Artificial Intelligence Applied to Battery Research: Hype or Reality?

Machine Learning-Assisted Bayesian Optimization for the Discovery of Effective Additives for Dendrite Suppression in Lithium Metal Batteries

In the pursuit of enhancing the performance and safety of lithium (Li)-metal batteries, the discovery of effective electrolyte additives to suppress Li dendrites has emerged as a paramount objective. In this study, we employ an inverse design strategy to identify potential additives for dendrite mitigation. Two key mechanisms, namely, the formation of robust solid electrolyte interphase layers and the leveling mechanism, serve as the foundation for our investigation. Our inverse design strategy is guided by molecular properties such as the lowest unoccupied molecular orbital energy and interaction energy upon Li surface adsorption. An active learning process utilizing Bayesian optimization (BO) was utilized to identify potential molecules with ideal properties. Through this screening process, we uncover a collection of 62 molecules with the potential to act as SEI-forming additives, along with 106 molecules for leveling additives, both surpassing the performance of established additives reported in the literature. This work highlights the potential of BO methods in computationally based inverse design of materials for many applications, and the discovered additives could potentially boost the commercialization of Li-metal batteries.

Zero-Shot Discovery of High-Performance, Low-Cost Organic Battery Materials Using Machine Learning

Organic electrode materials (OEMs), composed of abundant elements such as carbon, nitrogen, and oxygen, offer sustainable alternatives to conventional electrode materials that depend on finite metal resources. The vast structural diversity of organic compounds provides a virtually unlimited design space; however, exploring this space through Edisonian trial-and-error approaches is costly and time-consuming. In this work, we develop a new framework, SPARKLE, that combines computational chemistry, molecular generation, and machine learning to achieve zero-shot predictions of OEMs that simultaneously balance reward (specific energy), risk (solubility), and cost (synthesizability). We demonstrate that SPARKLE significantly outperforms alternative black-box machine learning algorithms on interpolation and extrapolation tasks. By deploying SPARKLE over a design space of more than 670,000 organic compounds, we identified ≈5000 novel OEM candidates. Twenty-seven of them were synthesized and fabricated into coin-cell batteries for experimental testing. Among SPARKLE-discovered OEMs, 62.9% exceeded benchmark performance metrics, representing a 3-fold improvement over OEMs selected by human intuition alone (20.8% based on six years of prior lab experience). The top-performing OEMs among the 27 candidates exhibit specific energy and cycling stability that surpass the state-of-the-art while being synthesizable at a fraction of the cost.

Interpretable Learning of Accelerated Aging in Lithium Metal Batteries

Lithium metal batteries (LMBs) with high energy density are perceived as the most promising candidates to enable long-endurance electrified transportation. However, rapid capacity decay and safety hazards have impeded the practical application of LMBs, where the entangled complex degradation pattern remains a major challenge for efficient battery design and engineering. Here, we present an interpretable framework to learn the accelerated aging of LMBs with a comprehensive data space containing 79 cells varying considerably in battery chemistries and cell parameters. Leveraging only data from the first 10 cycles, this framework accurately predicts the knee points where aging starts to accelerate. Leaning on the framework's interpretability, we further elucidate the critical role of the last 10%-depth discharging on LMB aging rate and propose a universal descriptor based solely on early cycle electrochemical data for rapid evaluation of electrolytes. The machine learning insights also motivate the design of a dual-cutoff discharge protocol, which effectively extends the cycle life of LMBs by a factor of up to 2.8.

A Knowledge-Data Dual-Driven Framework for Predicting the Molecular Properties of Rechargeable Battery Electrolytes

Developing rechargeable batteries that operate within a wide temperature range and possess high safety has become necessary with increasing demands. Rapid and accurate assessment of the melting points (MPs), boiling points (BPs), and flash points (FPs) of electrolyte molecules is essential for expediting battery development. Herein, we introduce Knowledge-based electrolyte Property prediction Integration (KPI), a knowledge-data dual-driven framework for molecular property prediction of electrolytes. Initially, the KPI collects molecular structures and properties, and then automatically organizes them into structured datasets. Subsequently, interpretable machine learning further explores the structure-property relationships of molecules from a microscopic perspective. Finally, by embedding the discovered knowledge into property prediction models, the KPI achieved very low mean absolute errors of 10.4, 4.6, and 4.8 K for MP, BP, and FP predictions, respectively. The KPI reached state-of-the-art results in 18 out of 20 datasets. Utilizing molecular neighbor search and high-throughput screening, 15 and 14 promising molecules, with and without Chemical Abstracts Service Registry Number, respectively, were predicted for wide-temperature-range and high-safety batteries. The KPI not only accurately predicts molecular properties and deepens the understanding of structure-property relationships but also serves as an efficient framework for integrating artificial intelligence and domain knowledge.

