Massimiliano Lupo Pasini Data Scientist Contact LUPOPASINIM@ORNL.GOV All Publications Scalable training of trustworthy and energy-efficient predictive graph foundation models for atomistic materials modeling: a case study with HydraGNN Evaluating the Use of Foundational Chemical Language Models in Multimodal Graph Fusion MDLoader: A Hybrid Model-Driven Data Loader for Distributed Graph Neural Network Training A Perspective on Scalable AI on High-Performance Computing and Leadership Class Supercomputing Facilities [Industrial and Governmental Activities] First-principles data for solid solution niobium-tantalum-vanadium alloys with body-centered-cubic structures 91°µÍø's Strategic Research and Development Insights for Digital Twins A Deep Learning Approach for Detection and Localization of Leaf Anomalies Scaling Ensembles of Data-Intensive Quantum Chemical Calculations for Millions of Molecules AI for Materials Design and Discovery Using Atomistic Scale Information [Industrial and Governmental Activities] Anderson acceleration with approximate calculations: Applications to scientific computing MDLoader: A Hybrid Model-driven Data Loader for Distributed Deep Neural Networks Training... Transferring predictions of formation energy across lattices of increasing size* Invariant Features for Accurate Predictions of Quantum Chemical UV-vis Spectra of Organic Molecules 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... DDStore: Distributed Data Store for Scalable Training of Graph Neural Networks on Large Atomistic Modeling Datasets Two excited-state datasets for quantum chemical UV-vis spectra of organic molecules Graph neural networks predict energetic and mechanical properties for models of solid solution metal alloy phases Machine Learning for First Principles Calculations of Material Properties for Ferromagnetic Materials Hierarchical Model Reduction Driven by Machine Learning for Parametric Advection-Diffusion-Reaction Problems in the Presence of Noisy Data Computational Workflow for Accelerated Molecular Design Using Quantum Chemical Simulations and Deep Learning Models A Neural Network Approach to Predict Gibbs Free Energy of Ternary Solid Solutions Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules Study of solid molecular deuterium D2 growth under gas pressure Stable parallel training of Wasserstein conditional generative adversarial neural networks Pagination Current page 1 Page 2 Next page ›â¶Äº Last page Last » Key Links Curriculum Vitae Organizations Computing and Computational Sciences Directorate Computational Sciences and Engineering Division Advanced Computing Methods for Engineered Systems Section Computational Coupled Physics
