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Found 4 papers, 2 papers with code
Accelerating Material Design with the Generative Toolkit for Scientific Discovery
With the growing availability of data within various scientific domains, generative models hold enormous potential to accelerate scientific discovery.
Improving Molecular Properties Prediction Through Latent Space Fusion
Pre-trained Language Models have emerged as promising tools for predicting molecular properties, yet their development is in its early stages, necessitating further research to enhance their efficacy and address challenges such as generalization and sample efficiency.
Molecular Inverse-Design Platform for Material Industries
The discovery of new materials has been the essential force which brings a discontinuous improvement to industrial products' performance.
MHG-GNN: Combination of Molecular Hypergraph Grammar with Graph Neural Network
Property prediction plays an important role in material discovery.
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