Data-Driven Studies of Li-Ion-Battery Materials

Abstract

Batteries are a critical component of modern society. The growing demand for new battery materials—coupled with a historically long materials development time—highlights the need for advances in battery materials development. Understanding battery systems has been frustratingly slow for the materials science community. In particular, the discovery of more abundant battery materials has been difficult. In this paper, we describe how machine learning tools can be exploited to predict the properties of battery materials. In particular, we report the challenges associated with a data-driven investigation of battery systems. Using a dataset of cathode materials and various statistical models, we predicted the specific discharge capacity at 25 cycles. We discuss the present limitations of this approach and propose a paradigm shift in the materials research process that would better allow data-driven approaches to excel in aiding the discovery of battery materials.

A database of battery materials auto-generated using ChemDataExtractor

“…This database was then used by Kauwe et al . 26 , who conducted data-driven research using machine-learning tools to predict the capacity of battery materials. However, as their dataset was extracted manually from literature, its size is relatively small.…”

Insight Gained from Using Machine Learning Techniques to Predict the Discharge Capacities of Doped Spinel Cathode Materials for Lithium‐Ion Batteries Applications

“…Similar issues and challenges have also been encountered by Kauwe et al during the data collection work for their data collection studies of LIB materials. [ 35 ] Lastly, despite a strict measure was made on collecting data from battery cells that used organic LiPF 6 electrolyte cells, there is still a variation in the solvents used for this compound, ranging from ethylene carbonate, ethylene methylene carbonate, to dimethyl carbonate.…”

Opportunities and challenges of text mining in materials research

“…The accuracy of scientific NLP imposes constraints on the potential range of questions that the extracted data can address. Kauwe et al. (2019) have investigated the viability and fidelity of ML modeling based on a text-mined dataset.…”

“…They used various ML algorithms and material structure models to predict the discharge capacity of battery materials after 25 cycles based on a dataset extracted from the literature and found inconclusive results. While one can speculate on the origin of this outcome, it is clear that the high level of uncertainty of the predictions can arise from invalid descriptors or models, as well as from the human bias and imperfectness of the experimental measurements ( Kauwe et al., 2019 ). As the “no-free-lunch” theorem states, there is no any particular ML model that will work best for a given task.…”