Volume predictor

Volume, an ensemble of 30 GNN models, accepts up to 4 elements

Try La_2Zr_2O_7, or La_2O_3(ZrO_2)_2, or ZrO_2, or HfC_0.93, or Ni_10Fe_72Cr_18

Cite this model:

Si-Da Xue and Qi-Jun Hong, Materials Properties Prediction (MAPP): Empowering the Prediction of Material Properties Solely Based on Chemical Formulas, Materials, 2024. Download.

This model is currently deployed on Microsoft Azure and the Research Computing facilities at ASU. Due to network limit, computation may take up to 10 seconds.

Metrics

R2 training score: 0.985

R2 testing score: 0.975

Root mean square error, training: 0.942 Ang^3/atom

Root mean square error, testing: 1.177 Ang^3/atom

What is new

Version 1: ensemble of 30 deep learning models.

About this model

This model is based on Graph Neural Network (GNN) and Residual Neural Network (ResNet).
This ensemble model is based on bootstrap aggregating (bagging).

Video Introduction