A deep learning perspective into the figure-of-merit of thermoelectric materials

Article Energy & Fuels

Machine Learning Approach for Maximizing Thermoelectric Properties of BiCuSeO and Discovering New Doping Element

Nuttawat Parse et al.

Summary: This article introduces the application of machine learning in thermoelectric materials research and the method to improve the model. By normalizing and selecting experimental data, as well as limiting important features for model construction, the model for predicting ZT of doped BiCuSeO was significantly improved.

ENERGIES (2022)

Add to Collection

Article Nanoscience & Nanotechnology

Lattice Thermal Conductivity: An Accelerated Discovery Guided by Machine Learning

Russlan Jaafreh et al.

Summary: In this study, a machine learning model was built to predict the lattice thermal conductivity of crystalline materials, and compounds with ultralow LTCs were identified through validation and screening. The model's predictive reliability was confirmed, demonstrating accurate prediction of the LTC-temperature behavior of new compounds.

ACS APPLIED MATERIALS & INTERFACES (2021)

Add to Collection

Article Materials Science, Multidisciplinary