A High-Throughput Computational Search for Novel Li-ion Battery Cathode Materials
Anubhav Jain, Massachusetts Institute of Technology
My project deals with the development of a large-scale database towards the discovery of a new high-rate, high energy density lithium ion battery cathode. During the last few decades, improvements in ab initio computational methods have allowed researchers to rationalize and even predict essential Li-ion electrode properties such as voltage, diffusivity, or thermodynamic stability. When coupled with exponential rises in computational power available to research groups, these developments provide the opportunity for a large-scale computational search for new Li-ion battery materials. Through my research, tens of thousands of novel materials can be generated and computationally screened for battery performance, focusing experiments on the most promising candidates and expanding coverage of new chemical spaces. To date, our group has performed via density functional theory over 50,000 total energy computations and 6,000 voltage predictions. These calculations have covered the space of materials in large experimental databases, as well as variations on these materials, screening them as potential Li battery cathodes.
In addition to screening new materials, my research addresses how large computational data sets can be used to mine useful materials properties. For example, several papers have addressed the tuning of redox couple voltages by polyanion chemistry through the inductive effect. Our calculated voltage data allows us to simultaneously analyze the inductive effect across several structure types, redox couples, and polyanion chemistries. In this way, I hope to contribute a new method of design and screening of advanced materials that may be useful in a multitude of applications.
Abstract Author(s): Anubhav Jain, Geoffroy Hautier, Charles Moore, and Gerbrand Ceder