Quantitative structure-property relationship study of cathode volume changes in lithium ion batteries using ab-initio and partial least squares analysis

Abstract In this paper, we report a method through the combination of ab-initio calculations and partial least squares (PLS) analysis to develop the Quantitative Structure –Activity Relationship (QSAR) formulations of cathode volume changes in lithium ion batteries. The PLS analysis is based on ab-initio calculation data of 14 oxide cathodes with spinel structure LiX2O4 and 14 oxide cathodes with layered-structure LiXO2 (X = Ti, V, Cr, Mn, Fe, Co, Ni, Nb, Mo, Ru, Rh, Pd, Ta, Ir). Five types of descriptors, describing the characteristics of each compound from crystal structure, element, composition, local distortion and electronic level, with 34 factors in total, are adopted to obtain the QSAR formulation. According to the variable importance in projection analysis, the radius of X4+ ion, and the X octahedron descriptors make major contributions to the volume change of cathode during delithiation. The analysis is hopefully applied to the virtual screening and combinatorial design of low-strain cathode materials for lithium ion batteries.

[1]  J. Vybíral,et al.  Big data of materials science: critical role of the descriptor. , 2014, Physical review letters.

[2]  Yong Liang,et al.  A High Capacity Nano ­ Si Composite Anode Material for Lithium Rechargeable Batteries , 1999 .

[3]  M. Fleet.  Distortion parameters for coordination polyhedra , 1976, Mineralogical Magazine.

[4]  W. Rüdorff,et al.  Notizen: Die Strukturen von LiVO2, NaVO2, LiCrO2 und NaCrO2 , 1954 .

[5]  M. Armand,et al.  Issues and challenges facing rechargeable lithium batteries , 2001, Nature.

[6]  Burke,et al.  Generalized Gradient Approximation Made Simple. , 1996, Physical review letters.

[7]  Zonghai Chen,et al.  Large-volume-change electrodes for Li-ion batteries of amorphous alloy particles held by elastomeric tethers , 2003 .

[8]  G. Kresse,et al.  Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set , 1996 .

[9]  G. V. Gibbs,et al.  Quadratic Elongation: A Quantitative Measure of Distortion in Coordination Polyhedra , 1971, Science.

[10]  Alok Choudhary,et al.  A General-Purpose Machine Learning Framework for Predicting Properties of Inorganic Materials , 2016 .

[11]  Blöchl,et al.  Projector augmented-wave method. , 1994, Physical review. B, Condensed matter.

[12]  Matteo Cococcioni,et al.  Towards more accurate First Principles prediction of redox potentials in transition-metal compounds with LDA+U , 2004, cond-mat/0406382.

[13]  Tim Mueller,et al.  Machine Learning in Materials Science , 2016 .

[14]  V. Shenoy,et al.  From ab initio calculations to multiscale design of Si/C core-shell particles for Li-ion anodes. , 2014, Nano letters.

[15]  Nongnuch Artrith,et al.  An implementation of artificial neural-network potentials for atomistic materials simulations: Performance for TiO2 , 2016 .

[16]  Anubhav Jain,et al.  Finding Nature’s Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory , 2010 .

[17]  Ya‐Xia Yin,et al.  A highly reversible, low-strain Mg-ion insertion anode material for rechargeable Mg-ion batteries , 2014 .

[18]  Michel Tenenhaus,et al.  PLS path modeling , 2005, Comput. Stat. Data Anal..

[19]  Yong‐Sheng Hu,et al.  Porous Li4Ti5O12 Coated with N‐Doped Carbon from Ionic Liquids for Li‐Ion Batteries , 2011, Advanced materials.

[20]  Stefan Adams,et al.  High power lithium ion battery materials by computational design , 2011 .

[21]  Yoshitsugu Sone,et al.  Understanding Volume Change in Lithium-Ion Cells during Charging and Discharging Using In Situ Measurements , 2007 .

[22]  Ruijuan Xiao,et al.  Candidate structures for inorganic lithium solid-state electrolytes identified by high-throughput bond-valence calculations , 2015 .

[23]  Yuesheng Wang,et al.  A zero-strain layered metal oxide as the negative electrode for long-life sodium-ion batteries , 2013, Nature Communications.

[24]  Dominic A. Notter,et al.  Contribution of Li-ion batteries to the environmental impact of electric vehicles. , 2010, Environmental science & technology.

[25]  Tsutomu Ohzuku,et al.  Zero‐Strain Insertion Material of Li [ Li1 / 3Ti5 / 3 ] O 4 for Rechargeable Lithium Cells , 1995 .

[26]  W. H. Baur.  The geometry of polyhedral distortions. Predictive relationships for the phosphate group , 1974 .

[27]  B. Dunn,et al.  Electrical Energy Storage for the Grid: A Battery of Choices , 2011, Science.

[28]  Hong Li,et al.  High-throughput design and optimization of fast lithium ion conductors by the combination of bond-valence method and density functional theory , 2015, Scientific Reports.

[29]  S. Ong,et al.  New opportunities for materials informatics: Resources and data mining techniques for uncovering hidden relationships , 2016 .

[30]  Stefan Adams,et al.  From bond valence maps to energy landscapes for mobile ions in ion-conducting solids , 2006 .

[31]  Eckhard Karden,et al.  Energy storage devices for future hybrid electric vehicles , 2007 .

[32]  Robert A. Huggins,et al.  Lithium alloy negative electrodes , 1999 .

[33]  K. Yoo,et al.  Structural and electrical characterization of Li(Mn1−δTiδ)2O4 electrode materials , 1998 .

[34]  K. Müller,et al.  Fast and accurate modeling of molecular atomization energies with machine learning. , 2011, Physical review letters.