Synergizing physics and machine learning for advanced battery management

Improving battery health and safety motivates the synergy of a powerful duo: physics and machine learning. Through seamless integration of these disciplines, the efficacy of mathematical battery models can be significantly enhanced. This paper delves into the challenges and potentials of managing battery health and safety, highlighting the transformative impact of integrating physics and machine learning to address those challenges. Based on our systematic review in this context, we outline several future directions and perspectives, offering a comprehensive exploration of efficient and reliable approaches. Our analysis emphasizes that the integration of physics and machine learning stands as a disruptive innovation in the development of emerging battery health and safety management technologies.

Panoramic view of artificial fruit ripening agents sensing technologies and the exigency of developing smart, rapid, and portable detection devices: A review

Recently, the availability of point-of-care sensor systems has led to the rapid development of smart and portable devices for the detection of hazardous analytes. The rapid flow of artificially ripened fruits into the market is associated with an elevated risk to human life, agriculture, and the ecosystem due to the use of artificial fruit ripening agents (AFRAs). Accordingly, there is a need for the development of "Point-of-care Sensors" to detect AFRAs due to several advantages, such as simple operation, promising detection mechanism, higher selectivity and sensitivity, compact, and portable. Traditional detection approaches are time-consuming and inappropriate for on-the-spot analyses. Presented comprehensive review aimed to reveal how such technology has systematically evolved over time (through conventional, advanced, and portable smart techniques) detection detect AFRA, till date. Moreover, focuses and highlights a framework of initiatives undertaken for technological advancements in the development of smart the portable detection techniques (kits) for the onsite detection of AFRAs in fruits with in-depth discussion over sensing mechanism and analytical performance of the sensing technology. Notably, colorimetric detection methods have the greatest potential for real-time monitoring of AFRA and its residues because they are easy to assemble, have a high level of selectivity and sensitivity, and can be read by the human eye independently. This study sought to differentiate between traditional credible strategies by presenting new prospects, perceptions, and challenges related to portable devices. This review provides systematic framework of advances in portable field recognition strategies for the on-spot AFRA detection in fruits and critical information for development of new paper-based portable sensors for fruit diagnostic sectors.

Self-Driving Laboratories for Chemistry and Materials Science

Self-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through the automation of experimental workflows, along with autonomous experimental planning, SDLs hold the potential to greatly accelerate research in chemistry and materials discovery. This review provides an in-depth analysis of the state-of-the-art in SDL technology, its applications across various scientific disciplines, and the potential implications for research and industry. This review additionally provides an overview of the enabling technologies for SDLs, including their hardware, software, and integration with laboratory infrastructure. Most importantly, this review explores the diverse range of scientific domains where SDLs have made significant contributions, from drug discovery and materials science to genomics and chemistry. We provide a comprehensive review of existing real-world examples of SDLs, their different levels of automation, and the challenges and limitations associated with each domain.

Rechargeable alkali metal-chlorine batteries: advances, challenges, and future perspectives

The emergence of Li-SOCl2 batteries in the 1970s as a high-energy-density battery system sparked considerable interest among researchers. However, limitations in the primary cell characteristics have restricted their potential for widespread adoption in today's sustainable society. Encouragingly, recent developments in alkali/alkaline-earth metal-Cl2 (AM-Cl2) batteries have shown impressive reversibility with high specific capacity and cycle performance, revitalizing the potential of SOCl2 batteries and becoming a promising technology surpassing current lithium-ion batteries. In this review, the emerging AM-Cl2 batteries are comprehensively summarized for the first time. The development history and advantages of Li-SOCl2 batteries are traced, followed by the critical working mechanisms for achieving high rechargeability. The design concepts of electrodes and electrolytes for AM-Cl2 batteries as well as key characterization techniques are also demonstrated. Furthermore, the current challenges and corresponding strategies, as well as future directions regarding the battery are systematically discussed. This review aims to deepen the understanding of the state-of-the-art AM-Cl2 battery technology and accelerate the development of practical AM-Cl2 batteries for next-generation high-energy storage systems.

Advancing Metallic Lithium Anodes: A Review of Interface Design, Electrolyte Innovation, and Performance Enhancement Strategies

Lithium (Li) metal is one of the most promising anode materials for next-generation, high-energy, Li-based batteries due to its exceptionally high specific capacity and low reduction potential. Nonetheless, intrinsic challenges such as detrimental interfacial reactions, significant volume expansion, and dendritic growth present considerable obstacles to its practical application. This review comprehensively summarizes various recent strategies for the modification and protection of metallic lithium anodes, offering insight into the latest advancements in electrode enhancement, electrolyte innovation, and interfacial design, as well as theoretical simulations related to the above. One notable trend is the optimization of electrolytes to suppress dendrite formation and enhance the stability of the electrode-electrolyte interface. This has been achieved through the development of new electrolytes with higher ionic conductivity and better compatibility with Li metal. Furthermore, significant progress has been made in the design and synthesis of novel Li metal composite anodes. These composite anodes, incorporating various additives such as polymers, ceramic particles, and carbon nanotubes, exhibit improved cycling stability and safety compared to pure Li metal. Research has used simulation computing, machine learning, and other methods to achieve electrochemical mechanics modeling and multi-field simulation in order to analyze and predict non-uniform lithium deposition processes and control factors. In-depth investigations into the electrochemical reactions, interfacial chemistry, and physical properties of these electrodes have provided valuable insights into their design and optimization. It systematically encapsulates the state-of-the-art developments in anode protection and delineates prospective trajectories for the technology's industrial evolution. This review aims to provide a detailed overview of the latest strategies for enhancing metallic lithium anodes in lithium-ion batteries, addressing the primary challenges and suggesting future directions for industrial advancement.

