Training

Here at Nanolayers we can help train your team to use state-of-the-art materials modelling and high performance computing tools. Among others, our courses have been featured in the Spring School in Computational Chemistry as well as at Aalto University in Helsinki, Finland. Below are a selection of the courses we have to offer:

Introduction to Machine Learning Methods
A general introduction to a wide variety of ML methods and their application to problems in materials science. The course covers unsupervised methods such as K-means clustering and principal component analysis for dimensionality reduction and data simplification. Supervised learning methods include kernel regression and neural networks, along with gradient-based and genetic algorithm training schemes. The course focusses on hands-on Jupyter notebooks tutorials on model as well as real scientific data.

CUDA Programming
The first part of the course shows how to compile and pre-existing CUDA software and utilise GPU libraries in own applications. The second part explains GPU programming, its intricacies and common optimisation tricks.

Hands On Molecular Dynamics with LAMMPS
Introduction to MD simulations with the LAMMPS software package. After a brief presentation of the key theoretical concepts, the course shows how to setup simulations of molecular fluids, solids and mixed system, and how results are analysed.

Parallel Programming: MPI and OpenMP
Introduction to parallel programming on conventional CPU architecture. The course covers the basics of MPI and OpenMP standards, and how to efficiently combine them on modern computer clusters.