🚨Deep learning for large-scale biomolecular dynamics is here!🚨

Today, our group is releasing new work showing how the SOTA accuracy of Allegro can be scaled to massive biomolecular systems up to the full HIV capsid at 44 million atoms!

arxiv.org/pdf/2304.10061… #compchem

1/🧵 We scale a large, pretrained Allegro model on various systems, from DHFR at 23k atoms, to Factor IX at 91k, Cellulose at 400k, the all-atom fully solvated HIV capsid at 44 million all the way up to >100 million atoms. 2/

A conversation with ChatGPT about DFT, quantum chemistry, and machine learning.

🧵 1/ 2/

🚨 What comes after Neural Message Passing? 🚨

Introducing Allegro - a new approach:

- no message passing or attention
- new SOTA on QM9+MD17
- scales to >100 million atoms
- 1 layer beats all MPNNs+Transformers
- blazing fast
- theory

arxiv.org/abs/2204.05249

How?👇#compchem First and foremost: this was joint with my co-first author and good friend Albert Musaelian with equal first-author contribution as well as with lab members Anders Johansson, Lixin Sun, Cameron Owen + Mordechai Kornbluth and of course @BKoz / Boris Kozinsky

🚀🚨 Equivariance changes the Scaling Laws of Interatomic Potentials 🚨🚀

We have updated the NequIP paper with up to 3x lower errors + (still) SOTA on MD17

Plus: equivariance changes the power-law exponent of the learning curve!

arxiv.org/abs/2101.03164…

👇🧵 #compchem #GNN Learning curves of error vs training set size typically follow a power law: error = a * N^b, where N is the number of training samples and the exponent b determines how fast a method learns as new data become available.

We're excited to introduce NequIP, an equivariant Machine Learning Interatomic Potential that not only obtains SOTA on MD-17, but also outperforms existing potentials with up to 1000x fewer data! w/ @tesssmidt @Materials_Intel @bkoz37 #compchem👇🧵 1/N

arxiv.org/pdf/2101.03164… NequIP (short for Neural Equivariant Interatomic Potentials) extends Graph Neural Network Interatomic Potentials that use invariant convolutions over scalar feature vectors to instead utilize rotation-equivariant convolutions over tensor features (i.e. scalars, vectors, ...). 2/N