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.

In this talk, I introduce Chemellia: a machine learning ecosystem (built on Flux.jl) designed for chemistry and materials science problems involving molecules, crystals, surfaces, etc. I will focus on two packages I have developed: first, ChemistryFeaturization, which allows customizable and invertible featurization of atomic systems. The second, AtomicGraphNets, implements graph neural network models tailored to atomic graphs, and substantially outperforms comparable Python packages.