Despite the overall successes of theoretical approaches, many simplifications are needed in practice to enable screening of many catalyst candidates at reasonable computing cost. In particular, scaling relationships (in which adsorption energies of complex molecules are modeled using simpler adsorption energies) (Figure ) and the Brønsted-Evans-Polanyi (BEP) relation (in which activation energies are estimated based on thermodynamic reaction energies) are commonly used approximations when screening materials . These techniques reduce the number and complexity of calculations that need to be performed at the expense of prediction accuracy. Other limitations include constraints on the number of atoms to ~1000 (making it challenging to capture behaviors at higher length scales that are observed in experiments), difficulty of mapping the initial reaction pathways, and high amount of researcher and computer time needed to perform the screening and model secondary effects. As discussed in the next section, machine learning techniques may help mitigate some of these challenges and unlock more rapid and more comprehensive computational screening.