In general, more complex algorithms can be more responsive to QSD inputs, but the complexity of an algorithm does not inherently correlate with usefulness or accuracy. As the algorithm becomes more mechanism-driven and complex, it may require additional or larger datasets for calibration, validation, and prediction, which may not be readily available. Its accuracy may also be affected by parameter uncertainty and missing mechanisms that introduce bias and skew results, and increased complexity will increase demand for computational resources. For example, one study used a complex model for risk evaluation of radioactive waste disposal composed of 286 sub-models and thousands of parameters, but its utility was undermined by the large uncertainty of a single key variable. In contrast, the Arrhenius equation, an empirical equation describing the correlation between a reaction rate constant and temperature, consists of only two reaction-specific parameters (the reaction activation energy and the pre-exponential factor), yet it is arguably one of the most widely used models in thermodynamics.