Machine-Learning-Enabled Tricks of the Trade for Rapid Host Material Discovery in Li-S Battery

Constructing and Evaluating Machine-Learned Interatomic Potentials for Li-Based Disordered Rocksalts

Lithium-based disordered rocksalts (LDRs), which are an important class of positive electrode materials that can increase the energy density of current Li-ion batteries, represent a significantly complex chemical and configurational space for conventional density functional theory (DFT)-based high-throughput screening approaches. Notably, atom-centered machine-learned interatomic potentials (MLIPs) are a promising pathway to accurately model the potential energy surface of highly disordered chemical spaces, such as LDRs, where the performance of such MLIPs has not been rigorously explored yet. Here, we represent a comprehensive evaluation of the accuracy, transferability, and ease of training of five atom-centered MLIPs, including the artificial neural network potentials developed by the atomic energy network (AENET), the Gaussian approximation potential (GAP), the spectral neighbor analysis potential (SNAP) and its quadratic extension (qSNAP), and the moment tensor potential (MTP), in modeling a 11-component LDR chemical space. Specifically, we generate a DFT-calculated data set of 10,842 configurations of disordered LiTMO2 and TMO2 compositions, where TM = Sc, Ti, V, Cr, Mn, Fe, Co, Ni, and/or Cu. To provide a point-of-comparison on the performance of atom-centered MLIPs, we also trained the neural equivariant interatomic potential (NequIP) on a subset of our data. Importantly, we find AENET to be the best potential in terms of accuracy and transferability for energy predictions, while MTP is the best for atomic forces. While AENET is the fastest to train among the MLIPs considered at low number of epochs (300), the training time increases significantly as epochs increase (3300), with a corresponding reduction in training errors (∼60%). Note that AENET and GAP tend to overfit in small data sets, with the extent of overfitting reducing with larger data sets. Finally, we observe AENET to provide reasonable predictions of average Li-intercalation voltages in layered, single-TM LiTMO2 frameworks, compared to DFT (∼10% error on average). Our study should pave the way both for discovering novel disordered rocksalt electrodes and for modeling other configurationally complex systems, such as high-entropy ceramics and alloys.

High-Entropy Alloy Electrocatalysts Bidirectionally Promote Lithium Polysulfide Conversions for Long-Cycle-Life Lithium-Sulfur Batteries

High-entropy alloys (HEAs) have attracted considerable attention, owing to their exceptional characteristics and high configurational entropy. Recent findings demonstrated that incorporating HEAs into sulfur cathodes can alleviate the shuttling effect of lithium polysulfides (LiPSs) and accelerate their redox reactions. Herein, we synthesized nano Pt0.25Cu0.25Fe0.15Co0.15Ni0.2 HEAs on hollow carbons (HCs; denoted as HEA/HC) by a facile pyrolysis strategy. The HEA/HC nanostructures were further integrated into hypha carbon nanobelts (HCNBs). The solid-solution phase formed by the uniform mixture of the five metal elements, i.e., Pt0.25Cu0.25Fe0.15Co0.15Ni0.2 HEAs, gave rise to a strong interaction between neighboring atoms in different metals, resulting in their adsorption energy transformation across a wide, multipeak, and nearly continuous spectrum. Meanwhile, the HEAs exhibited numerous active sites on their surface, which is beneficial to catalyzing the cascade conversion of LiPSs. Combining density functional theory (DFT) calculations with detailed experimental investigations, the prepared HEAs bidirectionally catalyze the cascade reactions of LiPSs and boost their conversion reaction rates. S/HEA@HC/HCNB cathodes achieved a low 0.034% decay rate for 2000 cycles at 1.0 C. Notably, the S/HEA@HC/HCNB cathode delivered a high initial areal capacity of 10.2 mAh cm-2 with a sulfur loading of 9 mg cm-2 at 0.1 C. The assembled pouch cell exhibited a capacity of 1077.9 mAh g-1 at the first discharge at 0.1 C. The capacity declined to 71.3% after 43 cycles at 0.1 C. In this work, we propose to utilize HEAs as catalysts not only to improve the cycling stability of lithium-sulfur batteries, but also to promote HEAs in energy storage applications.

Local Built-In Field at the Sub-nanometric Heterointerface Mediates Cascade Electrochemical Conversion of Lithium-sulfur Batteries

Heterogeneous catalytic mediators have been proposed to play a vital role in enhancing the multiorder reaction and nucleation kinetics in multielectron sulfur electrochemistry. However, the predictive design of heterogeneous catalysts is still challenging, owing to the lack of in-depth understanding of interfacial electronic states and electron transfer on cascade reaction in Li-S batteries. Here, a heterogeneous catalytic mediator based on monodispersed titanium carbide sub-nanoclusters embedded in titanium dioxide nanobelts is reported. The tunable catalytic and anchoring effects of the resulting catalyst are achieved by the redistribution of localized electrons caused by the abundant built-in fields in heterointerfaces. Subsequently, the resulting sulfur cathodes deliver an areal capacity of 5.6 mAh cm-2 and excellent stability at 1 C under sulfur loading of 8.0 mg cm-2 . The catalytic mechanism especially on enhancing the multiorder reaction kinetic of polysulfides is further demonstrated via operando time-resolved Raman spectroscopy during the reduction process in conjunction with theoretical analysis.

Towards Practical Application of Li-S Battery with High Sulfur Loading and Lean Electrolyte: Will Carbon-Based Hosts Win This Race?

