In some cases, optimization algorithms using Metropolis Monte-Carlo treat temperature as a tuning parameter to balance variety with optimization . Such an interpretation is possible with SPOTLIGHT as well. Increasing the temperature corresponds to larger oscillations in energy, resulting in less optimal but more varied molecules (useful when other key properties not captured by interaction strength are also necessary). Lowering the temperature reduces the variety and makes the algorithm slower, but leads to better optimization. Sometimes, it may also lead to non-convergence due to higher rejection. A temperature of 300 K -1200 K has been found to be quite reasonable and successful in previous use-cases .