Vincenzo Lordi

According to our database, Vincenzo Lordi authored at least 15 papers between 2016 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Benchmarking the performance of uncertainty quantification methods for neural network-based interatomic potentials.

Nicholas T. Wimer, Juliane Mueller, Sébastien Hamel, Vincenzo Lordi

J. Cheminformatics, December, 2026

Unsupervised atomic data mining via multi-kernel graph autoencoders for machine learning force fields.

Hong Sun, Joshua A. Vita, Amit Samanta, Vincenzo Lordi

Mach. Learn. Sci. Technol., 2026

Cluster-Graph Fingerprinting: A Framework for Quantitative Analysis of Machine-Learned Interatomic Model Training and Simulation Data.

Benjamin R. Laubach, Vincenzo Lordi, Rebecca K. Lindsey

J. Chem. Inf. Model., 2026

2025

A probabilistic foundation model for crystal structure denoising, phase classification, and order parameters.

Hyuna Kwon, Babak Sadigh, Sébastien Hamel, Vincenzo Lordi, John E. Klepeis, Fei Zhou

CoRR, December, 2025

Maximizing Efficiency of Dataset Compression for Machine Learning Potentials With Information Theory.

Benjamin Yu, Vincenzo Lordi, Daniel Schwalbe-Koda

CoRR, November, 2025

Composable and adaptive design of machine learning interatomic potentials guided by Fisher-information analysis.

Weishi Wang, Mark K. Transtrum, Vincenzo Lordi, Vasily V. Bulatov, Amit Samanta

CoRR, April, 2025

LTAU-FF: Loss Trajectory Analysis for Uncertainty in atomistic Force Fields.

Joshua A. Vita, Amit Samanta, Fei Zhou, Vincenzo Lordi

Mach. Learn. Sci. Technol., 2025

2024

Spectroscopy-guided discovery of three-dimensional structures of disordered materials with diffusion models.

Hyuna Kwon, Tim Hsu, Wenyu Sun, Wonseok Jeong, Fikret Aydin, James Chapman, Xiao Chen, Vincenzo Lordi, Matthew R. Carbone, Deyu Lu, Fei Zhou, Tuan Anh Pham

Mach. Learn. Sci. Technol., 2024

Ice Phase Classification Made Easy with Score-Based Denoising.

Hong Sun, Sébastien Hamel, Tim Hsu, Babak Sadigh, Vincenzo Lordi, Fei Zhou

J. Chem. Inf. Model., 2024

An information-matching approach to optimal experimental design and active learning.

Yonatan Kurniawan, Tracianne B. Neilsen, Benjamin L. Francis, Alex M. Stankovic, Mingjian Wen, Ilia A. Nikiforov, Ellad B. Tadmor, Vasily V. Bulatov, Vincenzo Lordi, Mark K. Transtrum

CoRR, 2024

Grand canonical generative diffusion model for crystalline phases and grain boundaries.

Bo Lei, Enze Chen, Hyuna Kwon, Tim Hsu, Babak Sadigh, Vincenzo Lordi, Timofey Frolov, Fei Zhou

CoRR, 2024

Information theory unifies atomistic machine learning, uncertainty quantification, and materials thermodynamics.

Daniel Schwalbe-Koda, Sébastien Hamel, Babak Sadigh, Fei Zhou, Vincenzo Lordi

CoRR, 2024

Towards MatCore: A Unified Metadata Standard for Materials Science.

Jane Greenberg, Pámela Bóveda-Aguirre, John Allison, Pietro Asinari, Maria Chan, Anand Chandrasekaran, Elif Ertekin, Emmanouel Garoufallou, Giulia Galli, Paolo Giannozzi, Feliciano Giustino, Gerhard Goldbeck, Hendrik Heinz, Arthi Jayaraman, Vincenzo Lordi, Kristin A. Persson, Gian-Marco Rignanese, Aidan Thompson, Eric Toberer, Scott McClellan, Ellad B. Tadmor

Proceedings of the Metadata and Semantic Research - 18th Research Conference, 2024

2018

TopoMS: Comprehensive topological exploration for molecular and condensed-matter systems.

Harsh Bhatia, Attila Gyulassy, Vincenzo Lordi, John E. Pask, Valerio Pascucci, Peer-Timo Bremer

J. Comput. Chem., 2018

2016

Interactive exploration of atomic trajectories through relative-angle distribution and associated uncertainties.

Harsh Bhatia, Attila Gyulassy, Valerio Pascucci, Martina Bremer, Mitchell T. Ong, Vincenzo Lordi, Erik W. Draeger, John E. Pask, Peer-Timo Bremer

Proceedings of the 2016 IEEE Pacific Visualization Symposium, 2016