Next Article in Journal
Direct Phasing of Protein Crystals with Non-Crystallographic Symmetry
Previous Article in Journal
Simple and Efficient Spherical Crystallization of Clopidogrel Bisulfate Form-I via Anti-Solvent Crystallization Method
Previous Article in Special Issue
Structural, Mechanical, and Dynamical Properties of Amorphous Li2CO3 from Molecular Dynamics Simulations
Article Menu
Issue 1 (January) cover image

Export Article

Open AccessArticle
Crystals 2019, 9(1), 54;

Data-Driven Studies of Li-Ion-Battery Materials

Materials Science & Engineering Department, University of Utah, Salt Lake City, UT 84112, USA
The Department of Physics, Harvard University, Cambridge, MA 02138, USA
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Received: 9 December 2018 / Revised: 13 January 2019 / Accepted: 14 January 2019 / Published: 18 January 2019
(This article belongs to the Special Issue New Materials for Li-Ion Batteries)
Full-Text   |   PDF [410 KB, uploaded 18 January 2019]   |  


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. View Full-Text
Keywords: battery materials; machine learning; materials discovery battery materials; machine learning; materials discovery

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Supplementary material


Share & Cite This Article

MDPI and ACS Style

Kauwe, S.K.; Rhone, T.D.; Sparks, T.D. Data-Driven Studies of Li-Ion-Battery Materials. Crystals 2019, 9, 54.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Crystals EISSN 2073-4352 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top