Even without much understanding of the underlying physics linking the response to the gas composition, we could build the model a(x) as follows. Construct the sensor array. Conduct gas exposure experiments: observe the response over a grid of gas compositions to generate data D = {(x i , m i )}. Build a regression model of a(x) using the training data D. Gravimetric MOF sensors are attractive from a modeling perspective because we can construct a reasonable model a(x) without actually constructing the sensor array and conducting laborious experiments. Gas adsorption data in MOFs is abundant; thermodynamic theories of gas adsorption are welldeveloped; and molecular models and simulations can predict adsorption in MOFs. The latter enables the computational design of MOFbased gas sensor arrays, as exemplified by Wilmer and coworkers , who use molecular simulations of adsorption to predict the response of gravimetric, MOF-based sensor arrays and then evaluate their fitness for gas sensing.