Machine Learning Enabled Computational Screening of Inorganic Solid Electrolytes for Suppression of Dendrite Formation in Lithium Metal Anodes

Solutions for Lithium Battery Materials Data Issues in Machine Learning: Overview and Future Outlook

The application of machine learning (ML) techniques in the lithium battery field is relatively new and holds great potential for discovering new materials, optimizing electrochemical processes, and predicting battery life. However, the accuracy of ML predictions is strongly dependent on the underlying data, while the data of lithium battery materials faces many challenges, such as the multi-sources, heterogeneity, high-dimensionality, and small-sample size. Through the systematic review of the existing literatures, several effective strategies are proposed for data processing as follows: classification and extraction, screening and exploration, dimensionality reduction and generation, modeling and evaluation, and incorporation of domain knowledge, with the aim to enhance the data quality, model reliability, and interpretability. Furthermore, other possible strategies for addressing data quality such as database management techniques and data analysis methodologies are also emphasized. At last, an outlook of ML development for data processing methods is presented. These methodologies are not only applicable to the data of lithium battery materials, but also endow important reference significance to electrocatalysis, electrochemical corrosion, high-entropy alloys, and other fields with similar data challenges.

Heterogeneity in Point Defect Distribution and Mobility in Solid Ion Conductors

Alkali metal anodes paired with solid ion conductors offer promising avenues for enhancing battery energy density and safety. To facilitate rapid ion transport crucial for fast charging and discharging of batteries, it is essential to understand the behavior of point defects in these conductors. In this study, we investigate the heterogeneity of defect distribution in two prototypical solid ion conductors, Li3OCl and Li2PO2N (LiPON), by quantifying the defect formation energy (DFE) as a function of distance from the surface and interface through first-principles simulations. To simulate defects at the electrode-electrolyte interface, we perform calculations of Li+ vacancy in Li3OCl near its interface with lithium metal. Our results reveal a significant difference between the bulk and surface/interface DFE which could lead to defect aggregation/depletion near the surface/interface. Interestingly, while Li3OCl has a lower surface DFE than the bulk in most cases, LiPON follows the opposite trend with a higher surface DFE compared to the bulk. Due to this difference between bulk and surface DFE, the defect density can be up to 14 orders of magnitude higher at surfaces compared to the bulk. Further, we reveal that the DFE transition from surface/interface to bulk is precisely characterized by an exponentially decaying function. By incorporating this exponential trend, we develop a revised model for the average behavior of defects in solid ion conductors that offers a more accurate description of the influence of grain sizes. Surface effects dominate for grain sizes ≲1 μm, highlighting the importance of surface defect engineering and the DFE function for accurately capturing ion transport in devices. We further explore the kinetics of defect redistribution by calculating the migration barriers for defect movement between bulk and surfaces. We find a highly asymmetric energy landscape for the lithium vacancies, exhibiting lower migration barriers for movement toward the surface compared to the bulk, while interstitial defects exhibit comparable kinetics between surface and bulk regions. These insights highlight the importance of considering both thermodynamic and kinetic factors in designing solid ion conductors for improved ion transport at surfaces and interfaces.

