Building a Chemistry and Materials Science Ecosystem in Julia
2021-07-28, 12:30–14:00 (UTC), BoF/Mini Track

Julia has a growing presence in the computational chemistry and materials science communities, already exhibiting best-in-class performance in several domains. However, a common set of tools, datatypes, and norms are largely lacking at present. In this session, we will have discussions to build consensus around a vision for such tools, with an emphasis on reusable structures/workflows, such as I/O for common file types, bindings for widely-used codes from other languages, and mathematical tools.

Julia is a natural choice for computational chemists and materials scientists, primarily due to its excellent computational performance combined with ease of code sharing and extensibility within and between packages. Unsurprisingly, interest in and use of Julia within this community is growing – in particular, at JuliaCon 2020, several packages were introduced (such as JuliaChem and DFTK) that generated substantial “buzz” in the community, and despite being quite young (O(1) developer-year of invested effort), these packages are already matching or even exceeding best-in-class performance for some use cases!

This BoF session aims to continue this momentum, as well as to set some longer-term goals and norms for the community as a whole. To our knowledge, there has not yet been a broad discussion of this kind, and an informal proposal on Julia Discourse (see ) indicated enthusiasm from a variety of developers and users.

In particular, at present, standards such as how to represent certain ubiquitous types of data and perform common tasks is lacking, which can lead to inadvertent duplication of effort. As interest in and use of Julia in these communities grows, the impact of establishing such norms multiplies. In this session, we plan to host a community discussion aimed at building consensus around these topics. Some specific examples include, but are not necessarily limited to:

  1. I/O for common structure file types (e.g. .cif, .xyz) and Julia data types for representing these structures (examples of Python versions of such systems include those provided by the Atomic Simulation Environment and pymatgen)
  2. Frequently-invoked mathematical procedures such as integration on common types of grids or using common sets of basis functions utilized within quantum chemical simulation approaches such as density functional theory and (post-)Hartree-Fock
  3. Julia bindings for widely-used codes in other languages (primarily C, C++, and Python) that are not worth duplicating in Julia in the short term but which provide functionality such as parsing outputs of simulation codes as well as some of the mathematical operations described above.

We are optimistic that the discussions in this session will both help to strengthen ties within this small but growing community as well as help to amplify its productivity and impact!

By training a chemist, who got more and more twisted towards the "dark side" of numerical analysis. Now working as a PostDoc at the Applied and Computational Mathematics lab of RWTH Aachen University, Germany. Lead developer of DFTK, a Julia code for mathematical research in on density-functional theory (a quantum chemistry method).

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Usually writing code (or docs!) or triathlon-ing.

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