As the need for high-energy-density batteries continues to grow, lithium-sulfur (Li-S) batteries have become a highly promising next-generation energy solution due to their low cost and exceptional energy density compared to commercially available Li-ion batteries. Research into carbon-based sulfur hosts for Li-S batteries has been ongoing for over two decades, leading to a significant number of publications and patents. However, the commercialization of Li-S batteries has yet to be realized. This can be attributed, in part, to the instability of the Li metal anode. However, even when considering just the cathode side, there is still no consensus on whether carbon-based hosts will prove to be the best sulfur hosts for the industrialization of Li-S batteries. Recently, there has been controversy surrounding the use of carbon-based materials as the ideal sulfur hosts for practical applications of Li-S batteries under high sulfur loading and lean electrolyte conditions. To address this question, it is important to review the results of research into carbon-based hosts, assess their strengths and weaknesses, and provide a clear perspective. This review systematically evaluates the merits and mechanisms of various strategies for developing carbon-based host materials for high sulfur loading and lean electrolyte conditions. The review covers structural design and functional optimization strategies in detail, providing a comprehensive understanding of the development of sulfur hosts. The review also describes the use of efficient machine learning methods for investigating Li-S batteries. Finally, the outlook section lists and discusses current trends, challenges, and uncertainties surrounding carbon-based hosts, and concludes by presenting our standpoint and perspective on the subject.

Theoretical and Experimental Understanding of Metal Single-Atom Electrocatalysts for Accelerating the Electrochemical Reaction of Lithium-Sulfur Batteries

Metal single-atom materials have attracted tremendous attention in the research field of lithium-sulfur (Li-S) batteries because they can effectively improve the reaction kinetics of sulfur cathodes. However, it is still difficult to determine the best metal single-atom catalyst for Li-S batteries, due to the lack of a unified measurement and evaluation method. Herein, a series of metal single-atom- and nitrogen-doped graphene materials (M-NG, M = Fe, Co, Ni, Ir, Ru) have been prepared as the catalysts for promoting the reaction kinetics of the sulfur reduction reaction process. Using rotating disk electrode measurements and density functional theory-based theoretical calculations, Ni-NG was screened out to be the best catalyst. It is found that Ni-NG materials can provide a kinetically favorable pathway for the reversible conversion of polysulfide conversion, thus increasing the utilization of sulfur. By coating the Ni-NG materials on the separator as a multifunctional interlayer, a commercially available sulfur cathode presents a stable specific capacity of 701.8 mAh g^-1 at a current rate of 0.5C over 400 cycles. Even with a high sulfur loading of 3.8 mg cm^-2, a high areal capacity of 4.58 mAh cm^-2 can be achieved. This work will provide a fundamental understanding of efficient single-atom catalyst materials for Li-S batteries.

Ion sieve membrane: Homogenizing Li+ flux and restricting polysulfides migration enables long life and highly stable Li-S battery

Limited by the notorious Li dendrites growth and serious polysulfide shuttle effect, the development of lithium-sulfur (Li-S) batteries is stagnant. Herein, a multifunctional separator composed of Cu-based metal-organic framework (Cu-MOF) and Li-Nafion was proposed to address the above intractable issues. The Cu-MOF with homogeneous porous structure and abundant Lewis acidic sites not only promotes uniform Li⁺ flux, but also exhibits a strong chemical interaction with polysulfides to inhibit the shuttle effect. Moreover, the narrow pore size distribution in the Cu-MOF and negatively charged gap endowed by the -SO₃^- groups both act as ion sieve to facilitate the passage of Li⁺ and restrict the migration of polysulfide anions, synergistically mitigating the dendritic Li growth and polysulfides shuttling. As a result, the symmetric cell with MOF/Nafion separator achieves ultralong cycling stability (1000 h) and ultralow overpotential of 20 mV at a current density of 1.0 mA cm^-2. Importantly, in the assembled Li-S full battery, the modified PP separator presents the superior cycle stability with capacity retention of 90% after 300 cycles at 0.5 C. Current outcomes open up a new route to design functional separators with ion permselective for realizing the dendrite-free and high-performance Li-S battery.

Understanding the interactions between lithium polysulfides and anchoring materials in advanced lithium-sulfur batteries using density functional theory

Lithium-sulfur batteries (LSBs) are promising energy storage devices because of their high theoretical capacity and energy density. However, the "shuttle" effect in lithium polysulfides (LiPSs) is an unresolved issue that can hinder their practical commercial application. Research on LSBs has focused on finding appropriate materials that suppress this effect by efficiently anchoring the LiPSs intermediates. Quantum chemical computations are a useful tool for understanding the mechanistic details of chemical interaction involving LiPSs, and they can also offer strategies for the rational design of LiPSs anchoring materials. In this perspective, we highlight computational and theoretical work performed on this topic. This includes elucidating and characterizing the adsorption mechanisms, and the dominant types of interactions, and summarizing the binding energies of LiPSs on anchoring materials. We also give examples and discuss the potential of descriptors and machine learning approaches to predict the adsorption strength and reactivity of materials. We believe that both approaches will become indispensable in modelling future LSBs.

Simple Structural Descriptor Obtained from Symbolic Classification for Predicting the Oxygen Vacancy Defect Formation of Perovskites

Symbolic classification is an approach of interpretable machine learning for building mathematical formulas that fit certain data sets. In this work, symbolic classification is used to establish the relationship between oxygen vacancy defect formation energy and structural features. We find a structural descriptor n_a(r_a/E_na - r_b), where n_a is the valence of the a-site ion, r_a is the radius of the a-site ion, E_na is the electronegativity of the a-site ion, and r_b is the radius of the b-site ion. It accelerates the screening of defect-free oxide perovskites in advance of density functional theory (DFT) calculations and experimental characterization. Our results demonstrate the potential of symbolic classification for accelerating the data-driven design and discovery of materials with improved properties.