Predicting Reactivity and Passivation of Solid-State Battery Interfaces

In this work, we build a computationally inexpensive, data-driven model that utilizes atomistic structure information to predict the reactivity of interfaces between any candidate solid-state electrolyte material and a Li metal anode. This model is trained on data from ab initio molecular dynamics (AIMD) simulations of the time evolution of the solid electrolyte-Li metal interfaces for 67 different materials. Predicting the reactivity of solid-state interfaces with ab initio techniques remains an elusive challenge in materials discovery and informatics, and previous work on predicting interfacial compatibility of solid-state Li-ion electrolytes and Li metal anodes has focused mainly on thermodynamic convex hull calculations. Our framework involves training machine learning models on AIMD data, thereby capturing information on both kinetics and thermodynamics, and then leveraging these models to predict the reactivity of thousands of new candidates in the span of seconds, avoiding the need for additional weeks-long AIMD simulations. We identify over 300 new chemically stable and over 780 passivating solid electrolytes that are predicted to be thermodynamically unfavored. Our results indicate many potential solid-state electrolyte candidates have been incorrectly labeled unstable via purely thermodynamic approaches using density functional theory (DFT) energetics, and that the pool of promising, Li-stable solid-state electrolyte materials may be much larger than previously thought from screening efforts. To showcase the value of our approach, we highlight two borate materials that were identified by our model and confirmed by further AIMD calculations to likely be highly conductive and chemically stable with Li: LiB13C2 and LiB12PC.

Revolutionizing Solid-State NASICON Sodium Batteries: Enhanced Ionic Conductivity Estimation through Multivariate Experimental Parameters Leveraging Machine Learning

Na superionic conductor (NASICON) materials hold promise as solid-state electrolytes due to their wide electrochemical stability and chemical durability. However, their limited ionic conductivity hinders their integration into sodium-ion batteries. The conventional approach to electrolyte design struggles with comprehending the intricate interactions of factors impacting conductivity, encompassing synthesis parameters, structural characteristics, and electronic descriptors. Herein, we explored the potential of machine learning in predicting ionic conductivity in NASICON. We compile a database of 211 datasets, covering 160 NASICON materials, and employ facile descriptors, including synthesis parameters, test conditions, molecular and structural attributes, and electronic properties. Random forest (RF) and neural network (NN) models were developed and optimized, with NN performing notably better, particularly with limited data (R2=0.820). Our analysis spotlighted the pivotal role of Na stoichiometric count in ionic conductivity. Furthermore, the NN algorithm highlighted the comparable significance of synthesis parameters to structural factors in determining conductivity. In contrast, the impact of electronegativity on doped elements appears less significant, underscoring the importance of dopant size and quantity. This work underscores the potential of machine learning in advancing NASICON electrolyte design for sodium-ion batteries, offering insights into conductivity drivers and a more efficient path to optimizing materials.

Examining graph neural networks for crystal structures: Limitations and opportunities for capturing periodicity

Graph neural networks (GNNs) have recently been used to learn the representations of crystal structures through an end-to-end data-driven approach. However, a systematic top-down approach to evaluate and understand the limitations of GNNs in accurately capturing crystal structures has yet to be established. In this study, we introduce an approach using human-designed descriptors as a compendium of human knowledge to investigate the extent to which GNNs can comprehend crystal structures. Our findings reveal that current state-of-the-art GNNs fall short in accurately capturing the periodicity of crystal structures. We analyze this failure by exploring three aspects: local expressive power, long-range information processing, and readout function. To address these identified limitations, we propose a straightforward and general solution: the hybridization of descriptors with GNNs, which directly supplements the missing information to GNNs. The hybridization enhances the predictive accuracy of GNNs for specific material properties, most notably phonon internal energy and heat capacity, which heavily rely on the periodicity of materials.

Recent progress and strategic perspectives of inorganic solid electrolytes: fundamentals, modifications, and applications in sodium metal batteries

Solid-state electrolytes (SEs) have attracted overwhelming attention as a promising alternative to traditional organic liquid electrolytes (OLEs) for high-energy-density sodium-metal batteries (SMBs), owing to their intrinsic incombustibility, wider electrochemical stability window (ESW), and better thermal stability. Among various kinds of SEs, inorganic solid-state electrolytes (ISEs) stand out because of their high ionic conductivity, excellent oxidative stability, and good mechanical strength, rendering potential utilization in safe and dendrite-free SMBs at room temperature. However, the development of Na-ion ISEs still remains challenging, that a perfect solution has yet to be achieved. Herein, we provide a comprehensive and in-depth inspection of the state-of-the-art ISEs, aiming at revealing the underlying Na⁺ conduction mechanisms at different length scales, and interpreting their compatibility with the Na metal anode from multiple aspects. A thorough material screening will include nearly all ISEs developed to date, i.e., oxides, chalcogenides, halides, antiperovskites, and borohydrides, followed by an overview of the modification strategies for enhancing their ionic conductivity and interfacial compatibility with Na metal, including synthesis, doping and interfacial engineering. By discussing the remaining challenges in ISE research, we propose rational and strategic perspectives that can serve as guidelines for future development of desirable ISEs and practical implementation of high-performance SMBs.

