most citedEfficient atomistic simulations of radiation damage in W and W-Mo using machine-learning potentials

21 citations · 23 across the 2 of their papers we have counts for

collaborators

2 papers

physics.app-ph2025★ 2 cited

Fabrication of Poly (ε-Caprolactone) 3D scaffolds with controllable porosity using ultrasound

Martin Weber, Dmitry Nikolaev, Mikko Koskenniemi +9

3D printing has progressed significantly, allowing objects to be produced using a wide variety of materials. Recent advances have employed focused ultrasound in 3D printing, to all…

cond-mat.mtrl-sci2022★ 21 cited

Efficient atomistic simulations of radiation damage in W and W-Mo using machine-learning potentials

Mikko Koskenniemi, Jesper Byggmästar, Kai Nordlund +1

The Gaussian approximation potential (GAP) is an accurate machine-learning interatomic potential that was recently extended to include the description of radiation effects. In this…