Table 2.
Category | Binary | Ternary | Quaternary | |||
---|---|---|---|---|---|---|
Count | Examples | Count | Examples | Count | Examples | |
[Li,K,Na]-Containing | 4 | KF6 NaF8 | 707 | NaY 2 F 7 KY 2 F 7 | 18446 | CsNa2CdF4 Na2CrPbF5 |
Chalco-/oxyhalides | 5 | OF9 SeF9 | 522 | Y2OF6 Sc2OF7 | 17184 | Sr3Cu2IO4 Zr6RhIO2 |
Metal Oxides | 1 | Cu 2 O | 81 | KTi4O5 ReAu2O5 | 501 | YAlV2O6 Y4FeBi2O3 |
3d Metal Oxides | 1 | Cu 2 O | 3 | Zn2(CuO)3 Ti5CuO2 | 1 | TiZnCrO5 |
Intermetallics | 11 | Nb5Sn3 Al 5Ir3 | 123 | HfAl5Ir3 YAl4Ir3 | 425 | Sc5NiSn3Mo ZrAl5OsRh |
Intermetallics HHIp < 2500 | 0 | 0 | 1 | NaMn2AlAu6 |
We list the number of binary, ternary, and quaternary systems for several categories of compounds along with the two most stable predictions. We validated some of the these compounds- NaY2F7 and KY2F7 using DFT computations by leveraging crystal structures of existing materials with similar stoi-chemistry; we found them to be stable using DFT, further literature search revealed that they have already been synthesized recently. Our model predicts Cu2O as the only new binary oxide which is a known compound but was not in our training set.