AI for Nanomaterials Development in Clean Energy and Carbon Capture, Utilization and Storage (CCUS)

Zero-carbon energy and negative emission technologies are crucial for achieving a carbon neutral future, and nanomaterials have played critical roles in advancing such technologies. More recently, due to the explosive growth in data, the adoption and exploitation of artificial intelligence (AI) as part of the materials research framework have had a tremendous impact on the development of nanomaterials. AI has enabled revolutionary next-generation paradigms to significantly accelerate all stages of material discovery and facilitate the exploration of the enormous design space. In this review, we summarize recent advancements of AI applications in nanomaterials discovery, with a special emphasis on the selected applications of AI and nanotechnology for the net-zero emission future including the development of solar cells, hydrogen energy, battery materials for renewable energy, and CO₂ capture and conversion materials for carbon capture, utilization and storage (CCUS) technologies. In addition, we discuss the limitations and challenges of current AI applications in this area by identifying the gaps that exist in current development. Finally, we present the prospect for future research directions in order to facilitate the large-scale applications of artificial intelligence for advancements in nanomaterials.

Contrastive Metric Learning for Lithium Super-ionic Conductor Screening

High-performance Li-ion battery significantly impacts modern society, and materials with high conductivity play critical roles in battery development. Machine learning (ML) technologies have rapidly changed the field in recent years. However, it is still challenging to predict the high conductors directly due to the lack of validated conductor samples. This paper presents a succinct but effective metric-learning framework for high conductor screening. The material structures are mapped to an optimized feature space using a Siamese network, and an instance-based method is used to classify the input sample. The experiments demonstrate that the proposed method could effectively extract knowledge from imbalanced data and has good performance and generalization ability.

Machine learning for a sustainable energy future

Transitioning from fossil fuels to renewable energy sources is a critical global challenge; it demands advances - at the materials, devices and systems levels - for the efficient harvesting, storage, conversion and management of renewable energy. Energy researchers have begun to incorporate machine learning (ML) techniques to accelerate these advances. In this Perspective, we highlight recent advances in ML-driven energy research, outline current and future challenges, and describe what is required to make the best use of ML techniques. We introduce a set of key performance indicators with which to compare the benefits of different ML-accelerated workflows for energy research. We discuss and evaluate the latest advances in applying ML to the development of energy harvesting (photovoltaics), storage (batteries), conversion (electrocatalysis) and management (smart grids). Finally, we offer an overview of potential research areas in the energy field that stand to benefit further from the application of ML.

Adsorbate chemical environment-based machine learning framework for heterogeneous catalysis

Heterogeneous catalytic reactions are influenced by a subtle interplay of atomic-scale factors, ranging from the catalysts' local morphology to the presence of high adsorbate coverages. Describing such phenomena via computational models requires generation and analysis of a large space of atomic configurations. To address this challenge, we present Adsorbate Chemical Environment-based Graph Convolution Neural Network (ACE-GCN), a screening workflow that accounts for atomistic configurations comprising diverse adsorbates, binding locations, coordination environments, and substrate morphologies. Using this workflow, we develop catalyst surface models for two illustrative systems: (i) NO adsorbed on a Pt₃Sn(111) alloy surface, of interest for nitrate electroreduction processes, where high adsorbate coverages combined with low symmetry of the alloy substrate produce a large configurational space, and (ii) OH* adsorbed on a stepped Pt(221) facet, of relevance to the Oxygen Reduction Reaction, where configurational complexity results from the presence of irregular crystal surfaces, high adsorbate coverages, and directionally-dependent adsorbate-adsorbate interactions. In both cases, the ACE-GCN model, trained on a fraction (~10%) of the total DFT-relaxed configurations, successfully describes trends in the relative stabilities of unrelaxed atomic configurations sampled from a large configurational space. This approach is expected to accelerate development of rigorous descriptions of catalyst surfaces under in-situ conditions.

