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Medford Group Graduate Training
VIP
VIP Materials
Course Description
Big Data & Quantum Mechanics
DFT adsorption energy reproducibility project
Training Materials
1. Introduction to Basic Python Tools
1.2. Introduction to Python programming
1.3. Numpy - multidimensional data arrays
1.4. SciPy - Library of scientific algorithms for Python
1.5. matplotlib - Plotting in Python
1.6. Exercises
2. Introduction to Manipulating Atoms in Python
2.2. Intro to Building Structures with ASE
2.3. Intro to ASE Calculators
2.4. Exercises
2.5. Exercises
3. Introduction to Linux and High-Performance Computing
3.3. Exercises
4. Introduction to Density Functional Theory
5. Running Density Functional Theory on PACE
5.2. Running QE on PACE
5.3. Running a simple DFT calculation on PACE Supercomputer Cluster
5.4. Exercises
6. Applications of Density Functional Theory
6.2. Adsorption energy calculation using DFT
6.3. Adsorption energy from DFT
6.4. Exercises
7. Intro to Regression and High Dimensional Data
7.2. Non-Parametric Models
7.3. Complexity Optimization
7.4. High Dimensional Data
7.5. Dimensionality Reduction
7.6. Exercises
8. Intro to Classification and Generative Models
8.2. Classification Overview
8.3. Generalized Linear Models
8.4. Alternate classification methods
8.5. Clustering
8.6. Generative Models
8.7. Exercises
Appendix
Appendix
Basics of Searching and Reading Scientific Literature
Create DFT Environments
Gas formation energy calculation using DFT
Referencing Binding Energies
Index
