# Autonomous battery research: Principles of heuristic operando experimentation

arXiv: https://arxiv.org/abs/2601.00851 · HTML: https://mlsanity.com/paper/2601.00851/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 60 references that are in the map.
- First posted on arXiv 29 Dec 2025.

## Builds on (most cited first, 4 of 4)

- [Fourier Neural Operator for Parametric Partial Differential Equations](https://mlsanity.com/paper/2010.08895/) (2020)
- [Human-In-the-Loop for Bayesian Autonomous Materials Phase Mapping](https://mlsanity.com/paper/2306.10406/) (2023)
- [Closing the loop: Autonomous experiments enabled by machine-learning-based online data analysis in synchrotron beamline environments](https://mlsanity.com/paper/2306.11899/) (2023)
- [Self-driving Multimodal Studies at User Facilities](https://mlsanity.com/paper/2301.09177/) (2023)

## Built on by (most cited first, 1 of 1)

- [Benchmarking Autonomy in Scientific Experiments: A Hierarchical Taxonomy for Autonomous Large-Scale Facilities](https://mlsanity.com/paper/2601.06978/) (2026)

## Abstract (arXiv)

Unravelling the complex processes governing battery degradation is critical to the energy transition, yet the efficacy of operando characterisation is severely constrained by a lack of Reliability, Representativeness, and Reproducibility (the 3Rs). Current methods rely on bespoke hardware and passive, pre-programmed methodologies that are ill-equipped to capture stochastic failure events. Here, using the Rutherford Appleton Laboratory's multi-modal toolkit as a case study, we expose the systemic inability of conventional experiments to capture transient phenomena like dendrite initiation. To address this, we propose Heuristic Operando experiments: a framework where an AI pilot leverages physics-based digital twins to actively steer the beamline to predict and deterministically capture these rare events. Distinct from uncertainty-driven active learning, this proactive search anticipates failure precursors, redefining experimental efficiency via an entropy-based metric that prioritises scientific insight per photon, neutron, or muon. By focusing measurements only on mechanistically decisive moments, this framework simultaneously mitigates beam damage and drastically reduces data redundancy. When integrated with FAIR data principles, this approach serves as a blueprint for the trusted autonomous battery laboratories of the future.