Calibrating DFT Formation Enthalpy Calculations by Multifidelity Machine Learning

The application of machine learning to predict materials properties measured by experiments are valuable yet difficult due to the limited amount of experimental data. In this work, we use a multifidelity random forest model to learn the experimental formation enthalpy of materials with prediction accuracy higher than the Perdew-Burke-Ernzerhof (PBE) functional with linear correction, PBEsol, and meta-generalized gradient approximation (meta-GGA) functionals (SCAN and r2SCAN), and it outperforms the hotly studied deep neural network-based representation learning and transfer learning. We then use the model to calibrate the DFT formation enthalpy in the Materials Project database and discover materials with underestimated stability. The multifidelity model is also used as a data-mining approach to find how DFT deviates from experiments by explaining the model output.

Data-Driven Materials Innovation and Applications

Owing to the rapid developments to improve the accuracy and efficiency of both experimental and computational investigative methodologies, the massive amounts of data generated have led the field of materials science into the fourth paradigm of data-driven scientific research. This transition requires the development of authoritative and up-to-date frameworks for data-driven approaches for material innovation. A critical discussion on the current advances in the data-driven discovery of materials with a focus on frameworks, machine-learning algorithms, material-specific databases, descriptors, and targeted applications in the field of inorganic materials is presented. Frameworks for rationalizing data-driven material innovation are described, and a critical review of essential subdisciplines is presented, including: i) advanced data-intensive strategies and machine-learning algorithms; ii) material databases and related tools and platforms for data generation and management; iii) commonly used molecular descriptors used in data-driven processes. Furthermore, an in-depth discussion on the broad applications of material innovation, such as energy conversion and storage, environmental decontamination, flexible electronics, optoelectronics, superconductors, metallic glasses, and magnetic materials, is provided. Finally, how these subdisciplines (with insights into the synergy of materials science, computational tools, and mathematics) support data-driven paradigms is outlined, and the opportunities and challenges in data-driven material innovation are highlighted.

Artificial intelligence inferred microstructural properties from voltage-capacity curves

The quantification of microstructural properties to optimize battery design and performance, to maintain product quality, or to track the degradation of LIBs remains expensive and slow when performed through currently used characterization approaches. In this paper, a convolution neural network-based deep learning approach (CNN) is reported to infer electrode microstructural properties from the inexpensive, easy to measure cell voltage versus capacity data. The developed framework combines two CNN models to balance the bias and variance of the overall predictions. As an example application, the method was demonstrated against porous electrode theory-generated voltage versus capacity plots. For the graphite|LiMn\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_2$$\end{document}2O\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_4$$\end{document}4 chemistry, each voltage curve was parameterized as a function of the cathode microstructure tortuosity and area density, delivering CNN predictions of Bruggeman’s exponent and shape factor with 0.97 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R^2$$\end{document}R2 score within 2 s each, enabling to distinguish between different types of particle morphologies, anisotropies, and particle alignments. The developed neural network model can readily accelerate the processing-properties-performance and degradation characteristics of the existing and emerging LIB chemistries.

