Molecular Geometry-aware Transformer for accurate 3D Atomic System modelingMolecular dynamic simulations are important in computational physics,
chemistry, material, and biology. Machine learning-based methods have shown
strong abilities in predicting molecular energy and properties and are much
faster than DFT calculations. Molecular energy is at least related to atoms,
bonds, bond angles, torsion angles, and nonbonding atom pairs. Previous
Transformer models only use atoms as inputs which lack explicit modeling of the
aforementioned factors. To alleviate this limitation, we propose Moleformer, a
novel Transformer architecture that takes nodes (atoms) and edges (bonds and
nonbonding atom pairs) as inputs and models the interactions among them using
rotational and translational invariant geometry-aware spatial encoding.
Proposed spatial encoding calculates relative position information including
distances and angles among nodes and edges. We benchmark Moleformer on OC20 and
QM9 datasets, and our model achieves state-of-the-art on the initial state to
relaxed energy prediction of OC20 and is very competitive in QM9 on predicting
quantum chemical properties compared to other Transformer and Graph Neural
Network (GNN) methods which proves the effectiveness of the proposed
geometry-aware spatial encoding in Moleformer.
arxiv.org