Table of Contents
For fun, I have been live-streaming some of our research talks from the past year. Two of these talks are shown below. This is an experiment of sorts, let me know if you like them!
1. Machine learned potentials and automatic differentiation in molecular simulation
Machine learned potentials are revolutionizing molecular simulations. In this talk, I will introduce what machine learned potentials are, how we think about them, and how we create them. Then, I will show an example of how we use them to model segregation in a metal alloy surface at many different bulk compositions. Finally, I will show how automatic differentiation, which is one of the foundations of machine learning, can be used more broadly in scientific programming with derivatives.
This was the second talk I did, but it is somewhat of an introduction to the next talk below.
2. Leveraging machine learning to accelerate simulations of dilute alloy catalysts with adsorbates
In this talk I talk about how we use machine learning to build cheap and accurate surrogate models of alloy catalyst surfaces in the dilute limit. I will show how we use this to simulate acrolein adsorption on dilute Ag-Pd alloys.
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