Learning Local Equivariant Representations for Large-Scale Atomistic Dynamics | Albert Musaelian

[DISCLAIMER] - For the full visual experience, we recommend you tune in through our ⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube channel ⁠⁠⁠⁠⁠⁠⁠⁠⁠to see the presented slides.

Try datamol.io - the open source toolkit that simplifies molecular processing and featurization workflows for machine learning scientists working in drug discovery: ⁠⁠⁠⁠⁠⁠https://datamol.io/⁠⁠⁠⁠⁠⁠

If you enjoyed this talk, consider joining the ⁠⁠⁠⁠⁠⁠⁠⁠⁠Molecular Modeling and Drug Discovery (M2D2) talks⁠⁠⁠⁠⁠⁠⁠⁠⁠ live.

Also, consider joining the ⁠⁠⁠⁠⁠⁠⁠⁠⁠M2D2 Slack⁠⁠⁠⁠⁠⁠⁠⁠⁠.

Abstract: Trade-offs between accuracy and speed have long limited the applications of machine learning interatomic potentials. Recently, E(3)-equivariant architectures have demonstrated leading accuracy, data efficiency, transferability, and simulation stability, but their computational cost and scaling has generally reinforced this trade-off. In particular, the ubiquitous use of message passing architectures has precluded the extension of accessible length- and time-scales with efficient multi-GPU calculations. In this talk I will discuss Allegro, a strictly local equivariant deep learning interatomic potential designed for parallel scalability and increased computational efficiency that simultaneously exhibits excellent accuracy. After presenting the architecture, I will discuss applications and benchmarks on various materials and chemical systems, including recent demonstrations of scaling to large all-atom biomolecular systems such as solvated proteins and a 44 million atom model of the HIV capsid. Finally, I will summarize the software ecosystem and tooling around Allegro.

Speaker: Albert Musaelian

Twitter -  ⁠⁠⁠⁠⁠⁠⁠⁠⁠Prudencio⁠⁠⁠⁠⁠⁠⁠⁠⁠

Twitter - ⁠⁠⁠⁠⁠⁠⁠⁠⁠ Jonny⁠⁠⁠⁠⁠⁠⁠⁠⁠

Twitter - ⁠⁠⁠⁠⁠⁠⁠⁠⁠datamol.io