AI-assisted chemistry research: a comprehensive analysis of evolutionary paths and hotspots through knowledge graphs

Artificial intelligence (AI) offers transformative potential for chemical research through its ability to optimize reactions and processes, enhance energy efficiency, and reduce waste. AI-assisted chemical research (AI + chem) has become a global hotspot. To better understand the current research status of "AI + chem", this study conducted a scientific bibliometric investigation using CiteSpace. The web of science core collection was utilized to retrieve original articles related to "AI + chem" published from 2000 to 2024. The obtained data allowed for the visualization of the knowledge background, current research status, and latest knowledge structure of "AI + chem". The "AI + chem" has entered a stage of explosive growth, and the number of papers will maintain long-term high-speed growth. This article systematically analyzes the latest progress in "AI + chem" and objectively predicts future trends, including molecular design, reaction prediction, materials design, drug design, and quantum chemistry. The outcomes of this study will provide readers with a comprehensive understanding of the overall landscape of "AI + chem".

Unveiling Confinement Engineering for Achieving High-Performance Rechargeable Batteries

The confinement effect, restricting materials within nano/sub-nano spaces, has emerged as an innovative approach for fundamental research in diverse application fields, including chemical engineering, membrane separation, and catalysis. This confinement principle recently presents fresh perspectives on addressing critical challenges in rechargeable batteries. Within spatial confinement, novel microstructures and physiochemical properties have been raised to promote the battery performance. Nevertheless, few clear definitions and specific reviews are available to offer a comprehensive understanding and guide for utilizing the confinement effect in batteries. This review aims to fill this gap by primarily summarizing the categorization of confinement effects across various scales and dimensions within battery systems. Subsequently, the strategic design of confinement environments is proposed to address existing challenges in rechargeable batteries. These solutions involve the manipulation of the physicochemical properties of electrolytes, the regulation of electrochemical activity, and stability of electrodes, and insights into ion transfer mechanisms. Furthermore, specific perspectives are provided to deepen the foundational understanding of the confinement effect for achieving high-performance rechargeable batteries. Overall, this review emphasizes the transformative potential of confinement effects in tailoring the microstructure and physiochemical properties of electrode materials, highlighting their crucial role in designing novel energy storage devices.

Building Smarter Aqueous Batteries

Amidst the global trend of advancing renewable energies toward carbon neutrality, energy storage becomes increasingly critical due to the intermittency of renewables. As an alternative to lithium-ion batteries (LIBs), aqueous batteries have received growing attention for large-scale energy storage due to their economical and safe features. Despite the fruitful achievements at the material level, the reliability and lifetime of aqueous batteries are still far from satisfactory. Alike LIBs, integrating smartness is essential for more reliable and long-life aqueous batteries via operando monitoring and automatic response to extreme abuses. In this review, recent advances in sensing techniques and multifunctional battery-sensor systems together with self-healing methods in aqueous batteries is summarized. The significant role of artificial intelligence in designing and optimizing aqueous batteries with high efficiency is also highlighted. Ultimately, it is extrapolated toward the future and present the humble perspective for building smarter aqueous batteries.

Principles and Design of Biphasic Self-Stratifying Batteries Toward Next-Generation Energy Storage

Large-scale energy storage devices play pivotal roles in effectively harvesting and utilizing green renewable energies (such as solar and wind energy) with capricious nature. Biphasic self-stratifying batteries (BSBs) have emerged as a promising alternative for grid energy storage owing to their membraneless architecture and innovative battery design philosophy, which holds promise for enhancing the overall performance of the energy storage system and reducing operation and maintenance costs. This minireview aims to provide a timely review of such emerging energy storage technology, including its fundamental design principles, existing categories, and prototype architectures. The challenges and opportunities of this undergoing research topic will also be systematically highlighted and discussed to provide guidance for the subsequent R&D of superior BSBs while conducive to bridging the gap for their future practical application.

