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

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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 - https://www.krellinst.org/csgf/fellow...

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Chapters:

00:00 - Intro
06:01 - Machine Learning Potentials
11:24 - No Constraints, Invariance, Equivariance
15:55 - NequIP Generalizes Across Geometry
20:04 - Message Passing Networks
21:09 - Allegro: Strictly Local Deep Equivariant Model
35:16 - Importance of Locality
39:54 - Demonstrating Allegro Scaling Up
49:56 - Weak Scaling
54:56 - Q+A