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Mikko Koskenniemi

2 papers hereh-index 115 citations5 works total

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author position
  • first author1
  • middle author1

Across the 2 of 2 papers where every author was matched, so the position is known.

fields
  • cond-mat.mtrl-sci1
  • physics.app-ph1

identity via Semantic Scholar / OpenAlex

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

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Showing cond-mat.mtrl-sciShow all

1 paper · 1 filter

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…

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