Christophe Domain

most citedA collinear-spin machine learned interatomic potential for Fe

_{7}

_{2}

Ni alloy

15 citations · 16 across the 2 of their papers we have counts for

collaborators

2 papers

cond-mat.mtrl-sci2025★ 1 cited

Unsupervised Machine-Learning Pipeline for Data-Driven Defect Detection and Characterisation: Application to Displacement Cascades

Samuel Del Fré, Andrée de Backer, Christophe Domain +2

Neutron irradiation produces, within a few picoseconds, displacement cascades that are sequences of atomic collisions generating point and extended defects which subsequently affec…

cond-mat.mtrl-sci2023★ 15 cited

A collinear-spin machine learned interatomic potential for Fe $_{7}$ Cr $_{2}$ Ni alloy

Lakshmi Shenoy, Christopher D. Woodgate, Julie B. Staunton +4

We have developed a new machine learned interatomic potential for the prototypical austenitic steel Fe $_{7}$ Cr $_{2}$ Ni, using the Gaussian approximation potential (GAP) framework.…

Unsupervised Machine-Learning Pipeline for Data-Driven Defect Detection and Characterisation: Application to Displacement Cascades

A collinear-spin machine learned interatomic potential for Fe7​Cr2​Ni alloy

A collinear-spin machine learned interatomic potential for Fe $_{7}$ Cr $_{2}$ Ni alloy