials Informatics, IISc Bangalore (at NPTEL)
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- CC BY-NC-SA, as NPTEL states it. Non-commercial use only.
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- NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
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All the lectures
- Evolution of Materials modelling
- Introduction to Machine learning
- Anaconda installation and python basics
- Functions and classes
- Numpy basics
- Pandas basics
- Regression
- Regression (continued)
- Classification
- Classification (continued)
- Regression and classification example on materials data
- Regression models
- Regression models (continued)
- Classification models
- Support vector machines and decision trees
- Ensemble models
- Ensemble models (continued) and dimentionality reduction
- Comparison of classical models on materials data
- Neural networks
- Neural networks with keras and tensorflow
- Neural networks with pytorch
- Neural networks example on materials data
- Convolutions
- Graph Networks
- Crystal Graph Convolutional Neural Networks
- First Principles methods
- Density Functional Theory
- Exchange Correlation Functionals
- Basis sets and Pesudopotentials
- DFT hands on
- Introduction to Molecular Dynamics
- Molecular Dynamics (Continued)
- Introduction to Statistical Mechanics
- Statistical Mechanics - I
- Statistical Mechanics - II
- Introduction to lattice models
- Lattice models and Monte Carlo simulations
- Cluster expasnion hands on
- Monte Carlo simulation hands on
- Introduction to machine learned potentials
- Machine learned interatomic potentials (continued)
- Atomic cluster expasnion
- Machine learned interatomic potentials hands on
- Transfer learning -I
- Transfer learning -II
- Transfer Learning hands on
- Diffusion Models
- Diffusion models hands on
- Large Language Models
- Large Language Models hands on