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Found 4 papers, 0 papers with code
Inverse Lithography Physics-informed Deep Neural Level Set for Mask Optimization
By gearing up DL with knowledge of inverse lithography physics, ILDLS provides a new and efficient mask optimization solution.
DR-Label: Improving GNN Models for Catalysis Systems by Label Deconstruction and Reconstruction
Reversely, the model Reconstructs a more robust equilibrium state prediction by transforming edge-level predictions to node-level with a sphere-fitting algorithm.
Initial Structure to Relaxed Energy (IS2RE), Direct
Property Prediction
Multi-Task Mixture Density Graph Neural Networks for Predicting Cu-based Single-Atom Alloy Catalysts for CO2 Reduction Reaction
Graph neural networks (GNNs) have drawn more and more attention from material scientists and demonstrated a high capacity to establish connections between the structure and properties.
Heterogeneous relational message passing networks for molecular dynamics simulations
With many frameworks based on message passing neural networks proposed to predict molecular and bulk properties, machine learning methods have tremendously shifted the paradigms of computational sciences underpinning physics, material science, chemistry, and biology.
