Deep learning workflow for the inverse design of molecules with specific optoelectronic properties
The inverse design of novel molecules with a desirable optoelectronic property requires
consideration of the vast chemical spaces associated with varying chemical composition …
consideration of the vast chemical spaces associated with varying chemical composition …
DDStore: Distributed Data Store for Scalable Training of Graph Neural Networks on Large Atomistic Modeling Datasets
Graph neural networks (GNNs) are a class of Deep Learning models used in designing
atomistic materials for effective screening of large chemical spaces. To ensure robust …
atomistic materials for effective screening of large chemical spaces. To ensure robust …
[HTML][HTML] Enhancing molecular design efficiency: Uniting language models and generative networks with genetic algorithms
This study examines the effectiveness of generative models in drug discovery, material
science, and polymer science, aiming to overcome constraints associated with traditional …
science, and polymer science, aiming to overcome constraints associated with traditional …