npj Computational Materials · 2023 · 35 citations · 36 references
Natural SciencesMaterials EngineeringMaterials ScienceComputational Materials ScienceGenerative DesignOrganic ChemistryChemical Space ExplorationGenerative ModelingMaterial ComputationMaterial ModelingMaterials InformaticsScientific DiscoveryComputational Material DesignData-driven Material DesignData-driven Materials DiscoveryData-driven Scientific DiscoveryDiscoveryGenerative Models For MaterialsMaterials Data-driven DesignScientific Hypotheses
Abstract With the growing availability of data within various scientific domains, generative models hold enormous potential to accelerate scientific discovery. They harness powerful representations learned from datasets to speed up the formulation of novel hypotheses with the potential to impact material discovery broadly. We present the Generative Toolkit for Scientific Discovery (GT4SD). This extensible open-source library enables scientists, developers, and researchers to train and use state-of-the-art generative models to accelerate scientific discovery focused on organic material design.
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PyTorch: An Imperative Style, High-Performance Deep Learning Library
Adam Paszke, Sam Gross, Francisco Massa et al. · arXiv (Cornell University) · 2019 · 16.2K citations · Full text