Iek-Heng Chu

Room-Temperature All-solid-state Rechargeable Sodium-ion Batteries with a Cl-doped Na3PS4 Superionic Conductor.

All-solid-state sodium-ion batteries are promising candidates for large-scale energy storage applications. The key enabler for an all-solid-state architecture is a sodium solid electrolyte that exhibits high Na+ conductivity at ambient temperatures, as well as excellent phase and electrochemical stability. In this work, we present a first-principles-guided discovery and synthesis of a novel Cl-doped tetragonal Na3PS4 (t-Na3−xPS4−xClx) solid electrolyte with a room-temperature Na+ conductivity exceeding 1 mS cm−1. We demonstrate that an all-solid-state TiS2/t-Na3−xPS4−xClx/Na cell utilizing this solid electrolyte can be cycled at room-temperature at a rate of C/10 with a capacity of about 80 mAh g−1 over 10 cycles. We provide evidence from density functional theory calculations that this excellent electrochemical performance is not only due to the high Na+ conductivity of the solid electrolyte, but also due to the effect that “salting” Na3PS4 has on the formation of an electronically insulating, ionically conducting solid electrolyte interphase.

Insights into the Performance Limits of the Li7P3S11 Superionic Conductor: A Combined First-Principles and Experimental Study

The Li7P3S11 glass-ceramic is a promising superionic conductor electrolyte (SCE) with an extremely high Li(+) conductivity that exceeds that of even traditional organic electrolytes. In this work, we present a combined computational and experimental investigation of the material performance limitations in terms of its phase and electrochemical stability, and Li(+) conductivity. We find that Li7P3S11 is metastable at 0 K but becomes stable at above 630 K (∼360 °C) when vibrational entropy contributions are accounted for, in agreement with differential scanning calorimetry measurements. Both scanning electron microscopy and the calculated Wulff shape show that Li7P3S11 tends to form relatively isotropic crystals. In terms of electrochemical stability, first-principles calculations predict that, unlike the LiCoO2 cathode, the olivine LiFePO4 and spinel LiMn2O4 cathodes are likely to form stable passivation interfaces with the Li7P3S11 SCE. This finding underscores the importance of considering multicomponent integration in developing an all-solid-state architecture. To probe the fundamental limit of its bulk Li(+) conductivity, a comparison of conventional cold-press sintered versus spark-plasma sintering (SPS) Li7P3S11 was done in conjunction with ab initio molecular dynamics (AIMD) simulations. Though the measured diffusion activation barriers are in excellent agreement, the AIMD-predicted room-temperature Li(+) conductivity of 57 mS cm(-1) is much higher than the experimental values. The optimized SPS sample exhibits a room-temperature Li(+) conductivity of 11.6 mS cm(-1), significantly higher than that of the cold-pressed sample (1.3 mS cm(-1)) due to the reduction of grain boundary resistance by densification. We conclude that grain boundary conductivity is limiting the overall Li(+) conductivity in Li7P3S11, and further optimization of overall conductivities should be possible. Finally, we show that Li(+) motions in this material are highly collective, and the flexing of the P2S7 ditetrahedra facilitates fast Li(+) diffusion.

Comparison of the polymorphs of VOPO4 as multi-electron cathodes for rechargeable alkali-ion batteries

Multi-electron polyanion cathodes offer the potential for achieving both high voltage and high capacity in rechargeable alkali-ion batteries. Among the few materials known to exhibit multi-electron cycling, the polymorphs of VOPO4, which operate on the V3+–V4+–V5+ redox couples, are particularly promising due to the high gravimetric capacities that have been achieved and the high voltage of the V4+/5+ couple. In this work, we performed a systematic first principles investigation, supported by careful electrochemical characterization and published experimental data, of the relative thermodynamic stability, voltage, band gap, and diffusion kinetics for alkali intercalation into the β, e and αI polymorphs of VOPO4. We find that all VOPO4 polymorphs remain reasonably stable with the insertion of one alkali ion per V, but are significantly destabilized with the insertion of two alkali ions per V. The voltages for Na insertion are ∼0.33–0.69 V lower than those for Li insertion. We find that the αI polymorph is predicted to have higher Li+ migration barriers and larger band gaps than the β and e polymorphs, which account for the relatively worse electrochemical cycling performance observed. On the other hand, only the αI polymorph exhibits reasonably low barriers for Na+ migration compared to the β and e polymorphs, which are consistent with observed electrochemical performances reported thus far in the literature. We also show that differences in the voltage, kinetics and rate capability of these different polymorphs for Li and Na insertion can be traced back to their fundamentally different VO6/VO5–PO4 frameworks.