Toward Bottom-Up Understanding of Transport in Concentrated Battery Electrolytes

Bottom-up understanding of transport describes how molecular changes alter species concentrations and electrolyte voltage drops in operating batteries. Such an understanding is essential to predictively design electrolytes for desired transport behavior. We herein advocate building a structure-property-performance relationship as a systematic approach to accurate bottom-up understanding. To ensure generalization across salt concentrations as well as different electrolyte types and cell configurations, the property-performance relation must be described using Newman's concentrated solution theory. It uses Stefan-Maxwell diffusivity, ij , to describe the role of molecular motions at the continuum scale. The key challenge is to connect ij to the structure. We discuss existing methods for making such a connection, their peculiarities, and future directions to advance our understanding of electrolyte transport.

Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery

Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels, reducing the impact of global warming, and providing solutions to environmental pollution. Improved processes for catalyst design and a better understanding of electro/photocatalytic processes are essential for improving catalyst effectiveness. Recent advances in data science and artificial intelligence have great potential to accelerate electrocatalysis and photocatalysis research, particularly the rapid exploration of large materials chemistry spaces through machine learning. Here a comprehensive introduction to, and critical review of, machine learning techniques used in electrocatalysis and photocatalysis research are provided. Sources of electro/photocatalyst data and current approaches to representing these materials by mathematical features are described, the most commonly used machine learning methods summarized, and the quality and utility of electro/photocatalyst models evaluated. Illustrations of how machine learning models are applied to novel electro/photocatalyst discovery and used to elucidate electrocatalytic or photocatalytic reaction mechanisms are provided. The review offers a guide for materials scientists on the selection of machine learning methods for electrocatalysis and photocatalysis research. The application of machine learning to catalysis science represents a paradigm shift in the way advanced, next-generation catalysts will be designed and synthesized.

Accelerating amorphous polymer electrolyte screening by learning to reduce errors in molecular dynamics simulated properties

Polymer electrolytes are promising candidates for the next generation lithium-ion battery technology. Large scale screening of polymer electrolytes is hindered by the significant cost of molecular dynamics (MD) simulation in amorphous systems: the amorphous structure of polymers requires multiple, repeated sampling to reduce noise and the slow relaxation requires long simulation time for convergence. Here, we accelerate the screening with a multi-task graph neural network that learns from a large amount of noisy, unconverged, short MD data and a small number of converged, long MD data. We achieve accurate predictions of 4 different converged properties and screen a space of 6247 polymers that is orders of magnitude larger than previous computational studies. Further, we extract several design principles for polymer electrolytes and provide an open dataset for the community. Our approach could be applicable to a broad class of material discovery problems that involve the simulation of complex, amorphous materials.

Screening polymer electrolytes for batteries is extremely expensive due to the complex structures and slow dynamics. Here the authors develop a machine learning scheme to accelerate the screening and explore a space much larger than past studies.

Machine Learning: An Advanced Platform for Materials Development and State Prediction in Lithium-Ion Batteries

Lithium-ion batteries (LIBs) are vital energy-storage devices in modern society. However, the performance and cost are still not satisfactory in terms of energy density, power density, cycle life, safety, etc. To further improve the performance of batteries, traditional "trial-and-error" processes require a vast number of tedious experiments. Computational chemistry and artificial intelligence (AI) can significantly accelerate the research and development of novel battery systems. Herein, a heterogeneous category of AI technology for predicting and discovering battery materials and estimating the state of the battery system is reviewed. Successful examples, the challenges of deploying AI in real-world scenarios, and an integrated framework are analyzed and outlined. The state-of-the-art research about the applications of ML in the property prediction and battery discovery, including electrolyte and electrode materials, are further summarized. Meanwhile, the prediction of battery states is also provided. Finally, various existing challenges and the framework to tackle the challenges on the further development of machine learning for rechargeable LIBs are proposed.

