QCL-layer_10-4rep-rand-m2A_3A-efield_0-10-150-v22-dataset

Abstract

The method for building QC datasets and identifying the laser transition for a design is referenced in [1] A. C. Hernandez, M. Lyu and C. F. Gmachl, "Generating Quantum Cascade Laser Datasets for Applications in Machine Learning," 2022 IEEE Photonics Society Summer Topicals Meeting Series (SUM), 2022, pp. 1-2, doi: 10.1109/SUM53465.2022.9858281.This dataset contains 1800 quantum cascade (QC) structures generated by randomly modifying an initial 10-layer design in the tolerance range of -2 to +3 Angstroms at an applied electric field range of 0 to 150 kV/cm (in 10 kV/cm increments). One structure at one electric field is one design, thus there are 27000 unique designs, represented as a row in the dataset. The layer thicknesses (in angstroms) and the electric field are inputs which get evaluated using a Schrödinger solver, ErwinJr2, to identify the laser transition Figure of Merit (fom*), among other reported outputs.Schmidt DataX Fund at Princeton University, National Science Foundation under Grant No. DGE-2039656, and the Center for Statistics and Machine Learning at Princeton University through the support of MicrosoftQCL-layer_10-4rep-rand-m2A_3A-efield_0-10-150-v22-dataset.csv, README.tx