Ziqin Rong

A high capacity thiospinel cathode for Mg batteries

Magnesium batteries are energy storage systems that potentially offer high energy density owing to their ability to employ magnesium metal as a negative electrode. Their development, however, has been thwarted by a paucity of functional positive electrode materials after the seminal discovery of the Mo6S8 Chevrel phase over 15 years ago. Herein, we report the second such material – a thiospinel – and demonstrate fully reversible Mg2+ electrochemical cycling vs. a Mg anode, which is complemented by diffraction and first principles calculations. The capacity approaches 80% of the theoretical value at a practical rate (C/5) at 60 °C, and yields a specific energy of 230 Wh kg−1, twice that of the Chevrel benchmark. Our results emphasize the advantage in employing “soft” anions to achieve practical divalent cation mobility.

Understanding the Initial Stages of Reversible Mg Deposition and Stripping in Inorganic Nonaqueous Electrolytes

Multivalent (MV) battery architectures based on pairing a Mg metal anode with a high-voltage (∼3 V) intercalation cathode offer a realistic design pathway toward significantly surpassing the energy storage performance of traditional Li-ion-based batteries, but there are currently only few electrolyte systems that support reversible Mg deposition. Using both static first-principles calculations and ab initio molecular dynamics, we perform a comprehensive adsorption study of several salt and solvent species at the interface of Mg metal with an electrolyte of Mg2+ and Cl– dissolved in liquid tetrahydrofuran (THF). Our findings not only provide a picture of the stable species at the interface but also explain how this system can support reversible Mg deposition, and as such, we provide insights in how to design other electrolytes for Mg plating and stripping. The active depositing species are identified to be (MgCl)+ monomers coordinated by THF, which exhibit preferential adsorption on Mg compared to possible...

Evaluation of sulfur spinel compounds for multivalent battery cathode applications

The rapid growth of portable consumer electronics and electric vehicles demands new battery technologies with greater energy stored at a reduced cost. Energy storage solutions based on multivalent metals, such as Mg, could significantly increase the energy density as compared to lithium-ion based technology. In this paper, we employ density functional theory calculations to systematically evaluate the performance, such as thermodynamic stability, ion diffusivity and voltage, of a group of 3d transition-metal sulfur-spinel compounds (21 in total) for multivalent cathode applications. Based on our calculations, Cr2S4, Ti2S4 and Mn2S4 spinel compounds exhibit improved Mg2+ mobility (diffusion activation energy <650 meV) relative to their oxide counterparts, however the improved mobility comes at the expense of lower voltage and thereby lower theoretical specific energy. Ca2+ intercalating into Cr2S4 spinel exhibits a low diffusion activation barrier of 500 meV and a voltage of ∼2 V, revealing a potential cathode for use in Ca rechargeable batteries.

Finding and proving the exact ground state of a generalized Ising model by convex optimization and MAX-SAT

This paper was supported primarily by the US Department of Energy (DOE) under Contract No. DE-FG02-96ER45571. In addition, some of the test cases for ground states were supported by the Office of Naval Research under contract N00014-14-1-0444.

An efficient algorithm for finding the minimum energy path for cation migration in ionic materials.

The Nudged Elastic Band (NEB) is an established method for finding minimum-energy paths and energy barriers of ion migration in materials, but has been hampered in its general application by its significant computational expense when coupled with density functional theory (DFT) calculations. Typically, an NEB calculation is initialized from a linear interpolation of successive intermediate structures (also known as images) between known initial and final states. However, the linear interpolation introduces two problems: (1) slow convergence of the calculation, particularly in cases where the final path exhibits notable curvature; (2) divergence of the NEB calculations if any intermediate image comes too close to a non-diffusing species, causing instabilities in the ensuing calculation. In this work, we propose a new scheme to accelerate NEB calculations through an improved path initialization and associated energy estimation workflow. We demonstrate that for cation migration in an ionic framework, initializing the diffusion path as the minimum energy path through a static potential built upon the DFT charge density reproduces the true NEB path within a 0.2 Å deviation and yields up to a 25% improvement in typical NEB runtimes. Furthermore, we find that the locally relaxed energy barrier derived from this initialization yields a good approximation of the NEB barrier, with errors within 20 meV of the true NEB value, while reducing computational expense by up to a factor of 5. Finally, and of critical importance for the automation of migration path calculations in high-throughput studies, we find that the new approach significantly enhances the stability of the calculation by avoiding unphysical image initialization. Our algorithm promises to enable efficient calculations of diffusion pathways, resolving a long-standing obstacle to the computational screening of intercalation compounds for Li-ion and multivalent batteries.

Electrostatic Estimation of Intercalant Jump-Diffusion Barriers Using Finite-Size Ion Models

We report on a scheme for estimating intercalant jump-diffusion barriers that are typically obtained from demanding density functional theory-nudged elastic band calculations. The key idea is to relax a chain of states in the field of the electrostatic potential that is averaged over a spherical volume using different finite-size ion models. For magnesium migrating in typical intercalation materials such as transition-metal oxides, we find that the optimal model is a relatively large shell. This data-driven result parallels typical assumptions made in models based on Onsagers reaction field theory to quantitatively estimate electrostatic solvent effects. Because of its efficiency, our potential of electrostatics-finite ion size (PfEFIS) barrier estimation scheme will enable rapid identification of materials with good ionic mobility.

Construction of ground-state preserving sparse lattice models for predictive materials simulations

First-principles based cluster expansion models are the dominant approach in ab initio thermodynamics of crystalline mixtures enabling the prediction of phase diagrams and novel ground states. However, despite recent advances, the construction of accurate models still requires a careful and time-consuming manual parameter tuning process for ground-state preservation, since this property is not guaranteed by default. In this paper, we present a systematic and mathematically sound method to obtain cluster expansion models that are guaranteed to preserve the ground states of their reference data. The method builds on the recently introduced compressive sensing paradigm for cluster expansion and employs quadratic programming to impose constraints on the model parameters. The robustness of our methodology is illustrated for two lithium transition metal oxides with relevance for Li-ion battery cathodes, i.e., Li2xFe2(1−x)O2 and Li2xTi2(1−x)O2, for which the construction of cluster expansion models with compressive sensing alone has proven to be challenging. We demonstrate that our method not only guarantees ground-state preservation on the set of reference structures used for the model construction, but also show that out-of-sample ground-state preservation up to relatively large supercell size is achievable through a rapidly converging iterative refinement. This method provides a general tool for building robust, compressed and constrained physical models with predictive power.Materials simulations: Constructing models guaranteed to preserve the ground statesA method has been developed for performing materials simulations without needing to perform manual parameter tuning for the ground-state. First-principles density functional theory calculations are one of the most commonly used tools for computational materials science research but they cannot easily be applied to large structures that contain many thousands of atoms. In such systems, cluster expansion models are often used but they have a problem: manual parameter tuning is required to preserve the ground-state --- important as this usually governs the materials properties. An international team of researchers led by Gerbrand Ceder from Massachusetts Institute of Technology, the University of California Berkeley and Lawrence Berkeley National Laboratory now present a procedure for constructing cluster expansion models that can preserve the ground states without any need for tuning.