Materials Properties Prediction (MAPP)
built on experimental data
empowered by artificial intelligence
enhanced by ab initio calculations
| Properties | Dataset Size | R2 Score (training/testing) | RMSE (training/testing) | Status |
| Consortium model | – | – | – | Deployed |
| Melting Temperature | 9,198 | 0.995/0.98 | 50/100 K | Deployed |
| Bulk Modulus | 4,236 | 0.997/0.97 | 4/14 GPa | Deployed |
| Volume | 49,213 | 0.985/0.975 | 0.942/1.177 Ang^3/atom | Deployed |
| Superconductor Critical Temperature | 12,448 | 0.92/0.90 | 8.5/9.0 K | Deployed |
| Crystal Structure | 7,882 | AUC: 0.998/0.961 | F1 Score: 0.86/0.66 | Deployed |
| Heat Capacity | 2,074 | 0.72/0.63 | 0.66/0.79 J mol-1K-1 | In Progress |
| Heat of Fusion | 1,233 | 0.94/0.89 | 0.4/0.6 kJ mol-1 | In Progress |
| Thermal Expansion | Planned | |||
| Calphad model excess parameter LA,B;0 | 790 | 0.96/0.85 | 18/35 kJ mol-1 | Deployed |
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Consortium, an ensemble of 30 GNN models, accepts up to 4 elements
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.
Qi-Jun Hong, A melting temperature database and a neural network model for melting temperature prediction, arXiv, 2021. Download.
Qi-Jun Hong, Sergey V Ushakov, Axel van de Walle, and Alexandra Navrotsky, Melting temperature prediction using a graph neural network model: From ancient minerals to new materials, PNAS, 2022. 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.
About this model
