Similarly, neural scaling provides practical ways to improve model performance and efficiency. Given an unlimited data and computation budget, the minimum loss in the neural scaling plot and corresponding model can be used. For example, the 300 million parameter ChemGPT model trained on 300 million tokens minimizes the loss in Figure . Likewise, the PaiNN model with capacity ≈ 1000 trained on 10 5 frames minimizes the loss in Figure . This may be valuable for pre-trained models that are designed to be reused and finetuned, where the training cost is amortized over many downstream applications. However, for many scientific applications, greedily optimizing for the minimum loss is not practical or even necessary. From the neural scaling results, identifying regions with the steepest slope allows for optimal and efficient allocation of resources. For example, for large chemical language models, the greatest performance improvements (Figure ) are seen for large data budgets when scaling up small models (10 5 parameters). For small data budgets, more rapid performance improvements are seen when scaling up medium-sized models (10 7 parameters). For NFFs, there are diminishing returns with increasing dataset sizes for low capacity models, while high capacity models show rapid performance improvements with increasing dataset size (Figure ). The benefits from scaling model and dataset sizes should therefore be balanced against the increased computational costs in order to find the most computationally-and data-efficient opportunities for performance improvement. Beyond optimizing resource allocation to achieve better model performance, our results on large chemical models suggest potentially new capabilities of these models, which we will explore next.