Solid-state fast ionic conductors are of great interest due to their application potential enabling the development of safer high-performance energy and conversion systems ranging from batteries through supercapacitors to fuel cells, electrolyzers, and novel neuromorphic devices. However, identifying fast ion conductors has remained a slow trial-and-error search process. High-throughput computational screening methods such as our bond valence site energy method can significantly accelerate this materials design, but their implementation not only needs to be computationally efficient and dependable but also simple to be used by experimentalists in order to find widespread usage for guiding experimental efforts to promising classes of candidate materials. To bridge the current gap between computational method developers and application-oriented users, we combine the computationally low-cost bond valence site energy calculations in our softBV software tool using a new automated pathway analysis toolthe bond valence pathway analyzer (BVPA). The integration of BVPA gives rapid comprehensive access to and simplifies the visualization of the desired information on the characteristics of ion transport properties in candidate materials. Examples for the main application of identifying suitable structure types for fast ion transport as solid electrolytes or mixed conducting electrode materials with high-rate capability are given. A new dopant predictor further simplifies defect engineering of the candidate systems by automatically suggesting suitable substitutional dopants for each site in the structure based on a new machine-learned approach.
LiTa2PO8 has recently been reported as a new fast Li-ion conducting structure type within the series of Li x (MO6/2) m (TO4/2) n polyanion oxides. Here, we demonstrate the preparation of LiTa2PO8 by solid-state syntheses, clarify the temperature dependence of lithium distribution and ionic conductivity, and study the structural stability, densification, and achievable total conductivity as a function of sintering conditions synergizing experimental neutron and X-ray powder diffraction and electrochemical studies with computational energy landscape analyses and molecular dynamics simulations. A total room temperature conductivity of 0.7 mS cm–1 with an activation energy of 0.27 eV is achieved after sintering at 1323 K for 10 h. Spark plasma sintering yields high densification >98%, highly reproducible bulk conductivities of 2.8 mS cm–1, in agreement with our bond valence site energy-based pathway predictions, and total conductivities of 0.6 mS cm–1 within minutes. Powder diffraction studies from 3 to 1273 K reveal a reversible flipping of the monoclinic angle from above to below 90° close to room temperature as a consequence of rearrangements of the mobile ions that change the detailed pathway topology. A consistent model of the temperature-dependent Li redistribution, conductivity anisotropy, and transport mechanism is derived from a synopsis of diffraction experiments, experimental conductivity studies, and simulations. Due to the limited electrochemical window of Li x (TaO6/2)2(PO4/2)1 (LTPO), a direct contact with Li metal or high voltage cathode materials leads to degradation, but as demonstrated in this work, semi-solid-state batteries, where LTPO is protected from direct contact with lithium by organic buffer layers, achieve stable cycling.
To achieve higher energy density in safer energy storage systems, a transition to ceramic all‐solid‐state batteries is widely expected. Their performance and cycle‐life is largely controlled by processes at buried interfaces. While experimental operando probing of interfacial processes is under development, first‐principle computational methods are challenged by the complexity of the multiphase models and long simulation periods required to capture slow degradation processes. Thus, simpler empirical reactive forcefields have the potential to substantially accelerate the design and optimization of all‐solid‐state batteries, provided that parameters are available for a wide range of relevant atom types. The energy‐scaled bond valence‐based softBV forcefield has successfully enabled the design of new solid electrolytes or insertion‐type electrode materials and analyses of ion transport processes therein. As a two‐body forcefield, it enables fast simulations for complex structures over long periods, but inevitably shares the tendency of two‐body forcefields to maximize coordination numbers if free volume facilitates a reorganization of the atoms, which makes them less suitable for studying interfacial processes. Herein, this vulnerability of two‐body forcefields is overcome in a computationally efficient way by introducing an embedded‐atom‐method‐inspired bond‐valence‐sum‐based new class of transferable forcefields and its effective use for modeling of surfaces and interfaces is demonstrated.
Identifying new materials that combine high ionic conductivity with structural and electrochemical stability so far remains a slow trial and error search process. To rationally accelerate materials design and exploit the opportunities in the materials genome a dependable rapid screening of materials is required that can pre-select structures that merit higher level computational as well as experimental characterization. Here we report on the progress of our “softBV” bond-valence site energy-based automated pathway analysis utilizing our new Bond Valence Pathway Analyzer (BVPA) – with fast bond valence site energy calculations to quickly obtain suggested candidate materials for fast ionic conduction. BVPA provides rapid and simplified visualization, in order to bridge the gap between experimentalists and simulation [1,2]. Calculation of ion transport pathways can be done extremely quickly on the order of seconds or minutes on desktop PCs providing a speedup factor of 3 to 5 orders of magnitude compared to DFT-based NEB methods. Combined with a graphical user interface our software suite (that can be downloaded from [2] and is free for academic use) should enable experimentalists to quickly identify candidate solid electrolyte materials. We also aim to integrate the pre-screening into an automated workflow for subsequent DFT characterization [3]. Results will be benchmarked against both experimental and DFT NEB migration barriers. Besides the migration barriers the approach now also comprises an AI-based dopant predictor utilizing bond-valence-based crystal chemical descriptors to assist experimentalists in exploring favorable substitutional doping strategies. We will also compare the predictability of absolute room temperature conductivities from static energy landscape analysis, bond-valence based empirical MD simulations and ab initio molecular dynamics (AIMD) simulations. While for small fast-ion conductor structures at sufficiently high temperatures AIMD appears to be the gold standard, the less reliable but computationally empirical approaches have an advantage in modelling complex disordered interfaces at low temperatures over longer periods. This eliminates the hazards involved in extrapolations down to room temperature properties for the frequent cases of order-disorder phase transitions at intermediate temperatures. As an example we will discuss lithium and sodium compounds containing multiple anions, in particular the combination of thiophosphate and halide anions or various MS4 polyanions. Based on computational screening using our bond valence site approach and DFT studies several thiophosphate halides along the A3PS4-LiX (Cl, Br, I; A = Li, Na) tie line [4] and the Ax(MS4)y(M’S4)z phase space [5] have been explored and their properties discussed based on BVSE pathway models and molecular dynamics simulations in combination with experimental (X-ray and neutron) diffraction, solid state NMR and electrochemical characterisation. MD simulations e.g. show that Li5(PS4)Cl2 is found to undergo an order-disorder phase transition and thus should, contrasting to earlier predictions, not be a fast Li+ ion conductor. The newly predicted thermodynamically stable cubic solid electrolyte Li15(PS4)4Cl3 was successfully prepared and characterized. Though its conductivity does not reach a superionic level, it demonstrates that the computational approach can successfully predict a completely new classes of solid electrolytes and can predict its optimization by doping. The simplicity of the approach also facilitates the study of homogeneity ranges as exemplified for the solid solution systems Li4-xPS4Ix (0<x<0.67) and Na9+x(MS4)3-x(SnS4)x (M = P, Sb; x ≈ 2). References: [1] L.L. Wong, K.C. Phuah, H. Chen, W.S. Chew, R. Dai, S Adams; submitted. [2] http://www.dmse.nus.edu.sg/asn/software.html [2] B. He, S. Chi, A. Ye et al.; accepted in npj Scientific Data 2020. [3] R. Prasada Rao, H. Chen, S. Adams; Chemistry of Materials 31 (2019) 8649-8662. [4] A. Sorkin, S. Adams, accepted in Materials Advances 2020.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.