Simon Batzner



E-Mail: echo "ude.dravrah.g@renztab" | rev

Simon Batzner
Harvard University
29 Oxford St Pierce Hall, Room 302
Cambridge, MA 02138

Research Interests

My interests lie at the intersection of Deep Learning and Physics. More specifically, I am interested in improving and accelerating Molecular Simulation with the help of Deep Learning. A core focus of my work has been on the role of symmetry. In particular, my Ph.D. work introduced E(3)-equivariant Machine Learning Interatomic Potentials and was the first to demonstrate the large improvements they can yield in sample efficiency, accuracy, and out-of-distribution generalization. More recently, my interests have focused on scalability, generalization to out-of-distribution data, and theory of equivariant neural networks. Most notably, our team was able to scale a state-of-the-art equivariant ML potential to >100,000,000 atoms on as little as 128 GPUs while maintaining high throughput. Our approach has focused heavily on both designing novel algorithms for potentials while also implementing them in fast, massively scalable, and easy-to-use software that is widely used by researchers around the globe.



I am a fourth-year PhD student at Harvard University, fortunate to be advised by Boris Kozinsky.

Prior to joining Harvard, I obtained a Master's from MIT, where I worked with Alexie Kolpak and Boris Kozinsky. At MIT, I also wrote a thesis on equivariant neural networks. Before that, I spent a year in Los Angeles, working on the NASA mission SOFIA, where I wrote software for analyzing telescope data and used ML to model the dynamics of piezolelectrics. I obtained my Bachelor's from the University of Stuttgart, Germany. I am originally from a small, but beautiful town a few minutes from the Bavarian Alps.



Here is a link to my Google Scholar.


Here is a link to my Linkedin.

Invited talks


We have published two codes for the NequIP and Allegro potential. Both are public on our group's Github. If you have questions, please reach out, we are happy to help: Both of these also come with a LAMMPS pair_style, which can be found here:


Last updated: 02/03/2023