3 citations · 3 across the 2 of their papers we have counts for
4 papers · 1 filter
Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles
MikoÅaj J. Gawkowski, Nongnuch Artrith, Silvia Bonfanti +14
Foundation machine learning interatomic potentials (MLIPs) are increasingly being used as drop-in replacements for first-principles calculations, enabling simulations of materials…
Accurate Machine Learning Interatomic Potentials for Polyacene Molecular Crystals: Application to Single Molecule Host-Guest Systems
Burak Gurlek, Shubham Sharma, Paolo Lazzaroni +2
Emerging machine learning interatomic potentials (MLIPs) offer a promising solution for large-scale accurate material simulations, but stringent tests related to the description of…
Investigating Anharmonicities in Polarization-Orientation Raman Spectra of Acene Crystals with Machine Learning
Paolo Lazzaroni, Shubham Sharma, Mariana Rossi
We present a first-principles machine-learning computational framework to investigate anharmonic effects in polarization-orientation (PO) Raman spectra of molecular crystals, focus…
Frontier orbitals control dynamical disorder in molecular semiconductors
Alexander Neef, Sebastian Hammer, Yuxuan Yao +11
Charge transport in organic semiconductors is limited by dynamical disorder. Design rules for new high-mobility materials have therefore focused on limiting its two foundations: st…