June 6, 2024, 4:43 a.m. | F\'elix Therrien, Edward H. Sargent, Oleksandr Voznyy

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.03278v1 Announce Type: new
Abstract: Graph neural networks (GNNs) have emerged as powerful tools to accurately predict materials and molecular properties in computational discovery pipelines. In this article, we exploit the invertible nature of these neural networks to directly generate molecular structures with desired electronic properties. Starting from a random graph or an existing molecule, we perform a gradient ascent while holding the GNN weights fixed in order to optimize its input, the molecular graph, towards the target property. Valence …

abstract article arxiv computational cs.lg discovery electronic exploit generate generators gnn gnns graph graph neural networks materials nature networks neural networks physics.chem-ph pipelines property random tools type

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