most citedAchieving DFT accuracy with a machine-learning interatomic potential: thermomechanics and defects in bcc ferromagnetic iron

298 citations · 298 across the 1 of their papers we have counts for

collaborators

1 paper

cond-mat.mtrl-sci2017★ 298 cited

Achieving DFT accuracy with a machine-learning interatomic potential: thermomechanics and defects in bcc ferromagnetic iron

Daniele Dragoni, Thomas D. Daff, Gabor Csanyi +1

We show that the Gaussian Approximation Potential machine learning framework can describe complex magnetic potential energy surfaces, taking ferromagnetic iron as a paradigmatic ch…