New Insights into the Interphase between the Na Metal Anode and Sulfide Solid-State Electrolytes: A Joint Experimental and Computational Study

In this work, we investigated the interface between the sodium anode and the sulfide-based solid electrolytes Na3SbS4 (NAS), Na3PS4 (NPS), and Cl-doped NPS (NPSC) in all-solid-state-batteries (ASSBs). Even though these electrolytes have demonstrated high ionic conductivities in the range of 1 mS cm-1 at ambient temperatures, sulfide sold-state electrolytes (SSEs) are known to be unstable with Na metal, though the exact reaction mechanism and kinetics of the reaction remain unclear. We demonstrate that the primary cause of capacity fade and cell failure is a chemical reaction spurred on by electrochemical cycling that takes place at the interface between the Na anode and the SSEs. To investigate the properties of the Na-solid electrolyte interphase (SSEI) and its effect on cell performance, the SSEI was predicted computationally to be composed of Na2S and Na3Sb for NAS and identified experimentally via X-ray photoelectron spectroscopy (XPS). These two compounds give the SSEI mixed ionic- and electronic-conducting properties, which promotes continued SSEI growth, which increases the cell impedance at the expense of cell performance and cycle life. The SSEI for NPS was similarly found to be comprised of Na2S and Na3P, but XPS analysis of Cl-doped NPS (NPSC) showed the presence of an additional compound at the SSEI, NaCl, which was found to mitigate the decomposition of NPS. The methodologies presented in this work can be used to predict and optimize the electrochemical behavior of an all-solid-state cell. Such joint computational and experimental efforts can inform strategies for engineering a stable electrolyte and SSEI to avoid such reactions. Through this work, we call for more emphasis on SSE compatibility with both anodes and cathodes, essential for improving the electrochemical properties, longevity, and practicality of Na-based ASSBs.

Ab Initio Molecular Dynamics Studies of Fast Ion Conductors

Ab initio molecular dynamics (AIMD) is emerging as a computational technique of choice in the study of the kinetics of materials, especially fast ionic conductors that are of immense interest to energy storage and other application. In this chapter, we will first provide an introduction of the theoretical underpinnings of AIMD, including both the Car-Parrinello and Born-Oppenheimer variants and the analysis of such simulations for diffusion properties. As for defects that are frequently introduced via aliovalent doping and are crucial for tuning the ionic conductivity in the conductors, we will briefly discuss the first principles techniques that allow us to measure the dopability of materials. Finally, we will review several application-driven examples, such as electrolytes for solid oxide fuel cells and rechargeable alkali-ion batteries, wherein AIMD techniques have provided useful insights for materials design.

Battery Electrodes, Electrolytes, and Their Interfaces

In recent years, first-principles modeling techniques have made tremendous advances. This allows researchers to estimate the various properties of materials and provide invaluable insights into the physical processes from a microscopic perspective, which cannot directly be assessable by experiments. With the continuing increasing computation powers, first-principles methods are expected to play a more important role in materials design. This chapter aims to serve as a battery-related computation handbook for general readers who may be new to first-principles calculations. Specifically, this chapter will introduce the well-established ab initio modeling methods widely used in battery-related studies from both the thermodynamic and kinetics aspects. The thermodynamic I.-H. Chu · M. Zhang · S. P. Ong ( ) · Y. S. Meng ( ) Department of NanoEngineering, University of California San Diego, La Jolla, CA, USA e-mail: ihchu@eng.ucsd.edu; johnson1228@gmail.com; miz016@eng.ucsd.edu; ongsp@eng.ucsd.edu; shirleymeng@ucsd.edu

Deep neural networks for accurate predictions of crystal stability

Predicting the stability of crystals is one of the central problems in materials science. Today, density functional theory (DFT) calculations remain comparatively expensive and scale poorly with system size. Here we show that deep neural networks utilizing just two descriptors—the Pauling electronegativity and ionic radii—can predict the DFT formation energies of C3A2D3O12 garnets and ABO3 perovskites with low mean absolute errors (MAEs) of 7–10 meV atom−1 and 20–34 meV atom−1, respectively, well within the limits of DFT accuracy. Further extension to mixed garnets and perovskites with little loss in accuracy can be achieved using a binary encoding scheme, addressing a critical gap in the extension of machine-learning models from fixed stoichiometry crystals to infinite universe of mixed-species crystals. Finally, we demonstrate the potential of these models to rapidly transverse vast chemical spaces to accurately identify stable compositions, accelerating the discovery of novel materials with potentially superior properties.Crystal stability prediction is of paramount importance for novel material discovery, with theoretical approaches alternative to expensive standard schemes highly desired. Here the authors develop a deep learning approach which, just using two descriptors, provides crystalline formation energies with very high accuracy.

Predicting the volumes of crystals

Abstract New crystal structures are frequently derived by performing ionic substitutions on known crystal structures. These derived structures are then used in further experimental analysis, or as the initial guess for structural optimization in electronic structure calculations, both of which usually require a reasonable guess of the lattice parameters. In this work, we propose two lattice prediction schemes to improve the initial guess of a candidate crystal structure. The first scheme relies on a one-to-one mapping of species in the candidate crystal structure to a known crystal structure, while the second scheme relies on data-mined minimum atom pair distances to predict the crystal volume of the candidate crystal structure and does not require a reference structure. We demonstrate that the two schemes can effectively predict the volumes within mean absolute errors (MAE) as low as 3.8% and 8.2%. We also discuss the various factors that may impact the performance of the schemes. Implementations for both schemes are available in the open-source pymatgen software.

Correction to Insights into the Performance Limits of the Li7P3S11 Superionic Conductor: A Combined First-Principles and Experimental Study

Author(s): Chu, Iek-Heng; Nguyen, Han; Hy, Sunny; Lin, Yuh-Chieh; Wang, Zhenbin; Xu, Zihan; Deng, Zhi; Meng, Ying Shirley; Ong, Shyue Ping