Showing 14 results for Researcher: Massimiliano Lupo Pasini
July 22, 2025
5 MIN READ
The Department of Energy’s Oak Ridge National Laboratory had a major presence at the second annual AI+ Expo for National Competitiveness in Washington, D.C. The event, hosted by the Special Competitive Studies Project, showcased prominent players in technology, academia and government as they navigate the rapid growth of artificial intelligence.
June 10, 2025
5 MIN READ
Massimiliano “Max” Lupo Pasini, a data scientist at the Department of Energy’s Oak Ridge National Laboratory, was named a senior member of the Institute of Electrical and Electronics Engineers, the world’s largest organization for technical professionals.
January 28, 2025
3 MIN READ
Massimiliano (Max) Lupo Pasini, an R&D data scientist from ORNL, was awarded the National Energy Research Scientific Computing Center’s High Performance Computing Achievement Award for High Impact Scientific Achievement for his work in “Groundbreaking contributions to scientific machine learning, particularly through the development of HydraGNN.”
September 19, 2024
4 MIN READ
A study led by the Department of Energy’s Oak Ridge National Laboratory details how artificial intelligence researchers created an AI model to help identify new alloys used as shielding for housing fusion applications components in a nuclear reactor. The findings mark a major step towards improving nuclear fusion facilities.
June 7, 2024
5 MIN READ
ORNL researchers and communications specialists took part in the inaugural AI Expo for National Competitiveness in Washington D.C, May 7 and 8, to showcase and provide insight into how the lab is leading the way for utilizing the vast possibilities of AI.
January 18, 2024
5 MIN READ
Researchers at the Department of Energy’s Oak Ridge and Lawrence Berkeley National Laboratories are evolving graph neural networks to scale on the nation’s most powerful computational resources, a necessary step in tackling today’s data-centric
December 12, 2023
5 MIN READ
A team of computational scientists at ORNL has generated and released datasets of unprecedented scale that provide the ultraviolet visible spectral properties of over 10 million organic molecules.
February 1, 2023
4 MIN READ
We released two open-source datasets named GDB-9-Ex and ORNL_AISD-Ex that provide calculations of electronic excitation energies and their associated oscillator strengths based on the time-dependent density-functional tight-binding (TD-DFTB) method.
July 18, 2022
3 MIN READ
A graph convolutional neural network (GCNN) was trained to accurately predict formation energy and mechanical properties of solid solution alloys crystallized in different lattice structures, thereby advancing the design of alloys for improving mechanical performance.
July 18, 2022
4 MIN READ
A graph convolutional neural network (GCNN) was trained with millions of molecules to accurately predict molecular photo-optical properties by scaling data loading and training to over 1,500 GPUs on the Summit and Perlmutter supercomputers at the OLCF and NERSC supercomputing centers, respectively.
February 14, 2022
2 MIN READ
Deep learning models have trained to predict crystallographic and thermodynamic properties of multi-component solid solution alloys, enabling the design of advanced alloys.
February 14, 2022
2 MIN READ
Researchers from the Computing and Computational Sciences Directorate (CCSD) at Oak Ridge National Laboratory (ORNL) have developed a distributed implementation of graph convolutional neural networks [1]. The code has been shown to successfully take advantage of HPC resources on ORNL-CADES-DGX, ORNL-CADES-CONDO, and ORNL-OLCF-SUMMIT to produce fast and accurate predictions of macroscopic material properties using atomic information.