Overall, we demonstrated that incorporating multitask learning with an attention mechanism creates a framework that can achieve chemistry agnosticism as envisioned by Battery 2030+ 5 and the interesting fact that a DL architecture trained on a smaller, noisier, but more diverse dataset yields better generalization at the cost of higher uncertainty. We hope that the pipeline will emerge as an indispensable and transformative tool to bridge the gap between lab-scale research and commercial viability, and will become essential for development of applications and insightful predictive models in the energy storage field.