Microstructures of layered Ni-rich cathodes for lithium-ion batteries

Millions of electric vehicles (EVs) on the road are powered by lithium-ion batteries (LIBs) based on nickel-rich layered oxide (NRLO) cathodes, and they suffer from a limited driving range and safety concerns. Increasing the Ni content is a key way to boost the energy densities of LIBs and alleviate the EV range anxiety, which are, however, compromised by the rapid performance fading. One unique challenge lies in the worsening of the microstructural stability with a rising Ni-content in the cathode. In this review, we focus on the latest advances in the understanding of NLRO microstructures, particularly the microstructural degradation mechanisms, state-of-the-art stabilization strategies, and advanced characterization methods. We first elaborate on the fundamental mechanisms underlying the microstructural failures of NRLOs, including anisotropic lattice evolution, microcracking, and surface degradation, as a result of which other degradation processes, such as electrolyte decomposition and transition metal dissolution, can be severely aggravated. Afterwards, we discuss representative stabilization strategies, including the surface treatment and construction of radial concentration gradients in polycrystalline secondary particles, the fabrication of rod-shaped primary particles, and the development of single-crystal NRLO cathodes. We then introduce emerging microstructural characterization techniques, especially for identification of the particle orientation, dynamic changes, and elemental distributions in NRLO microstructures. Finally, we provide perspectives on the remaining challenges and opportunities for the development of stable NRLO cathodes for the zero-carbon future.

Electrochemical coupling in subnanometer pores/channels for rechargeable batteries

Subnanometer pores/channels (SNPCs) play crucial roles in regulating electrochemical redox reactions for rechargeable batteries. The delicately designed and tailored porous structure of SNPCs not only provides ample space for ion storage but also facilitates efficient ion diffusion within the electrodes in batteries, which can greatly improve the electrochemical performance. However, due to current technological limitations, it is challenging to synthesize and control the quality, storage, and transport of nanopores at the subnanometer scale, as well as to understand the relationship between SNPCs and performances. In this review, we systematically classify and summarize materials with SNPCs from a structural perspective, dividing them into one-dimensional (1D) SNPCs, two-dimensional (2D) SNPCs, and three-dimensional (3D) SNPCs. We also unveil the unique physicochemical properties of SNPCs and analyse electrochemical couplings in SNPCs for rechargeable batteries, including cathodes, anodes, electrolytes, and functional materials. Finally, we discuss the challenges that SNPCs may face in electrochemical reactions in batteries and propose future research directions.

Sixty years of electrochemical optical spectroscopy: a retrospective

Sixty years ago, Reddy, Devanatan, and Bockris performed the first in situ electrochemical ellipsometry experiment, which ushered in a new era in the study of electrochemistry, using optical spectroscopy. After six decades of development, electrochemical optical spectroscopy, particularly electrochemical vibrational spectroscopy, has advanced from a phase of immaturity with few methods and limited applications to a phase of maturity with excellent substrate generality and significantly improved resolutions. Here, we divide the development of electrochemical optical spectroscopy into four phases, focusing on the proof-of-concept of different electrochemical optical spectroscopy studies, the emergence of plasmonic enhancement-based electrochemical optical spectroscopic (in particular vibrational spectroscopic) methods, the realization of electrochemical vibrational spectroscopy on well-defined surfaces, and the efforts to achieve operando spectroelectrochemical applications. Finally, we discuss the future development trend of electrochemical optical spectroscopy, as well as examples of new methodology and research paradigms for operando spectroelectrochemistry.

Building Stable Anodes for High-Rate Na-Metal Batteries

Due to low cost and high energy density, sodium metal batteries (SMBs) have attracted growing interest, with great potential to power future electric vehicles (EVs) and mobile electronics, which require rapid charge/discharge capability. However, the development of high-rate SMBs has been impeded by the sluggish Na+ ion kinetics, particularly at the sodium metal anode (SMA). The high-rate operation severely threatens the SMA stability, due to the unstable solid-electrolyte interface (SEI), the Na dendrite growth, and large volume changes during Na plating-stripping cycles, leading to rapid electrochemical performance degradations. This review surveys key challenges faced by high-rate SMAs, and highlights representative stabilization strategies, including the general modification of SMB components (including the host, Na metal surface, electrolyte, separator, and cathode), and emerging solutions with the development of solid-state SMBs and liquid metal anodes; the working principle, performance, and application of these strategies are elaborated, to reduce the Na nucleation energy barriers and promote Na+ ion transfer kinetics for stable high-rate Na metal anodes. This review will inspire further efforts to stabilize SMAs and other metal (e.g., Li, K, Mg, Zn) anodes, promoting high-rate applications of high-energy metal batteries towards a more sustainable society.

Materials descriptors of machine learning to boost development of lithium-ion batteries

Traditional methods for developing new materials are no longer sufficient to meet the needs of the human energy transition. Machine learning (ML) artificial intelligence (AI) and advancements have caused materials scientists to realize that using AI/ML to accelerate the development of new materials for batteries is a powerful potential tool. Although the use of certain fixed properties of materials as descriptors to act as a bridge between the two separate disciplines of AI and materials chemistry has been widely investigated, many of the descriptors lack universality and accuracy due to a lack of understanding of the mechanisms by which AI/ML operates. Therefore, understanding the underlying operational mechanisms and learning logic of AI/ML has become mandatory for materials scientists to develop more accurate descriptors. To address those challenges, this paper reviews previous work on AI, machine learning and materials descriptors and introduces the basic logic of AI and machine learning to help materials developers understand their operational mechanisms. Meanwhile, the paper also compares the accuracy of different descriptors and their advantages and disadvantages and highlights the great potential value of accurate descriptors in AI/machine learning applications for battery research, as well as the challenges of developing accurate material descriptors.

