Lecture notes for R programming course
Click here to download notes. Click here and here to download datasets.
Lecture notes for density functional theory and band structure course
The internet is full of nob lecture notes on DFT and solid-state physics. Take a look at these instead: DFT1, band theory, DFT2.
Lecture notes from the course Machine Learning for Materials Science
Full set of notes from machine learning course taught to graduate-level computational materials science students. Datasets are available upon request.
(1. General Introduction) (2. Introduction to R) (3. Features and Dimensionality Reduction) (4. Features and Data Visualization Using R) (5. Classification and R Exercises) (6. Kernelized SVM and R Exercises) (7. Regression) (8. Kernel Ridge Regression with R) (9. Gaussian Process Regression) (10. Gaussian Process Regression II. Global Optimization) (11. Feed-Forward Neural Networks) (12. Feed-Forward Neural Networks with R) (13. Advanced Neural Network Models) (14. Summary)
Lecture notes on Markov chain Monte Carlo, support vector machines, and kernel ridge regression
Lecture notes from a hodgepodge sampling/machine learning course that I taught at Kyushu University many years ago. Still useful perhaps, although the notes above are a vastly better for studying machine learning. See MCMC, SVM, KRR.
Grant-writing tips for young scientists in Japan
Foreigners almost never ge...(snip!), but these tips might help if you wish to try.
Bayesian optimization for the Br2 interatomic potential
This code accompanies Chapter 2 of the text Bayesian Optimisation for Materials Science (Springer, 2017). This code will calculate the expected improvement based upon a sample of Br-Br interatomic distances and the corresponding potential energies. Click here to download code
This code was written in the R programming language. Simply copy and paste the code into the R interpreter. The R interpreter can be downloaded freely online (click here). The code is intended for the Windows version of R, and some minor modifications may be needed in order to run it in a Linux or Mac environment.