General comments

The authors implemented MISCHBARES, an autonomous millimetre-scale high-throughput battery research system operated by hierarchical autonomous laboratory automation and orchestration. The authors present cathode electrolyte interphase (CEI) formation in lithium-ion batteries at various potentials by correlating high-throughput electrochemistry and spectroscopy to demonstrate the usability of the presented work. The authors mention that MISCHBARES integrates automatic quality control for hardware and software to ensure high reliability through an on-the-fly fidelity assessment of each experiment. Finally, the authors note the integration with the Modular and Autonomous Data Analysis Platform (MADAP) software for Data analysis.

The presented work is exciting and describes the MISCHBARES platform as integrating different modules, pipelines, and software to facilitate autonomous millimetre-scale high-throughput battery research. The authors describe the various modules and show how to use the implemented tool in a specific case study. Despite the exciting results presented by the authors, some comments need to be addressed to improve the quality of the presented work:

• Please improve the clarity of the presented work, in particular for abstract and introduction section, facilitating a well understanding of the implemented method and helping to any reader to understand the implemented approach.

• Please add a methodology section to describe the implementation strategies, software description, libraries, components, deployment, etc.

• Please mention the license of the implemented metho

• Please clarify how the authors introduce AI control for the implemented method.

• Please improve the case of study to facilitate the clarity of the obtained results and how to use the implemented method.

• Please incorporate more details about the different data analyzis pipeline implemented in this work and does not explained in the previous work.

• Please discuss the limitations of the implemented library, the hardware and software requerimients

Specific comments

Abstract
- Please improve the abstract of the presented work because it is unclear what is presented in this work, if it is only MISCHBARES or also HELAO and MADAP, or if it is only the integration of these three previously presented works.
- I recommend improving the review of the proposed article because it is unclear what the proposed method is, how it was implemented, what the new methods are, and what was proposed by the authors in this work.

Introduction
- Please improve the redaction of the introduction; it is very complex to understand. For example, in the case of challenges description, it is unclear what the authors want to describe in the section "The challenge of multifidelity and scability". My apologies if I didn't understand the proposed section, but for me, it is unclear and cryptic at some points. The authors write an introduction describing the context, current solution, and state-of-the-art, explain the problems, and, finally, describe the proposed work, results, solved problems, and case of study with a demonstration of the proposed methodologies.

• In general, the authors claim different challenges in subsections, but understanding each of them needs to be clarified or more concise. Try to improve it, thinking about the type of reader associated with the journal.

Results and discussion

• I suggest a schematic representation of the different components included in the implemented framework and showing how each component interacts with the rest of the components.
• Please improve the redaction of the presented work. The work is fascinating and promissory, but a lousy redaction does not help to understand the work and the different possible uses.
• One of the most relevant points in the presented work is the MultiAnalyseHub module. I suggest the authors explain in more detail how all methodologies incorporated in this module are addressed. The authors could use an SI as support to include it. Also, it could be interesting to mention if the methods implemented in this module are common or if there is some previously reported work and state-of-the-art.
• The authors mention your "control protocol"; please give more details about it because it is unclear.
• Describe the content and functionalities of the different QE implemented in the proposed work.
• Please provide more justification for the incorporation of the Telegram chatbot. Is it only for notification methods, or is there an integration for decision-making?
• Please mention the differences between MultiAnalyseHub and MADAP.
• Describe the FAIR principles or add a reference about them.
• In the MultiAnalyseHub section, the authors describe this module. However, the authors incorporate some sentences like " MADAP capabilities offer a rapid assessment of experimental quality and can yield valuable scientific insights". In this work, you don't need to explain MADAP and only need to add the corresponding reference.
• Please discuss why the authors have selected a relational schema to implement the database and why they do not prefer alternatives like graph database or document database.
• Concerning the application of ML algorithms usage, at what moment do the authors recommend applying these methods? What do the authors want with ML? Patter recognition? Predictive model? Classification? The incorporation of ML methods is a bit cryptic and needs to be improved, particularly the explanation of the different tasks, what algorithms, etc.

Questions/Comments/Suggestions
- Automated quality control and data interpretation are the missing puzzle pieces towards prolonged walk-away-times in closed-loop experimentation; why?
- The implementation description of each software artefact is part of a methodology process and not a result of the work. I suggest changing the redaction to clarify it. In other words, the results usually express "what", and the methodology expresses "how".
- In Figure 1-B, why are the tables in the MER presented in different colours?
- Why do the authors use the ROI method for drop detection? Are there other alternatives? Please comment on it.
- The authors could incorporate a schematic representation of the implemented pipelines in the MultiAnalyseHub module, describing input/outputs, critical process, and optional process.
- What are the "AI control" incorporated in this work?