Traian Dumitrică

This work introduces PolyGraphPy, an open-source unified Python framework that automates Density Functional Tight Binding (DFTB+) quantum-mechanics calculations to build structured datasets for monomers, homopolymers, and alternating copolymers, employs Bayesian Graph Neural Networks with stochastic graph representations for property prediction such as static polarizability together with uncertainty quantification, and incorporates two complementary generative models, a SELFIES-based Generative Pre-trained Transformer and a BRICS graph-fragmentation Genetic Algorithm, for de novo design of targeted molecules, demonstrated on a dataset of acrylates as a highly customizable end-to-end pipeline.

This work introduces PolyGraphPy, an open-source unified Python framework that automates Density Functional Tight Binding (DFTB+) quantum-mechanics calculations to build structured datasets for monomers, homopolymers, and alternating copolymers, employs Bayesian Graph Neural Networks with stochastic graph representations for property prediction such as static polarizability together with uncertainty quantification, and incorporates two complementary generative models, a SELFIES-based Generative Pre-trained Transformer and a BRICS graph-fragmentation Genetic Algorithm, for de novo design of targeted molecules, demonstrated on a dataset of acrylates as a highly customizable end-to-end pipeline.