For millennia, materials science has relied on the paradigm of empiricism to describe the physical world and to fuel technological advance. It was only some centuries ago that the paradigm of mathematical modelling was introduced, boosting scientific knowledge with the introduction of laws in the form of equations. Their predictive capability proved essential to the human understanding of nature, but, as the complexity of the problems increased over time, equations became more and more impenetrable, with analytical solutions being often rather challenging to determine. Electronic computers cut this Gordian knot for us, allowing for quick(er) calculations of approximate solutions to the equations mentioned above. [1]Battery science has greatly benefit from these advances: Density Functional Theory allowed for e.g. reliable quantifications of active materials’ figures of merit like stability or operational potential. The introduction of multiscale modelling was also allowed by this third paradigm of science, leveraging quick computation speeds to infer cell level properties from lower-level ones. [2]The data generated both with experimentation and computation has now accumulated, and a great amount is being published on platforms like, but not limited to, the Materials Project. [3] This is where science enters the so-called fourth paradigm, i.e. data driven science, [4] where statistical methods can contaminate and enhance the “traditional” physico-chemical ones.In fact, new approaches to materials screening are being published with increasing frequency, like neural networks able to predict material properties [5], or generative algorithms able to suggest novel materials. [6] It is important to notice that these methods rely heavily on data quality, hence are not to be seen as a substitute for first principle calculations, but rather as techniques able to expand their scope and capabilities.Battery science itself can greatly benefit from the fourth paradigm of science as well: it is in this context that we developed the present work, in an endeavor to tackle a specific issue in battery science: active materials screening has rarely considered the properties that emerge when coupling two active materials, embedding them into composite electrodes and connecting them electrically.The present work introduces a Python-based combinatorial materials screening workflow, named VOLTA. Its pipeline allows for a novel battery active material explorative workflow, that prioritizes the cell level performance indicators, like cell capacity and voltage profile. This is achieved by the construction of a starting dataset of both observed and virtual active materials from the Materials Project, the implementation of a physics-based pipeline for the assessment of practical electrode properties like porosity and thickness, [7] and the coupling of the electrodes into virtual cells, whose figures of merit are calculated, like voltage and capacity. The screening can be conducted by applying filters to these cell-level properties, achieving an indirect selection of the most suitable active materials.The approach is validated through comparison to current commercial battery technology, and we demonstrate that VOLTA is able to identify promising electrode materials for high energy batteries, like the well-known LCO, LNO and graphite. We also illustrate a case-study, where the pipeline is used in a low-voltage "battery discovery" problem. Bibliography [1] Agrawal, A., & Choudhary, A. (2016). Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science. APL Materials, 4(5), 053208.[2] Jain, A., Ong, S. P., Hautier, G., Chen, W., Richards, W. D., Dacek, S., Cholia, S., Gunter, D., Skinner, D., Ceder, G., & Persson, K. A. (2013). Commentary: The Materials Project: A materials genome approach to accelerating materials innovation. APL Materials, 1(1), 011002.[3] Franco, A. A., Rucci, A., Brandell, D., Frayret, C., Gaberscek, M., Jankowski, P., & Johansson, P. (2019). Boosting Rechargeable Batteries R&D by Multiscale Modeling: Myth or Reality? Chemical Reviews, 119(7), 4569–4627.[4] Hey, T., Tansley, S., Tolle, K., & Gray, J. (2009). The Fourth Paradigm: Data-Intensive Scientific Discovery. The Fourth Paradigm: Data-Intensive Scientific Discovery, 39–44.[5] Wang, A. Y.-T., Kauwe, S. K., Murdock, R. J., & Sparks, D. (2021). Compositionally-Restricted Attention-Based Network for Materials Property Predictions. Npj Computational Materials, 33.[6] Dan, Y., Zhao, Y., Li, X., Li, S., Hu, M., & Hu, J. (2020). Generative adversarial networks (GAN) based efficient sampling of chemical composition space for inverse design of inorganic materials. Npj Computational Materials, 6(1), 84.[7] Lombardo, T., Caro, F., Ngandjong, A. C., Hoock, J. B., Duquesnoy, M., Delepine, J. C., Ponchelet, A., Doison, S., & Franco, A. A. (2022). The ARTISTIC Online Calculator: Exploring the Impact of Lithium-Ion Battery Electrode Manufacturing Parameters Interactively Through Your Browser. Batteries & Supercaps, 5(3), e202100324.
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Oct 1, 2024
Mrudula Prasad + 11
Open Access
