An ORC in genGEO can be designed to either minimize cost or maximize power, which result in different power generation and cost (Section S5.3). As this optimization process can be computationally expensive, genGEO provides lookup tables of simulation design parameters to accomplish these optimizations; however, the code could be modified to perform this optimization during a simulation. This robust and transparent optimization feature of genGEO is an improvement upon the methods used within GETEM and GEOPHIRES. GETEM uses an EXCEL macro to find the second-law efficiency input into the ASPEN-derived characterizations of cost and electricity generation that minimize cost. GEOPHIRES does not optimize for cost and instead uses the same ASPEN-derived equation that provides the maximum electricity generation as a function of production temperature for all simualtions. In our third comparison (Section 3.1.3), we use data from the Tungsten Mountain geothermal power plant in Nevada, U.S., along with two other cases of realistic but hypothetical geologic conditions to compare the capital costs of an entire geothermal power plant facility across these three tools and to validate genGEO.