Rachel Kurchin

The JuliaMolSim community is hosting a minisymposium! Come hear about AtomsBase, our project to create a unified interface for representing atomic geometries, as well as packages for simulation (both quantum mechanical and classical particles-based) and machine learning on atomistic systems. Do you have an idea for a package you think the community needs? Participate in our “quick pitch” session and find co-developers to help build it!

The JuliaMolSim community is open to anyone who uses/develops Julia code that is used for simulating/analyzing systems that are resolved at the level of atomic/molecular coordinates. You can learn more about the packages we maintain and join conversations on our Slack workspace by going to our website at [https://juliamolsim.github.io](https://juliamolsim.github.io/) .

Our BoF session from JuliaCon 2021, “Building a Chemistry and Materials Science Ecosystem in Julia,” helped jumpstart the Slack community. A major subsequent output from those ongoing conversations was the development of the AtomsBase interface, defining a common set of functions for specifying atomic geometries. We’re really excited about the prospect of this effort enabling great interoperability between different types of simulation and analysis as well as to share code for tasks like visualization and I/O. In fact, it already has begun to have this impact in a number of academic projects with international collaborators and funded by major agencies such as the US Department of Energy. A major part of the strength and impact of these efforts has been substantial investment of effort from the beginning by mathematicians, computer scientists, and domain scientists working together, a hallmark of the Julia community writ large and a major part of the reason we’re building this community in Julia.

This year, we’re hosting a minisymposium to keep the community going strong, make new connections, show off cool projects, and collect new ideas! Our planned agenda (so far!) is as follows:

1. Introduction to JuliaMolSim in general and AtomsBase in particular with brief showcase of packages adopting the interface so far
2. Some “deeper-dive” talks on packages now using AtomsBase, focusing on updates since last JuliaCon and also elucidating other emerging themes such as support for automatic differentiation (AD) and GPU utilization
    1. Chemellia machine learning ecosystem (Rachel Kurchin)
    2. Molly.jl particle simulation package (Joe Greener)
    3. DFTK.jl density functional theory package (Michael Herbst)
    4. CESMIX project (Emmanuel Lujan)
3. Other contributed talks from the JuliaMolSim community, including:
    1. Fermi.jl (Gustavo Aroeira)
    2. ACE.jl (Christoph Ortner)
    3. NQCDynamics.jl (James Gardner)
4. “Quick pitch” session: what’s the next community project a la AtomsBase? Pitch your idea and find collaborators! (If you are interested in pitching, contact the minisymposium organizers and we will be in touch with more details). Some example topics could include:
    1. Plotting recipes (e.g. in Makie) for AtomsBase systems
    2. An ab initio MD engine based in Molly, utilizing DFTK for energy/force calculations via AtomsBase

Green

JuliaMolSim: Computation with Atoms

I will introduce ElectrochemicalKinetics.jl, a package that implements a variety of models for electrochemical reaction rates (such as Butler-Volmer or Marcus-Hush-Chidsey). It can also fit model parameters and construct nonequilibrium phase diagrams. While the package has already been of great use in electrochemical research applications, I will focus more on the design choices as well as the challenges that have come up in implementing automatic differentiation support.

In electrochemical reaction modeling, there are a variety of mathematical models (such as Butler-Volmer, Marcus, or Marcus-Hush-Chidsey kinetics) used to describe the relationship between the overpotential and the reaction rate (or electric current). Another important entity is the inverse of this function, i.e. given a current, what overpotential would be needed to drive it? Most of the models used do not have analytical inverses, so inverting them requires an optimization problem to be solved.

In ElectrochemicalKinetics.jl, I created a generic interface for computing these reaction rates and overpotentials, as well as using these quantities for other analyses such as fitting model parameters or constructing nonequilibrium phase diagrams, important for predicting, for example, behavior of a battery under fast charge or discharge conditions. Given a `KineticModel` object `m` we can always compute the rate constant at a given overpotential with the same syntax, no matter if `m isa ButlerVolmer` or `m isa Marcus` or any other implemented model type. This allows for easy comparison between these models, including when analyzing real data.

We can also construct nonequilibrium phase diagrams, to, for example, understand and predict lithium intercalation behavior in a battery at various charge or discharge rates. Building these phase diagrams requires calling the inverse function mentioned above and using it within another optimization (to satisfy the thermodynamic common-tangent condition), making automatic differentiation challenging. I will also discuss some of these challenges and the solutions I have found for them so far.

Rachel Kurchin

Talks:

JuliaMolSim: Computation with Atoms

Reaction rates and phase diagrams in ElectrochemicalKinetics.jl

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