Published March 30, 2022 | Version v1
Dataset Open

E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials

  • 1. John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
  • 2. Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
  • 3. Robert Bosch Research and Technology Center, Cambridge, MA 02139, USA
  • 4. Computational Research Division and Center for Advanced Mathematics for Energy Research Applications, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
  • 5. Department of Electrical Engineering and Computer Science and Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA

* Contact person

Description

This work presents Neural Equivariant Interatomic Potentials (NequIP), an E(3)-equivariant neural network approach for learning interatomic potentials from ab-initio calculations for molecular dynamics simulations. While most contemporary symmetry-aware models use invariant convolutions and only act on scalars, NequIP employs E(3)-equivariant convolutions for interactions of geometric tensors, resulting in a more information-rich and faithful representation of atomic environments. The method achieves state-of-the-art accuracy on a challenging and diverse set of molecules and materials while exhibiting remarkable data efficiency. NequIP outperforms existing models with up to three orders of magnitude fewer training data, challenging the widely held belief that deep neural networks require massive training sets. The high data efficiency of the method allows for the construction of accurate potentials using high-order quantum chemical level of theory as reference and enables high-fidelity molecular dynamics simulations over long time scales.

Files

File preview

files_description.md

All files

Files (774.1 MiB)

Name Size
md5:b3a970f6e5874616810803b2d61fd167
269 Bytes Preview Download
md5:eb0224ff48b6cf3592ec96de0a1548b2
36.6 MiB Download
md5:d436cfb166276304c8acf9ffe8f36738
524.9 MiB Download
md5:16f91b4aca0b5759026f8bd62790e5c6
212.6 MiB Download
md5:aa22b3ba42b42ed12fc05dea78b6ba01
558 Bytes Preview Download

References

Preprint (Preprint where the data is discussed)
Batzner, S., Musaelian, A., Sun, L., Geiger, M., Mailoa, J. P., Kornbluth, M., ... & Kozinsky, B. (2021). E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. arXiv preprint arXiv:2101.03164.