{"id":824,"date":"2022-03-28T20:57:58","date_gmt":"2022-03-29T03:57:58","guid":{"rendered":"https:\/\/faculty.engineering.asu.edu\/hong\/?page_id=824"},"modified":"2026-02-12T17:12:30","modified_gmt":"2026-02-13T00:12:30","slug":"materials-properties-prediction-mapp","status":"publish","type":"page","link":"https:\/\/faculty.engineering.asu.edu\/hong\/materials-properties-prediction-mapp\/","title":{"rendered":"Materials Properties Prediction (MAPP)"},"content":{"rendered":"
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built<\/i> on experimental data<\/mark>
empowered<\/i> by artificial intelligence<\/mark>
enhanced<\/i> by ab initio calculations<\/mark><\/p>\n\n\t<\/div>\n\n<\/div>\n\n<\/div>\n\n

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Properties<\/td>Dataset Size<\/td>R2 Score (training\/testing)<\/t-d>RMSE (training\/testing)<\/td>Status<\/td><\/tr>
Consortium model<\/a><\/td>–<\/td>–<\/td>–<\/td>Deployed<\/a><\/td><\/tr>
Melting Temperature<\/a><\/td>9,198<\/td>0.995\/0.98<\/td>50\/100 K<\/td>Deployed<\/a><\/td><\/tr>
Bulk Modulus<\/a><\/td>4,236<\/td>0.997\/0.97<\/td>4\/14 GPa<\/td>Deployed<\/a><\/td><\/tr>
Volume<\/a><\/td>49,213<\/td>0.985\/0.975<\/td>0.942\/1.177 Ang^3\/atom<\/td>Deployed<\/a><\/td><\/tr>
Superconductor Critical Temperature<\/a><\/td>12,448<\/td>0.92\/0.90<\/td>8.5\/9.0 K<\/td>Deployed<\/a><\/td><\/tr>
Crystal Structure<\/a><\/td>7,882<\/td>AUC: 0.998\/0.961<\/td>F1 Score: 0.86\/0.66<\/td>Deployed<\/a><\/td><\/tr>\n
Calphad model excess parameter LA,B;0<\/sub><\/a><\/td>790 <\/td>0.96\/0.85 <\/td>18\/35 kJ mol-1<\/sup><\/td>Deployed<\/a><\/td><\/tr>\n
Band gap<\/a><\/td>33990 (GGA) + 735 (GW)  <\/td>0.92\/0.88 <\/td>0.49\/0.64 eV<\/td>Deployed<\/a><\/td><\/tr>\n
Formation Energy<\/a><\/td>46,672 <\/td>0.997\/0.995 <\/td>5\/7 kJ\/mol atoms<\/td>Deployed<\/a><\/td><\/tr>\n
Debye Temperature<\/a><\/td>5,486 <\/td>0.97\/0.91 <\/td>26\/60 K<\/td>Deployed<\/a><\/td><\/tr>
Heat Capacity<\/td>2,074<\/td>0.72\/0.63<\/td>0.66\/0.79 J mol-1<\/sup>K-1<\/sup><\/td>In Progress<\/td><\/tr>
Heat of Fusion<\/td>1,233<\/td>0.94\/0.89<\/td>0.4\/0.6 kJ mol-1<\/sup><\/td>In Progress<\/td><\/tr>
Thermal Expansion<\/td> <\/td> <\/td> <\/td>Planned<\/td><\/tr>\n<\/tbody><\/table><\/figure>\n\n<\/div>\n\n\n
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Not everyone is an expert on machine learning<\/em>
We share these models with you, so…
MAterials Properties Prediction<\/strong> is just a few clicks away<\/p>\n\n<\/div>\n\n\n\n

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