High prediction accuracy is required in ML models that aim at functioning as surrogate DFT engines, which can be applied in computationally efficient high-throughput screening exercises. Prioritising excessively high accuracy, such as going below the 17 meV MAE in E f prediction, might favour purely data-driven ML exercise with increasingly complex models, with less attention to injecting "physics" into the ML frameworkphysics-informed ML. Would a physically rich model with moderate accuracy be favoured to a reduced model with complex ML infrastructure and high accuracy?