Vincenzo Lordi
Scholar

Vincenzo Lordi

Google Scholar ID: HyPBIkgAAAAJ
Lawrence Livermore National Laboratory
Computational Materials ScienceSemiconductorsSpectroscopyRenewable EnergyQuantum Information Science
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Citations & Impact
All-time
Citations
2,338
 
H-index
25
 
i10-index
55
 
Publications
20
 
Co-authors
42
 
Publications
6 items
Inverse design of bespoke interatomic potentials via active learning by information-matching
2026
Cited
0
A probabilistic foundation model for crystal structure denoising, phase classification, and order parameters
2025
Cited
0
Maximizing Efficiency of Dataset Compression for Machine Learning Potentials With Information Theory
2025
Cited
0
Unsupervised Atomic Data Mining via Multi-Kernel Graph Autoencoders for Machine Learning Force Fields
2025
Cited
0
Composable and adaptive design of machine learning interatomic potentials guided by Fisher-information analysis
2025
Cited
0
An information-matching approach to optimal experimental design and active learning
arXiv.org · 2024
Cited
0
Resume
Research Experience
  • Member of the Quantum Simulations Group within the Materials Science Division, Physical and Life Sciences Directorate at Lawrence Livermore National Laboratory (LLNL)
  • Focuses on integrating state-of-the-art quantum simulation approaches with data science and large-scale computing to validate, understand, and predict material properties and performance
  • Research emphasizes complex materials such as disordered and amorphous systems, and solid–liquid/solid–solid interfaces under realistic operating conditions
  • Supports LLNL’s national security mission through collaboration with the Global Security Directorate and the Laboratory for Energy Applications for the Future
  • Leads modeling and simulation efforts for multiple U.S. Department of Energy consortia, including HydroGEN, HyMARC, H2NEW, and the CEI Consortium