2 citations · 3 across the 3 of their papers we have counts for
3 papers
cond-mat.mtrl-sci2025
Screening of material defects using universal machine-learning interatomic potentials
Ethan Berger, Mohammad Bagheri, Hannu-Pekka Komsa
Finding new materials with previously unknown atomic structure or materials with optimal set of properties for a specific application greatly benefits from computational modeling.…
physics.comp-ph2024★ 1 cited
Raman spectra of amino acids and peptides from machine learning polarizabilities
Ethan Berger, Juha Niemelä, Outi Lampela +2
Raman spectroscopy is an important tool in the study of vibrational properties and composition of molecules, peptides and even proteins. Raman spectra can be simulated based on the…
cond-mat.mes-hall2023★ 2 cited
Polarizability Models for Simulations of Finite Temperature Raman Spectra from Machine Learning Molecular Dynamics
Ethan Berger, Hannu-Pekka Komsa
Raman spectroscopy is a powerful and nondestructive method that is widely used to study the vibrational properties of solids or molecules. Simulations of finite-temperature Raman s…