Junqi Yin Computational Scientist Contact YINJ@ORNL.GOV All Publications Language Models for the Prediction of SARS-CoV-2 Inhibitors Stable parallel training of Wasserstein conditional generative adversarial neural networks Learning to Scale the Summit: AI for Science on a Leadership Supercomputer 2021 Operational Assessment - OLCF Distributed Training and Optimization of Neural Networks Anderson Acceleration for Distributed Training of Deep Learning Models... Stable Parallel Training of Wasserstein Conditional Generative Adversarial Neural Networks : *Full/Regular Research Paper submission for the symposium CSCI-ISAI: Artificial Intelligence Magnetic properties of CrFeCoNi based high entropy alloys Mitigating Catastrophic Forgetting in Deep Learning in a Streaming Setting Using Historical Summary... Inter-Subunit Dynamics Controls Tunnel Formation During the Oxygenation Process in Hemocyanin Hexamers The Role of Hydrophobic Nodes in the Dynamics of Class A β-Lactamases Geometrical-Based Generative Adversarial Network to Enhance Digital Rock Image Quality Dynamic Profiling of β-Coronavirus 3CL Mpro Protease Ligand-Binding Sites A scalable algorithm for the optimization of neural network architectures Scalable balanced training of conditional generative adversarial neural networks on image data Fast and stable deep-learning predictions of material properties for solid solution alloys Monte Carlo simulation of order-disorder transition in refractory high entropy alloys: A data-driven approach Performance Evaluation of Python Based Data Analytics Frameworks in Summit: Early Experiences Deep learning of interface structures from simulated 4D STEM data: cation intermixing vs. roughening Data optimization for large batch distributed training of deep neural networks Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19 Toward Real-Time Analysis of Synchrotron Micro-Tomography Data: Accelerating Experimental Workflows with AI and HPC Smoky Mountain Data Challenge 2020: An Open Call to Solve Data Problems in the Areas of Neutron Science, Material Science, Urban Modeling and Dynamics, Geophysics, and Biomedical Informatics Machine-learning informed prediction of high-entropy solid solution formation: Beyond the Hume-Rothery rules Robust data-driven approach for predicting the configurational energy of high entropy alloys Pagination First page « First Previous page ‹‹ Page 1 Current page 2 Page 3 Next page ›› Last page Last » Key Links ORCID Organizations Computing and Computational Sciences Directorate National Center for Computational Sciences Advanced Technologies Section Analytics and AI Methods at Scale Group
