Fig. 3: Training dynamics and predictive accuracy of the neural network potential.
From: Machine learning-driven molecular dynamics decodes thermal tuning in graphene foam composites
a SEM images of GF/PDMS composite45 (Reproduced with permission of Elsevier, 2016); b Loss functions of training and testing datasets with the number of training steps; Plots comparing NEP predictions against DFT reference values for energy (c), atomic forces (d), and virial stresses (e); f Convergence check results in atomic forces; g The comparison of Reaxff and DFT; h Comparison of computational speeds between AIMD and MLP-NEP; i Size dependence of thermal conductivity in GF/PDMS composites with a 5% doping rate at different sizes of 30 Å, 45 Å, 60 Å, 75 Å, 300 Å and 600 Å.
