Kshitij Mehta Computer Scientist Contact 865.574.1739 | MEHTAKV@ORNL.GOV All Publications Collection: TD-DFT and EOM-CCSD Calculations for the GDB-9-Ex Dataset Scalable training of trustworthy and energy-efficient predictive graph foundation models for atomistic materials modeling: a case study with HydraGNN MDLoader: A Hybrid Model-Driven Data Loader for Distributed Graph Neural Network Training Scaling Ensembles of Data-Intensive Quantum Chemical Calculations for Millions of Molecules MDLoader: A Hybrid Model-driven Data Loader for Distributed Deep Neural Networks Training... DDStore: Distributed Data Store for Scalable Training of Graph Neural Networks on Large Atomistic Modeling Datasets Deep learning workflow for the inverse design of molecules with specific optoelectronic properties... Two excited-state datasets for quantum chemical UV-vis spectra of organic molecules Computational Workflow for Accelerated Molecular Design Using Quantum Chemical Simulations and Deep Learning Models Running Ensemble Workflows at Extreme Scale: Lessons Learned and Path Forward Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules A codesign framework for online data analysis and reduction Official Report on the 2021 Computational and Autonomous Workflows Workshop (CAW 2021) A Community Roadmap for Scientific Workflows Research and Development Reusability First: Toward FAIR Workflows DYFLOW: A flexible framework for orchestrating scientific workflows on supercomputers The Exascale Framework for High Fidelity coupled Simulations (EFFIS): Enabling whole device modeling in fusion science Data Federation Challenges in Remote Near-Real-Time Fusion Experiment Data Processing Visualization as a Service for Scientific Data ADIOS 2: The Adaptable Input Output System. A framework for high-performance data management Extending the Publish/Subscribe Abstraction for High-Performance I/O and Data Management at Extreme Scale... Understanding Performance-Quality Trade-offs in Scientific Visualization Workflows with Lossy Compression A Codesign Framework for Online Data Analysis and Reduction Understanding Performance-Quality Trade-offs in Scientific Visualization Workflows with Lossy Compression... A Co-Design Study Of Fusion Whole Device Modeling Using Code Coupling Pagination Current page 1 Page 2 Next page ›› Last page Last » Key Links Google Scholar ORCID LinkedIn GitHub Organizations Computing and Computational Sciences Directorate Computer Science and Mathematics Division Data and AI Systems Section Workflow Systems Group
Research Highlight CCSD Researchers Create Two Open-source Datasets for Quantum Chemical Prediction of UV/Vis Absorption Spectra for Organic Molecules
Research Highlight Scalable Training of Graph Convolutional Neural Networks for Fast and Accurate Predictions of Molecular Photo-Optical Properties
