High-throughput DFT/Machine Learning

        Density functional theory (DFT) calculations have been widely used as efficient tools for battery research to predict phase transformation during electrochemical reactions, material electrical and ionic conductivity, and explore new electrode materials. Using DFT based high-throughput screening method, we identified candidate materials for novel battery chemistries. Here we build a novel strategy with new criterions added based on observations reported in this field.
Figure 6. A high-throughput discovery of novel hybrid Metal-ion/Metal-O2 battery chemistries (Metal = Li, Na, Mg, and Al). Results of composition, thermodynamic, and kinetic screening steps. Highlighting reactions that provide a capacity > 500 mAh/g. [Ref. 2]
Representative Publications
  1. Z. Yao, S. Kim, J. He, V. I. Hegde, C. Wolverton, Interplay of Cation and Anion Redox in Li4Mn2O5 Material and Prediction of Improved Li4(Mn,M)2O5 Education Cathodes for Li-ion Batteries, Under review.
  2. Z. Yao, V. Hedge, M. K. Y. Chan, M. M. Thackeray, and C. Wolverton, A High- Throughput Discovery of Novel Hybrid Metal-ion-Metal-O2 Battery Chemistries Metal = Li, Na, Mg, and Al, Under review.
  3. Z. Yao, S. Kim, K. Michel, Y. Zhang, M. Aykol, C. Wolverton, Stability and Conductivity Study of the Complex Lithium Borohydride Based Solid-state Electrolytes from First Principles, Under review.
  4. L. Li, Z. Yao, J. Zhu, K. Chen, C. Wolverton, M. C. Hersam, Comprehensive Enhancement of Nanostructured NMC Cathode Materials via Conformal Graphene Dispersion, Under review.
  5. L. Li, S. Kim, Z. Yao, J. Zhu, K. Chen, L. M. Guiney, X. Liu, Z. Wang, C. Wolverton, M. C. Hersam, Toward A Qualitative Understanding of Graphene in Improving Electrochemical Performance of Spinel LiMn2O4 Cathodes, Under review.