Robert Smith Scientific Software Engineer Contact SMITHRW@ORNL.GOV All Publications Interpretable machine learning models classify minerals via spectroscopy... Raman spectroscopic investigation of selected natural uranyl sulfate minerals CURIES: Compendium of uranium Raman and infrared experimental spectra Leveraging Single-Page Applications for Seamless Scientific Workflows: DevSecOps Considerations Enabling Interconnected Science Workflows through an Adapter Approach Best practices for documenting a scientific Python project Calvera: A Platform for the Interpretation and Analysis of Neutron Scattering Data Towards a Software Development Framework for Interconnected Science Ecosystems Uncovering the hydride ion diffusion pathway in barium hydride via neutron spectroscopy Methodology for interpretable reinforcement learning model for HVAC energy control... Double Deep Q-Networks for Optimizing Electricity Cost of a Water Heater Chemical structure and curing dynamics of bisphenol S, PEEKTM‐like, and resveratrol phthalonitrile thermoset resins Evaluating the Adaptability of Reinforcement Learning Based HVAC Control for Residential Houses REAL-TIME AUTOMATED HEALTH INFORMATION TECHNOLOGY HAZARD DETECTION Simple analytical model for fitting QENS data from liquids The eclipse integrated computational environment Key Links Organizations Computing and Computational Sciences Directorate Computer Science and Mathematics Division Advanced Computing Systems Research Section Software Engineering Group
Research Highlight Discovery of Spectrographic Features in Uranium Compounds through Machine Learning
