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Simon Batzner

@simonbatzner

Apr 12

🚨 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

@BKoz

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

Message Passing Neural Networks have taken molecular ML by storm and over the past few years, a lot of progress in Machine Learning for molecules and materials has been variations on this theme.

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Simon Batzner

@simonbatzner

Dec 17, 2021

🚀🚨 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.

Interestingly, it has been found that different models on the same data set usually only shift the learning curve, but do not change the power-law exponent, see e.g. [1]

[1] arxiv.org/abs/1712.00409

Read 14 tweets

Simon Batzner

@simonbatzner

Jan 11, 2021

@tesssmidt

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

We benchmark NequIP on a wide variety of molecules+materials: we start with atomic forces from MD-17 with 1,000 training configurations and find that we not only outperform other deep neural networks, but also perform better or sometimes on par with kernel-based methods. 3/N

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