Pegolo, Paolo

1. PET-MAD, a lightweight universal interatomic potential for advanced materials modeling
   2025/03/18 by Arslan Mazitov, Mazitov, Arslan, Filippo Bigi +15 · 1 voice · 13 citations
   Computer Science · Physics and Astronomy · #Chemical Physics (physics.chem-ph) #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Materials Science (cond-mat.mtrl-sci) #cond-mat.mtrl-sci #cs.LG #physics.chem-ph
2. Exploring the design space of machine-learning models for quantum chemistry with a fully differentiable framework
   2025/04/01 by Divya Suman, Jigyasa Nigam, Suman, Divya +13 · 1 voice · 9 citations
   Physics and Astronomy · #physics.chem-ph
3. Metatensor and metatomic: foundational libraries for interoperable atomistic machine learning
   2025/08/21 by Filippo Bigi, Joseph W. Abbott, Bigi, Filippo +27 · 1 voice · 7 citations
   Physics and Astronomy · #physics.chem-ph
4. Seebeck coefficient of ionic conductors from Bayesian regression analysis
   2024/02/07 by Enrico Drigo, Stefano Baroni, Drigo, Enrico +3 · 2 citations
   Engineering · #FOS: Physical sciences #Materials Science (cond-mat.mtrl-sci) #Phase Equilibria and Thermodynamics
5. Unearthing the foundational role of anharmonicity in heat transport in glasses
   2023/07/14 by Alfredo Fiorentino, Enrico Drigo, Fiorentino, Alfredo +5 · 1 citation
   Engineering · Materials Science · #Computational Physics (physics.comp-ph) #Disordered Systems and Neural Networks (cond-mat.dis-nn) #FOS: Physical sciences #Material Dynamics and Properties #Materials Science (cond-mat.mtrl-sci) #Thermal properties of materials #Thermography and Photoacoustic Techniques
6. Thermal transport of glasses via machine learning driven simulations
   2024/02/09 by Paolo Pegolo, Federico Grasselli, Pegolo, Paolo +1 · 1 citation
   Arts and Humanities · Engineering · #Cultural Heritage Materials Analysis #Disordered Systems and Neural Networks (cond-mat.dis-nn) #FOS: Physical sciences #Industrial Vision Systems and Defect Detection #Materials Science (cond-mat.mtrl-sci) #Surface Roughness and Optical Measurements
7. Revealing Fast Ionic Conduction in Solid Electrolytes through Machine Learning Accelerated Raman Calculations
   2025/11/26 by Manuel Grumet, Takeru Miyagawa, Grumet, Manuel +11 · 1 voice · 1 citation
   Engineering · Materials Science · #Advanced Battery Materials and Technologies #FOS: Physical sciences #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci) #Thermal Expansion and Ionic Conductivity