Geometric deep learning on molecular representations
Geometric deep learning (GDL) is based on neural network architectures that incorporate
and process symmetry information. GDL bears promise for molecular modelling applications …
and process symmetry information. GDL bears promise for molecular modelling applications …
Machine learning on neutron and x-ray scattering and spectroscopies
Neutron and x-ray scattering represent two classes of state-of-the-art materials
characterization techniques that measure materials structural and dynamical properties with …
characterization techniques that measure materials structural and dynamical properties with …
Equiformer: Equivariant graph attention transformer for 3d atomistic graphs
YL Liao, T Smidt - arxiv preprint arxiv:2206.11990, 2022 - arxiv.org
Despite their widespread success in various domains, Transformer networks have yet to
perform well across datasets in the domain of 3D atomistic graphs such as molecules even …
perform well across datasets in the domain of 3D atomistic graphs such as molecules even …
E (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
Abstract This work presents Neural Equivariant Interatomic Potentials (NequIP), an E (3)-
equivariant neural network approach for learning interatomic potentials from ab-initio …
equivariant neural network approach for learning interatomic potentials from ab-initio …
e3nn: Euclidean neural networks
We present e3nn, a generalized framework for creating E (3) equivariant trainable functions,
also known as Euclidean neural networks. e3nn naturally operates on geometry and …
also known as Euclidean neural networks. e3nn naturally operates on geometry and …
Artificial intelligence for science in quantum, atomistic, and continuum systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
Equivariance with learned canonicalization functions
Symmetry-based neural networks often constrain the architecture in order to achieve
invariance or equivariance to a group of transformations. In this paper, we propose an …
invariance or equivariance to a group of transformations. In this paper, we propose an …
Geometric clifford algebra networks
Abstract We propose Geometric Clifford Algebra Networks (GCANs) for modeling dynamical
systems. GCANs are based on symmetry group transformations using geometric (Clifford) …
systems. GCANs are based on symmetry group transformations using geometric (Clifford) …
Self-consistent determination of long-range electrostatics in neural network potentials
Abstract Machine learning has the potential to revolutionize the field of molecular simulation
through the development of efficient and accurate models of interatomic interactions. Neural …
through the development of efficient and accurate models of interatomic interactions. Neural …
DeepRank: a deep learning framework for data mining 3D protein-protein interfaces
Abstract Three-dimensional (3D) structures of protein complexes provide fundamental
information to decipher biological processes at the molecular scale. The vast amount of …
information to decipher biological processes at the molecular scale. The vast amount of …