Interpretable Machine Learning To Accelerate the Analysis of Doping Effect on Li/Ni Exchange in Ni-Rich Layered Oxide Cathodes

In Ni-rich layered oxide cathodes, one effective way to adjust the performance is by introducing dopants to change the degree of Li/Ni exchange. We calculated the formation energy of Li/Ni exchange defects in LiNi0.8Mn0.1X0.1O2 with different doping elements X, using first-principles calculations. We then proposed an interpretable machine learning method combining the Random Forest (RF) model and the Shapley Additive Explanation (SHAP) analysis to accelerate identification of the key factors influencing the formation energy among the complex variables introduced by doping. The valence state of the doping element effectively regulates Li/Ni exchange defects through changing the valence state of Ni and the strength of the superexchange interaction, and COOPSU-SD and MagO were proposed as two indicators to assess superexchange interaction. The volume change also affects the Li/Ni exchange defects, with a larger volume reduction corresponding to fewer Li/Ni exchange defects.

A Review of Transition Metal Boride, Carbide, Pnictide, and Chalcogenide Water Oxidation Electrocatalysts

Transition metal borides, carbides, pnictides, and chalcogenides (X-ides) have emerged as a class of materials for the oxygen evolution reaction (OER). Because of their high earth abundance, electrical conductivity, and OER performance, these electrocatalysts have the potential to enable the practical application of green energy conversion and storage. Under OER potentials, X-ide electrocatalysts demonstrate various degrees of oxidation resistance due to their differences in chemical composition, crystal structure, and morphology. Depending on their resistance to oxidation, these catalysts will fall into one of three post-OER electrocatalyst categories: fully oxidized oxide/(oxy)hydroxide material, partially oxidized core@shell structure, and unoxidized material. In the past ten years (from 2013 to 2022), over 890 peer-reviewed research papers have focused on X-ide OER electrocatalysts. Previous review papers have provided limited conclusions and have omitted the significance of "catalytically active sites/species/phases" in X-ide OER electrocatalysts. In this review, a comprehensive summary of (i) experimental parameters (e.g., substrates, electrocatalyst loading amounts, geometric overpotentials, Tafel slopes, etc.) and (ii) electrochemical stability tests and post-analyses in X-ide OER electrocatalyst publications from 2013 to 2022 is provided. Both mono and polyanion X-ides are discussed and classified with respect to their material transformation during the OER. Special analytical techniques employed to study X-ide reconstruction are also evaluated. Additionally, future challenges and questions yet to be answered are provided in each section. This review aims to provide researchers with a toolkit to approach X-ide OER electrocatalyst research and to showcase necessary avenues for future investigation.

Data-Driven Insight into the Reductive Stability of Ion-Solvent Complexes in Lithium Battery Electrolytes

Lithium (Li) metal batteries (LMBs) are regarded as one of the most promising energy storage systems due to their ultrahigh theoretical energy density. However, the high reactivity of the Li anodes leads to the decomposition of the electrolytes, presenting a huge impediment to the practical application of LMBs. The routine trial-and-error methods are inefficient in designing highly stable solvent molecules for the Li metal anode. Herein, a data-driven approach is proposed to probe the origin of the reductive stability of solvents and accelerate the molecular design for advanced electrolytes. A large database of potential solvent molecules is first constructed using a graph theory-based algorithm and then comprehensively investigated by both first-principles calculations and machine learning (ML) methods. The reductive stability of 99% of the electrolytes decreases under the dominance of ion-solvent complexes, according to the analysis of the lowest unoccupied molecular orbital (LUMO). The LUMO energy level is related to the binding energy, bond length, and orbital ratio factors. An interpretable ML method based on Shapley additive explanations identifies the dipole moment and molecular radius as the most critical descriptors affecting the reductive stability of coordinated solvents. This work not only affords fruitful data-driven insight into the ion-solvent chemistry but also unveils the critical molecular descriptors in regulating the solvent's reductive stability, which accelerates the rational design of advanced electrolyte molecules for next-generation Li batteries.

Identification of Lithium Compounds on Surfaces of Lithium Metal Anode with Machine-Learning-Assisted Analysis of ToF-SIMS Spectra

Detailed knowledge about contamination and passivation compounds on the surface of lithium metal anodes (LMAs) is essential to enable their use in all-solid-state batteries (ASSBs). Time-of-flight secondary ion mass spectrometry (ToF-SIMS), a highly surface-sensitive technique, can be used to reliably characterize the surface status of LMAs. However, as ToF-SIMS data are usually highly complex, manual data analysis can be difficult and time-consuming. In this study, machine learning techniques, especially logistic regression (LR), are used to identify the characteristic secondary ions of 5 different pure lithium compounds. Furthermore, these models are applied to the mixture and LMA samples to enable identification of their compositions based on the measured ToF-SIMS spectra. This machine-learning-based analysis approach shows good performance in identifying characteristic ions of the analyzed compounds that fit well with their chemical nature. Moreover, satisfying accuracy in identifying the compositions of unseen new samples is achieved. In addition, the scope and limitations of such a strategy in practical applications are discussed. This work presents a robust analytical method that can assist researchers in simplifying the analysis of the studied lithium compound samples, offering the potential for broader applications in other material systems.

