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 5. HT-DFT screening results for high-performance M-Ca anodes with relaxed voltage constraint. We performed computational screening for M-Ca alloy-type anode materials. Candidates are systems with energy densities and specific energies (calculated with graphite cathode usage assumed as discussed in Section 2.4) higher than the counterparts of Sn. The color (and size) of each marker indicates the volume expansion (inversed trend for size) per Ca.      [Ref. 3]
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 Cathodes for Li-ion Batteries, Science Advances 4, eaao6754 (2018).
  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, V. Hegde, A. Aspuru-Guzik, C. Wolverton, A discovery of the calcium-metal alloy anodes for reversible Ca-ion batteries, Under review.  
  4. J. He, Z. Yao, C. Wolverton, Discovery of oxysulfide based Li-ion superionic conductors, Under review.