Taras Shevchenko National University of Kyiv

Effective thermal conductivity of porous silicon has long scattered because pore morphology and substrate doping differ, and no single model covered both. Gas-microphone photoacoustic measurements, atomistic molecular dynamics and physics-informed neural networks were compared on the same 48% to 75% porosity layers: molecular dynamics matched heavily doped columnar structures, while the networks matched the stochastic spongy networks of lightly and moderately doped silicon. Symbolic regression then combined porosity and temperature into one analytical expression, with mean absolute percentage error no more than 6.5% on the training set over 5% to 85% porosity and 300 to 900 K.

Effective thermal conductivity of porous silicon has long scattered because pore morphology and substrate doping differ, and no single model covered both. Gas-microphone photoacoustic measurements, atomistic molecular dynamics and physics-informed neural networks were compared on the same 48% to 75% porosity layers: molecular dynamics matched heavily doped columnar structures, while the networks matched the stochastic spongy networks of lightly and moderately doped silicon. Symbolic regression then combined porosity and temperature into one analytical expression, with mean absolute percentage error no more than 6.5% on the training set over 5% to 85% porosity and 300 to 900 K.