Machine learning potentials derived from DFT calculations enable equilibrium molecular dynamics to compute thermal conductivities of crystalline and amorphous silicon that match experimental values.
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Thermal Conductivity Modeling using Machine Learning Potentials: Application to Crystalline and Amorphous Silicon
Machine learning potentials derived from DFT calculations enable equilibrium molecular dynamics to compute thermal conductivities of crystalline and amorphous silicon that match experimental values.