activity
20232026
most citedElectronic excited states from physically-constrained machine learning

1 citations · 1 across the 4 of their papers we have counts for

collaborators

5 papers

cond-mat.mes-hall2026

Beyond the Quantum Picture: The Electrodynamic Origin of Chiral Nanoplasmonics

Vasil Saroka, Lorenzo Cupellini, Nicolò Maccaferri +2

Chiral plasmonic nanostructures are rapidly emerging as ideal substrates for enantioselective sensing, chiral near-field engineering, and plasmon-assisted catalysis, owing to their…

physics.chem-ph2025

A symmetry-preserving and transferable representation for learning the Kohn-Sham density matrix

Liwei Zhang, Patrizia Mazzeo, Michele Nottoli +3

The Kohn-Sham (KS) density matrix is one of the most essential properties in KS density functional theory (DFT), from which many other physical properties of interest can be derive…

physics.chem-ph2024

Theoretical study of the influence of the photosynthetic membrane on B800-B850 energy transfer within the peripheral light-harvesting complex LH2

Chawntell Kulkarni, Hallmann Óskar Gestsson, Lorenzo Cupellini +2

Photosynthetic organisms rely on a network of light-harvesting protein-pigment complexes to efficiently absorb sunlight and transfer excitation energy to reaction center proteins f…

physics.chem-ph2024

The OpenMMPol Library for Polarizable QM/MM Calculations of Properties and Dynamics

Mattia Bondanza, Tommaso Nottoli, Michele Nottoli +3

We present a new library designed to provide a simple and straightforward way to implement QM/AMOEBA and other polarizable QM/MM methods based on induced point dipoles. The library…

physics.chem-ph2023★ 1 cited

Electronic excited states from physically-constrained machine learning

Edoardo Cignoni, Divya Suman, Jigyasa Nigam +3

Data-driven techniques are increasingly used to replace electronic-structure calculations of matter. In this context, a relevant question is whether machine learning (ML) should be…