Dates: 2022
Principal Investigator: Dr. Fahhad Alharbi
Funded by: SDAIA-KFUPM Joint Research Center for Artificial Intelligence

Description: This proposal aims to use machine learning to develop physics-guided models that predict solid-state materials’ properties directly from chemical formulas of binary and ternary compounds. Building on prior work with large materials databases and accurate crystal symmetry prediction, the project will extend ML to forecast lattice structures, electronic structures, and key properties (optoelectronic, thermal, thermoelectric). The work will involve expanding atomic/ionic data, predicting crystal and band structures, and enabling computational property calculations. Ultimately, these tools will accelerate materials discovery for solar energy applications, including energy harvesting and direct solar use.