Oxide Ion-Conducting Materials Containing Tetrahedral Moieties: Structures and Conduction Mechanisms

This Review presents an overview from the perspective of tetrahedral chemistry on various oxide ion-conducting materials containing tetrahedral moieties which have received continuous growing attention as candidates for key components of various devices, including solid oxide fuel cells and oxygen sensors, due to the deformation and rotation flexibility of tetrahedral units facilitating oxide ion transport. Emphasis is placed on the structural and mechanistic features of various systems ranging from crystalline to amorphous materials, which include a variety of gallates, silicates, germanates, molybdates, tungstates, vanadates, aluminates, niobate, titanates, indium oxides, and the newly reported borates. They contain tetrahedral units in either isolated or linked manners forming different polyhedral dimensionality (0 to 3) with various defect properties and transport mechanisms. The development of oxide ion conductors containing tetrahedral moieties and the elucidation of the roles of tetrahedral units in oxide ion migration have demonstrated diverse opportunities for discovering superior electrolytes for solid oxide fuel cells and other related devices and provided useful clues for uncovering the key factors directing fast oxide ion conduction.

PiNNwall: Heterogeneous Electrode Models from Integrating Machine Learning and Atomistic Simulation

Electrochemical energy storage always involves the capacitive process. The prevailing electrode model used in the molecular simulation of polarizable electrode-electrolyte systems is the Siepmann-Sprik model developed for perfect metal electrodes. This model has been recently extended to study the metallicity in the electrode by including the Thomas-Fermi screening length. Nevertheless, a further extension to heterogeneous electrode models requires introducing chemical specificity, which does not have any analytical recipes. Here, we address this challenge by integrating the atomistic machine learning code (PiNN) for generating the base charge and response kernel and the classical molecular dynamics code (MetalWalls) dedicated to the modeling of electrochemical systems, and this leads to the development of the PiNNwall interface. Apart from the cases of chemically doped graphene and graphene oxide electrodes as shown in this study, the PiNNwall interface also allows us to probe polarized oxide surfaces in which both the proton charge and the electronic charge can coexist. Therefore, this work opens the door for modeling heterogeneous and complex electrode materials often found in energy storage systems.

Deep learning to estimate lithium-ion battery state of health without additional degradation experiments

State of health is a critical state which evaluates the degradation level of batteries. However, it cannot be measured directly but requires estimation. While accurate state of health estimation has progressed markedly, the time- and resource-consuming degradation experiments to generate target battery labels hinder the development of state of health estimation methods. In this article, we design a deep-learning framework to enable the estimation of battery state of health in the absence of target battery labels. This framework integrates a swarm of deep neural networks equipped with domain adaptation to produce accurate estimation. We employ 65 commercial batteries from 5 different manufacturers to generate 71,588 samples for cross-validation. The validation results indicate that the proposed framework can ensure absolute errors of less than 3% for 89.4% of samples (less than 5% for 98.9% of samples), with a maximum absolute error of less than 8.87% in the absence of target labels. This work emphasizes the power of deep learning in precluding degradation experiments and highlights the promise of rapid development of battery management algorithms for new-generation batteries using only previous experimental data.

Porous Flow Field for Next-Generation Proton Exchange Membrane Fuel Cells: Materials, Characterization, Design, and Challenges

Porous flow fields distribute fuel and oxygen for the electrochemical reactions of proton exchange membrane (PEM) fuel cells through their pore network instead of conventional flow channels. This type of flow fields has showed great promises in enhancing reactant supply, heat removal, and electrical conduction, reducing the concentration performance loss and improving operational stability for fuel cells. This review presents the research and development progress of porous flow fields with insights for next-generation PEM fuel cells of high power density (e.g., ∼9.0 kW L^-1). Materials, fabrication methods, fundamentals, and fuel cell performance associated with porous flow fields are discussed in depth. Major challenges are described and explained, along with several future directions, including separated gas/liquid flow configurations, integrated porous structure, full morphology modeling, data-driven methods, and artificial intelligence-assisted design/optimization.

Unity of Opposites between Soluble and Insoluble Lithium Polysulfides in Lithium-Sulfur Batteries

Rechargeable batteries based on Li-S chemistry show promise as being possible for next-generation energy storage devices because of their ultrahigh capacities and energy densities. Research over the past decade has demonstrated that the morphology of lithium polysulfides (LPSs) in electrolytes (soluble or insoluble) plays a decisive role in battery performance. Early studies have focused mainly on inhibiting the dissolution of LPSs and invested considerable effort to realize this objective. However, in recent years, a completely different view that the dissolution of LPSs during battery discharge/charge should be promoted has emerged. At this critical juncture in the large-scale application of Li-S batteries, it is time to summarize and discuss both sides of the contradiction. Herein, an overview of these two opposite views pertaining to soluble and insoluble LPSs, including their historical environment, classical strategies, advantages, and disadvantages. Finally, the future morphology of LPSs in Li-S batteries is predicted based on a multiangle review of research studies conducted thus far, and the reasoning behind this conjecture is thoroughly discussed.

