Learning non-local molecular interactions via equivariant local representations and charge equilibration

Nature·Sep 16

✦ NabkaNews BriefAuto-summarized from multiple outlets · verify with the source

researchers are exploring a method for learning non-local molecular interactions. this approach involves using equivariant local representations and charge equilibration. the details of this method are being examined for its potential applications.

Full coverage

A foundation machine learning potential with polarizable long-range interactions for materials modelling

Nature·Nov 25

Extending the range of graph neural networks with global encodings

Nature·Feb 18

A scalable and quantum-accurate foundation model for biomolecular force fields via linearly tensorized quadrangle attention

Nature·Mar 7

Pushing charge equilibration-based machine learning potentials to their limits

Nature·Sep 16

Learning local equivariant representations for large-scale atomistic dynamics

Nature·Feb 3

Incorporating long-range interactions via the multipole expansion into ground and excited-state molecular simulations

Nature·Mar 25

Efficient equivariant model for machine learning interatomic potentials

Nature·Feb 26

A Euclidean transformer for fast and stable machine learned force fields

Nature·Aug 6

View original report at Nature ↗

Fetching the latest headlines…