Important interdisciplinary work is being done, but many projects and initiatives unfortunately lack the coordination or incentivization to have an impact beyond academic publications, and many learnings, tools and datasets remain siloed or forgotten.
The electrochemical and battery communities have data generated by experimentalists, algorithms developed by theoreticians, and the electrochemical domain expertise to solve thorny problems. However, what is missing is the common infrastructure.
Leveraging modern software and data science tools allows for many new opportunities to better equip scientists and engineers to have greater impact, but requires greater communication between the relevant stakeholders.
Our goal is to grow an ecosystem of battery innovators that tightens the feedback loop between basic science at academic institutions and applied engineering that takes place in industry to advance battery technology.
Components of this infrastructure include:
1) Open-source modeling and data analysis tools
2) Access to open datasets
3) Emergent best practices for managing data and contributing to the open-source community
We want to shorten the journey from intuition to experimental raw data to insights and intelligent decision making to solve important scientific and technological challenges.
We have already started this process by developing workflow tools for routine battery data management and analysis. Our platform parses electrochemical datasets from leading hardware vendors, and provides a user interface for recording project and testing metadata. Automated plotting and reporting are among the other available features. To date, our software handles basic cycling statistics and voltage profiles as well as more advanced tools such as differential capacity analysis. Future work will allow for custom analysis with an environment for scripting with Python.
We aim to arm aspiring electrochemical data scientists with tools that save time and energy. Beyond this, we help users increase the visibility of their work by curating a platform where new battery models and analyses can be hosted, rather than be delegated to “shelfware” after PhD students graduate and leave their former labs. Further, by hosting data sets and analysis tools, researchers benefit from greater reproducibility and transparency among labs, which helps to engender greater trust between the battery community and the broader public.
In doing so, we hope this platform will serve to accelerate battery innovation.