Theoretical Progress of 2D Six-Membered-Ring Inorganic Materials as Anodes for Non-Lithium-Ion Batteries

The use and storage of renewable and clean energy has become an important trend due to resource depletion, environmental pollution, and the rising price of refined fossil fuels. Confined by the limited resource and uneven distribution of lithium, non-lithium-ion batteries have become a new focus for energy storage. The six-membered-ring (SMR) is a common structural unit for numerous material systems. 2D SMR inorganic materials have unique advantages in the field of non-lithium energy storage, such as fast electrochemical reactions, abundant active sites and adjustable band gap. First-principles calculations based on density functional theory (DFT) can provide a basic understanding of materials at the atomic-level and establish the relationship between SMR structural units and electrochemical energy storage. In this review, the theoretical progress of 2D SMR inorganic materials in the field of non-lithium-ion batteries in recent years is discussed to summarize the common relationship among 2D SMR non-lithium energy storage anodes. Finally, the existing challenges are analyzed and potential solutions are proposed.

Accelerating the theoretical study of Li-polysulfide adsorption on single-atom catalysts via machine learning approaches

Li-S batteries are a promising alternative to Li-ion batteries, offering large energy storage capacity and wide operating temperature range. However, their performance is heavily affected by the Li-polysulfide (LiPS) shuttling. Computational screening of LiPS adsorption on single-atom catalyst (SAC) substrates is of great aid to the design of Li-S batteries which are robust against the LiPS shuttling from the cathode to the anode and the electrolyte. To facilitate this process, we develop a machine learning (ML) protocol to accelerate the systematic mapping of dominant local energy minima found with calculations based on the density functional theory (DFT), and, in turn, fast screening of LiPS adsorption properties on SACs. We first validate the approach by probing the potential energy surface for LiPS adsorbed on graphene decorated with a Fe-N4-C SAC. We identify minima whose binding energies are better or on par with the one previously reported in the literature. We then move to analyze the adsorption trends on Zn-N4-C SAC and observe similar adsorption strength and behavior with the Fe-N4-C SAC, highlighting the good predictive power of our protocol. Our approach offers a comprehensive and computationally efficient alternative to conventional approaches studying LiPS adsorption.

Machine Learning Predicts the X-ray Photoelectron Spectroscopy of the Solid Electrolyte Interface of Lithium Metal Battery

X-ray photoelectron spectroscopy (XPS) is a powerful surface analysis technique widely applied in characterizing the solid electrolyte interphase (SEI) of lithium metal batteries. However, experiment XPS measurements alone fail to provide atomic structures from a deeply buried SEI, leaving vital details missing. By combining hybrid ab initio and reactive molecular dynamics (HAIR) and machine learning (ML) models, we present an artificial intelligence ab initio (AI-ai) framework to predict the XPS of a SEI. A localized high-concentration electrolyte with a Li metal anode is simulated with a HAIR scheme for ∼3 ns. Taking the local many-body tensor representation as a descriptor, four ML models are utilized to predict the core level shifts. Overall, extreme gradient boosting exhibits the highest accuracy and lowest variance (with errors ≤ 0.05 eV). Such an AI-ai model enables the XPS predictions of ten thousand frames with marginal cost.

Chemomechanics of Rechargeable Batteries: Status, Theories, and Perspectives

Chemomechanics is an old subject, yet its importance has been revived in rechargeable batteries where the mechanical energy and damage associated with redox reactions can significantly affect both the thermodynamics and rates of key electrochemical processes. Thanks to the push for clean energy and advances in characterization capabilities, significant research efforts in the last two decades have brought about a leap forward in understanding the intricate chemomechanical interactions regulating battery performance. Going forward, it is necessary to consolidate scattered ideas in the literature into a structured framework for future efforts across multidisciplinary fields. This review sets out to distill and structure what the authors consider to be significant recent developments on the study of chemomechanics of rechargeable batteries in a concise and accessible format to the audiences of different backgrounds in electrochemistry, materials, and mechanics. Importantly, we review the significance of chemomechanics in the context of battery performance, as well as its mechanistic understanding by combining electrochemical, materials, and mechanical perspectives. We discuss the coupling between the elements of electrochemistry and mechanics, key experimental and modeling tools from the small to large scales, and design considerations. Lastly, we provide our perspective on ongoing challenges and opportunities ranging from quantifying mechanical degradation in batteries to manufacturing battery materials and developing cyclic protocols to improve the mechanical resilience.

