Pei Zhang Computational Scientist Contact zhangp1@ornl.gov | 865.341.0368 All Publications Enhancing molecular design efficiency: Uniting language models and generative networks with genetic algorithms Transferring a Molecular Foundation Model for Polymer Property Predictions DDStore: Distributed Data Store for Scalable Training of Graph Neural Networks on Large Atomistic Modeling Datasets User Manual - HydraGNN: Distributed PyTorch Implementation of Multi-Headed Graph Convolutional Neural Networks Deep learning workflow for the inverse design of molecules with specific optoelectronic properties A direct numerical simulation study of the dilution tolerance of propane combustion under spark-ignition engine conditions... Computational Workflow for Accelerated Molecular Design Using Quantum Chemical Simulations and Deep Learning Models... Autoencoder neural network for chemically reacting systems... Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules... Multi-task graph neural networks for simultaneous prediction of global and atomic properties in ferromagnetic systems... PI3NN: Out-of-distribution-aware Prediction Intervals from Three Neural Networks... An out-of-distribution-aware autoencoder model for reduced chemical kinetics... A prediction interval method for uncertainty quantification of regression models... A priori examination of reduced chemistry models derived from canonical stirred reactors using three-dimensional direct numer... Reduced Models for Chemical Kinetics derived from Parallel Ensemble Simulations of Stirred Reactors... Key Links Google Scholar ORCID LinkedIn GitHub Organizations Computing and Computational Sciences Directorate Computational Sciences and Engineering Division Advanced Computing Methods for Physical Sciences Section Multiscale Materials Group
