Graph neural networks for materials science and chemistry
Abstract Machine learning plays an increasingly important role in many areas of chemistry
and materials science, being used to predict materials properties, accelerate simulations …
and materials science, being used to predict materials properties, accelerate simulations …
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 …
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 …
Structure-based drug design with geometric deep learning
Abstract Structure-based drug design uses three-dimensional geometric information of
macromolecules, such as proteins or nucleic acids, to identify suitable ligands. Geometric …
macromolecules, such as proteins or nucleic acids, to identify suitable ligands. Geometric …
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 …
General framework for E (3)-equivariant neural network representation of density functional theory Hamiltonian
The combination of deep learning and ab initio calculation has shown great promise in
revolutionizing future scientific research, but how to design neural network models …
revolutionizing future scientific research, but how to design neural network models …
Equiformerv2: Improved equivariant transformer for scaling to higher-degree representations
Equivariant Transformers such as Equiformer have demonstrated the efficacy of applying
Transformers to the domain of 3D atomistic systems. However, they are still limited to small …
Transformers to the domain of 3D atomistic systems. However, they are still limited to small …
Deep-learning density functional theory Hamiltonian for efficient ab initio electronic-structure calculation
The marriage of density functional theory (DFT) and deep-learning methods has the
potential to revolutionize modern computational materials science. Here we develop a deep …
potential to revolutionize modern computational materials science. Here we develop a deep …
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) …
Efficient and equivariant graph networks for predicting quantum Hamiltonian
We consider the prediction of the Hamiltonian matrix, which finds use in quantum chemistry
and condensed matter physics. Efficiency and equivariance are two important, but conflicting …
and condensed matter physics. Efficiency and equivariance are two important, but conflicting …