NAi/Li Antisite Defects in the Li1.2Ni0.2Mn0.6O2 Li-Rich Layered Oxide: A DFT Study

Li-rich layered oxide (LRLO) materials are promising positive-electrode materials for Li-ion batteries. Antisite defects, especially nickel and lithium ions, occur spontaneously in many LRLOs, but their impact on the functional properties in batteries is controversial. Here, we illustrate the analysis of the formation of Li/Ni antisite defects in the layered lattice of the Co-free LRLO Li1.2Mn0.6Ni0.2O2 compound through a combination of density functional theory calculations performed on fully disordered supercells and a thermodynamic model. Our goal was to evaluate the concentration of antisite defects in the trigonal lattice as a function of temperature and shed light on the native disorder in LRLO and how synthesis protocols can promote the antisite defect formation.

Applying Classical, Ab Initio, and Machine-Learning Molecular Dynamics Simulations to the Liquid Electrolyte for Rechargeable Batteries

Rechargeable batteries have become indispensable implements in our daily life and are considered a promising technology to construct sustainable energy systems in the future. The liquid electrolyte is one of the most important parts of a battery and is extremely critical in stabilizing the electrode-electrolyte interfaces and constructing safe and long-life-span batteries. Tremendous efforts have been devoted to developing new electrolyte solvents, salts, additives, and recipes, where molecular dynamics (MD) simulations play an increasingly important role in exploring electrolyte structures, physicochemical properties such as ionic conductivity, and interfacial reaction mechanisms. This review affords an overview of applying MD simulations in the study of liquid electrolytes for rechargeable batteries. First, the fundamentals and recent theoretical progress in three-class MD simulations are summarized, including classical, ab initio, and machine-learning MD simulations (section 2). Next, the application of MD simulations to the exploration of liquid electrolytes, including probing bulk and interfacial structures (section 3), deriving macroscopic properties such as ionic conductivity and dielectric constant of electrolytes (section 4), and revealing the electrode-electrolyte interfacial reaction mechanisms (section 5), are sequentially presented. Finally, a general conclusion and an insightful perspective on current challenges and future directions in applying MD simulations to liquid electrolytes are provided. Machine-learning technologies are highlighted to figure out these challenging issues facing MD simulations and electrolyte research and promote the rational design of advanced electrolytes for next-generation rechargeable batteries.

Theory-guided experimental design in battery materials research

A reliable energy storage ecosystem is imperative for a renewable energy future, and continued research is needed to develop promising rechargeable battery chemistries. To this end, better theoretical and experimental understanding of electrochemical mechanisms and structure-property relationships will allow us to accelerate the development of safer batteries with higher energy densities and longer lifetimes. This Review discusses the interplay between theory and experiment in battery materials research, enabling us to not only uncover hitherto unknown mechanisms but also rationally design more promising electrode and electrolyte materials. We examine specific case studies of theory-guided experimental design in lithium-ion, lithium-metal, sodium-metal, and all-solid-state batteries. We also offer insights into how this framework can be extended to multivalent batteries. To close the loop, we outline recent efforts in coupling machine learning with high-throughput computations and experiments. Last, recommendations for effective collaboration between theorists and experimentalists are provided.

Data-driven prediction of battery failure for electric vehicles

Despite great progress in battery safety modeling, accurately predicting the evolution of multiphysics systems is extremely challenging. The question on how to ensure safety of billions of automotive batteries during their lifetime remains unanswered. In this study, we overcome the challenge by developing machine learning techniques based on the recorded data that are uploaded to the cloud. Using charging voltage and temperature curves from early cycles that are yet to exhibit symptoms of battery failure, we apply data-driven models to both predict and classify the sample data by health condition based on the observational, empirical, physical, and statistical understanding of the multiscale systems. The best well-integrated machine learning models achieve a verified classification accuracy of 96.3% (exhibiting an increase of 20.4% from initial model) and an average misclassification test error of 7.7%. Our findings highlight the need for cloud-based artificial intelligence technology tailored to robustly and accurately predict battery failure in real-world applications.

•

A well-integrated machine learning technique is applied to failure prediction

•

A cloud-based closed-loop framework is established for real-world EV applications

•

Cloud-based AI solution is based on an in-depth analysis of the field data

•

Both electrochemical and statistical feature engineering are established

Towards Predictive Synthesis of Inorganic Materials Using Network Science

Accelerating materials discovery is the cornerstone of modern technological competitiveness. Yet, the inorganic synthesis of new compounds is often an important bottleneck in this quest. Well-established quantum chemistry and experimental synthesis methods combined with consolidated network science approaches might provide revolutionary knowledge to tackle this challenge. Recent pioneering studies in this direction have shown that the topological analysis of material networks hold great potential to effectively explore the synthesizability of inorganic compounds. In this Perspective we discuss the most exciting work in this area, in particular emerging new physicochemical insights and general concepts on how network science can significantly help reduce the timescales required to discover new materials and find synthetic routes for their fabrication. We also provide a perspective on outstanding problems, challenges and open questions.