Jun Jiang

most citedMachine Learning Assisted Modeling of Amorphous TiO

_{2}

-Doped GeO

_{2}

for Advanced LIGO Mirror Coatings

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

collaborators

2 papers

cond-mat.mtrl-sci2026

Equivariant generative diffusion learns and generalizes the structural ensemble of amorphous oxides

Jun Jiang, Ian Berry, James N. Fry +1

Amorphous materials are statistical ensembles rather than definitive structures, and conventional density-functional (DFT) and machine-learned-potential simulations sample only a s…

cond-mat.mtrl-sci2025★ 1 cited

Machine Learning Assisted Modeling of Amorphous TiO $_{2}$ -Doped GeO $_{2}$ for Advanced LIGO Mirror Coatings

Jun Jiang, Rui Zhang, Kiran Prasai +4

The mechanical loss angle of amorphous TiO $_{2}$ -doped GeO $_{2}$ can be lower than 10 $^{- 4}$ , making it a candidate for Laser Interferometer Gravitational-wave Observatory (LIGO) mirro…

Equivariant generative diffusion learns and generalizes the structural ensemble of amorphous oxides

Machine Learning Assisted Modeling of Amorphous TiO2​-Doped GeO2​ for Advanced LIGO Mirror Coatings

Machine Learning Assisted Modeling of Amorphous TiO $_{2}$ -Doped GeO $_{2}$ for Advanced LIGO Mirror Coatings