Engineering and Technologynpj Computational Materials
Fast and accurate machine learning prediction of phonon scattering rates and lattice thermal conductivity
Z. Guo, P. R. Chowdhury, et al.
Unlock the secrets of lattice thermal conductivity with groundbreaking machine learning techniques developed by Ziqi Guo and colleagues. This study achieves unprecedented accuracy in predicting phonon scattering rates and thermal conductivity, overcoming challenges of high skewness and complex contributions. Experience a leap in computational efficiency that paves the way for large-scale thermal transport informatics.
Related Publications
Explore these studies to deepen your understanding
Adjacent work that informs or extends this paper's methodology and findings.
Engineering and Technology
Scale-invariant machine-learning model accelerates the discovery of quaternary chalcogenides with ultralow lattice thermal conductivity
K. Pal, C. W. Park, et al.
Medicine and Health
Prediction of mortality risk and duration of hospitalization of COVID-19 patients with chronic comorbidities based on machine learning algorithms
P. Amiri, M. Montazeri, et al.
Engineering and Technology
Machine learning-enabled forward prediction and inverse design of 4D-printed active plates
X. Sun, L. Yue, et al.
Physics
Machine learning and evolutionary prediction of superhard B-C-N compounds
W. Chen, J. N. Schmidt, et al.

