ThermoElectric Predictions
File:
Mode:
Submode:
Elements:
Temperatures:
Enter temperatures separated by commas, values between 10 and 2500.
Submit
Processing... Please wait
Click here for instructions:
Selected Materials Mode
Selected Materials Sample File
Materials Systems Mode
Materials Systems Sample File
×
N. K. Barua, S. Lee, A. O. Oliynyk, H. Kleinke,
Thermoelectric Materials Performance (zT) Predictions with Machine Learning
, ACS Appl. Mater. Interf.
17
, 1662 - 1673 (2025).
N. K. Barua, H. Kleinke,
Machine Learning Predictions of Thermopower for Thermoelectric Material Screening
, ACS Appl. Energy Mater.
8
, 16110 - 16121 (2025).
N. K. Barua, E. Hall, Y. Cheng, A. O. Oliynyk, H. Kleinke,
An Interpretable Machine Learning Model on Thermal Conductivity using Publicly Available Datasets and Our Internal Lab Dataset
, Chem. Mater.
36
, 7089 - 7100 (2024).
Note: The application accepts all chemical formulae. However, not all compositions are physically meaningful or experimentally viable. The predictions should be treated as exploratory and validated with domain knowledge.
ThermoElectric Predictions