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Found 8 papers, 6 papers with code
A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems
In these graphs, the geometric attributes transform according to the inherent physical symmetries of 3D atomic systems, including rotations and translations in Euclidean space, as well as node permutations.
On the importance of catalyst-adsorbate 3D interactions for relaxed energy predictions
The use of machine learning for material property prediction and discovery has traditionally centered on graph neural networks that incorporate the geometric configuration of all atoms.
Crystal-GFN: sampling crystals with desirable properties and constraints
Accelerating material discovery holds the potential to greatly help mitigate the climate crisis.
FAENet: Frame Averaging Equivariant GNN for Materials Modeling
Applications of machine learning techniques for materials modeling typically involve functions known to be equivariant or invariant to specific symmetries.
PhAST: Physics-Aware, Scalable, and Task-specific GNNs for Accelerated Catalyst Design
Catalyst materials play a crucial role in the electrochemical reactions involved in numerous industrial processes key to this transition, such as renewable energy storage and electrofuel synthesis.
Higher-order Clustering and Pooling for Graph Neural Networks
Graph Neural Networks achieve state-of-the-art performance on a plethora of graph classification tasks, especially due to pooling operators, which aggregate learned node embeddings hierarchically into a final graph representation.
GraphSVX: Shapley Value Explanations for Graph Neural Networks
Graph Neural Networks (GNNs) achieve significant performance for various learning tasks on geometric data due to the incorporation of graph structure into the learning of node representations, which renders their comprehension challenging.
Breaking Writer's Block: Low-cost Fine-tuning of Natural Language Generation Models
It is standard procedure these days to solve Information Extraction task by fine-tuning large pre-trained language models.
