ONETEP is a leading linear-scaling DFT code for parallel computers for performing calculations with thousands of atoms with large basis set (plane waves) accuracy. Amongst its capabilities are total energies, geometry relaxations, molecular dynamics, excited states, solvent and electrolyte models and constant voltage simulations, metallic systems, band structure unfolding, a range of xc functionals including hybrid and non-local functionals, simulation cell relaxation, open, periodic and mixed boundary conditions etc. The applications of the code include battery materials and degradation processes, protein-ligand binding for drug design, 2D material heterostructures for low power electronics, supported nanoparticle catalysts for fuel cells, etc. More information about its capabilities and examples of applications can be found at https://onetep.org/
The ONETEP Developers Group are organising a 3-day Masterclass 22-24 August 2023 at STFC Harwell. The aim of the Masterclass is to train participants to apply the code on their own specific problems of interest. For this reason each participant is allocated 1 or 2 tutors who will help them set up and run calculations on their particular systems. Introductory lectures and lectures on some of the functionalities of the code will also be delivered.
ONETEP is free for academics and participants should obtain the code at least one month in advance of attending the Masterclass and have it compiled and tested on the HPC cluster they will use during the workshop – instructions will be provided and if needed further help for this can be provided by the tutors and via the ONETEP mailing list.
To enquire or apply for participation to the Masterclass please fill in this form:
Those who are applying to participate should also provide a short description of the application they want to do with the code so that suitable tutors can be allocated.
Participation in the Masterclass is free and accommodation and meals will be provided. We will also cover economy class travel expenses for UK-based participants.