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Found 15 papers, 10 papers with code
Generalizing Denoising to Non-Equilibrium Structures Improves Equivariant Force Fields
We study the effectiveness of training equivariant networks with DeNS on OC20, OC22 and MD17 datasets and demonstrate that DeNS can achieve new state-of-the-art results on OC20 and OC22 and significantly improve training efficiency on MD17.
Symphony: Symmetry-Equivariant Point-Centered Spherical Harmonics for Molecule Generation
We present Symphony, an $E(3)$-equivariant autoregressive generative model for 3D molecular geometries that iteratively builds a molecule from molecular fragments.
Ophiuchus: Scalable Modeling of Protein Structures through Hierarchical Coarse-graining SO(3)-Equivariant Autoencoders
Three-dimensional native states of natural proteins display recurring and hierarchical patterns.
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.
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.
Learning Integrable Dynamics with Action-Angle Networks
Here, we propose an alternative construction for learned physical simulators that are inspired by the concept of action-angle coordinates from classical mechanics for describing integrable systems.
e3nn: Euclidean Neural Networks
We present e3nn, a generalized framework for creating E(3) equivariant trainable functions, also known as Euclidean neural networks.
Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs
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 when 3D-related inductive biases like translational invariance and rotational equivariance are considered.
Graph Attention
Initial Structure to Relaxed Energy (IS2RE), Direct
Sign and Basis Invariant Networks for Spectral Graph Representation Learning
We introduce SignNet and BasisNet -- new neural architectures that are invariant to two key symmetries displayed by eigenvectors: (i) sign flips, since if $v$ is an eigenvector then so is $-v$; and (ii) more general basis symmetries, which occur in higher dimensional eigenspaces with infinitely many choices of basis eigenvectors.
Ranked #10 on
Graph Regression
on ZINC-500k
Generative Coarse-Graining of Molecular Conformations
Coarse-graining (CG) of molecular simulations simplifies the particle representation by grouping selected atoms into pseudo-beads and drastically accelerates simulation.
Deep Learning and Spectral Embedding for Graph Partitioning
The embedding phase is trained first by minimizing a loss function inspired by spectral graph theory.
SE(3)-equivariant prediction of molecular wavefunctions and electronic densities
Machine learning has enabled the prediction of quantum chemical properties with high accuracy and efficiency, allowing to bypass computationally costly ab initio calculations.
Graph Partitioning and Sparse Matrix Ordering using Reinforcement Learning and Graph Neural Networks
The partitioning quality is compared with partitions obtained using METIS and SCOTCH, and the nested dissection ordering is evaluated in the sparse solver SuperLU.
Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds
We introduce tensor field neural networks, which are locally equivariant to 3D rotations, translations, and permutations of points at every layer.
