  A multi-layer perceptron neural network was used to predict the laser
transition figure of merit, a measure of the laser threshold gain, of over 900
million Quantum Cascade Laser designs using only layer thicknesses and the
applied electric field as inputs. Designs were generated by randomly altering
the layer thicknesses of an initial 10-layer design. Validating the predictions
with our 1D Schr\"odinger solver, the predicted values show 5% to 15% error for
structures where a laser transition could occur, and 35% to 70% error for
structures where there was no laser transition. The algorithm allowed (i) for
the identification of high figure of merit structures, (ii) recognition of
which layers should be altered to maximize the figure of merit at a given
electric field, and (iii) increased the original design figure of merit of 94.7
to 141.2 eV ps \r{A}^2, a 1.5-fold improvement and significant for QC lasers.
The computational time for laser design data collection is greatly reduced from
32 hours for 27000 designs using our 1D Schr\"odinger solver on a virtual
machine, to 8 hours for 907 million designs using the machine learning
algorithm on a laptop computer.
