Registration deadline is 15th of March 2023 by following the form.

https://cvent.me/zzYXnV

Atomistic simulations are widespread in modern chemistry and materials. They can give new insight into experimental data, predict new materials, and screen for desirable properties. These methods encompass a range of modeling approaches from emerging machine-learning potentials to reference-quality post-Hartree-Fock calculations. They are implemented across an intimidating assortment of software packages making trade-offs in user-friendliness, interoperability, and ease of automation. It can be difficult to trace the origin of a calculation or ensure that the results used in a study are consistent, and it can be costly to move from one research group to another with an incompatible in-house toolchain. Without shared collaborative tools, high-level algorithms may be implemented in a particular package, leaving the rest of the community to create in-house variants for their own work.

The Atomic Simulation Environment (ASE) is a community-driven Python package that solves the "n^2 problem" of code interfaces by providing some standard data structures and interfaces to ~100 file formats, acting as useful "glue" for work with multiple packages.[1] ASE integrates with more than 30 atomistic codes, covering methods from classical MD, machine learning interatomic potential to ab-initio codes. In addition, it can manipulate structures and run calculations, providing a range of generic dynamics and geometry-optimisation routines with a toolkit for the development of such methods. New schemes such as preconditioned optimisers are written once and immediately available to users of established atomistic codes. ASE is used for input preparation such as generation of slabs and nanoparticles, and output analysis such as thermochemistry, phase-diagram generation and energy level plots. Developers of new calculation packages have been able to focus on novel aspects (e.g. implementation of machine-learning potentials) and make use of existing tools for structure manipulation and dynamics. Workflow management systems can be built on this framework, creating "recipes" with ASE code.[2]

In this workshop, chemistry and physics research will be presented that applies and develops atomistic methods with an emphasis on automation, interoperability and reproducibility.  We hope to highlight and teach good practices around presentable, sharable, automatable calculations and analysis, using available and emerging Python-based tools. Software can be used to create very complex analysis pipelines, but if handled properly can also provide exact documentation of those methods. The event will consist of a science program with invited and contributed presentations and posters, followed by parallel tutorial and "code sprint" sessions.

The tutorials are intended for students and early-career researchers to develop confidence performing reproducible calculations using the Atomic Simulation Environment and related packages. The tutorial programme will include basic ASE tutorials by the workshop organisers, external package tutorials by workshop attendees and a session on Open Science practices. A basic familiarity with Python programming is required; some experience with atomistic simulations is also assumed.

The code sprint is intended for more experienced ASE users and developers to get hands-on experience navigating, modifying and extending the codebase. New contributors will be able to develop ideas in-person and create their first merge requests, while established developers will have an opportunity to advance new features and experiment with ways of improving the maintainability and re-usability of the codebase.

Applications to participate can be made through https://cvent.me/zzYXnV

Please note a small fee will be payable for successfull candidates. £100 if accommodation required £50 pounds otherwise.