A critical review of the circular economy for lithium-ion batteries and photovoltaic modules - status, challenges, and opportunities

To meet net-zero emissions and cost targets for power production, recent analysis indicates that photovoltaic (PV) capacity in the United States could exceed 1 TW by 2050 alongside comparable levels of energy storage capacity, mostly from batteries. For comparison, the total U.S. utility-scale power capacity from all energy sources in 2020 was 1.2 TW, of which solar satisfied approximately 3%. With such massive scales of deployment, questions have arisen regarding issues of material supply for manufacturing, end-of-life management of technologies, environmental impacts across the life cycle, and economic costs to both individual consumers and society at large. A set of solutions to address these issues center on the development of a circular economy - shifting from a take-make-waste linear economic model to one that retains the value of materials and products as long as possible, recovering materials at end of life to recirculate back into the economy. With limited global experience, scholars and practitioners have begun to investigate circular economy pathways, focusing on applying novel technologies and analytical methods to fast-growing sectors like renewable energy. This critical review aims to synthesize the growing literature to identify key insights, gaps, and opportunities for research and implementation of a circular economy for two of the leading technologies that enable the transition to a renewable energy economy: solar PV and lithium-ion batteries (LIBs). We apply state-of-the-science systematic literature review procedures to critically analyze over 3,000 publications on the circular economy of solar PV and LIBs, categorizing those that pass a series of objective screens in ways that can illuminate the current state of the art, highlight existing impediments to a circular economy, and recommend future technological and analytical research. We conclude that while neither PV nor LIB industries have reached a circular economy, they are both on a path towards increased circularity. Based on our assessment of the state of current literature and scientific understanding, we recommend research move beyond its prior emphasis on recycling technology development to more comprehensively investigate other CE strategies, more holistically consider economic, environmental and policy aspects of CE strategies, increase leveraging of digital information systems that can support acceleration towards a CE, and to continue to study CE-related aspects of LIB and PV markets.

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.

Opportunities and challenges for 2D heterostructures in battery applications: a computational perspective

With an increasing demand for large-scale energy storage systems, there is a need for novel electrode materials to store energy in batteries efficiently. 2D materials are promising as electrode materials for battery applications. Despite their excellent properties, none of the available single-phase 2D materials offers a combination of properties required for maximizing energy density, power density, and cycle life. This article discusses how stacking distinct 2D materials into a 2D heterostructure may open up new possibilities for battery electrodes, combining favourable characteristics and overcoming the drawbacks of constituent 2D layers. Computational studies are crucial to advancing this field rapidly with first-principles simulations of various 2D heterostructures forming the basis for such investigations that offer insights into processes that are hard to determine otherwise. We present a perspective on the current methodology, along with a review of the known 2D heterostructures as anodes and their potential for Li and Na-ion battery applications. 2D heterostructures showcase excellent tunability with different compositions. However, each of them has distinct properties, with its own set of challenges and opportunities for application in batteries. We highlight the current status and prospects to stimulate research into designing new 2D heterostructures for battery applications.

Smart Materials Prediction: Applying Machine Learning to Lithium Solid-State Electrolyte

Traditionally, the discovery of new materials has often depended on scholars’ computational and experimental experience. The traditional trial-and-error methods require many resources and computing time. Due to new materials’ properties becoming more complex, it is difficult to predict and identify new materials only by general knowledge and experience. Material prediction tools based on machine learning (ML) have been successfully applied to various materials fields; they are beneficial for modeling and accelerating the prediction process for materials that cannot be accurately predicted. However, the obstacles of disciplinary span led to many scholars in materials not having complete knowledge of data-driven materials science methods. This paper provides an overview of the general process of ML applied to materials prediction and uses solid-state electrolytes (SSE) as an example. Recent approaches and specific applications to ML in the materials field and the requirements for building ML models for predicting lithium SSE are reviewed. Finally, some current obstacles to applying ML in materials prediction and prospects are described with the expectation that more materials scholars will be aware of the application of ML in materials prediction.

Machine Learning enabled High-Throughput Screening of Inorganic Solid Electrolytes for Regulating Dendritic Growth in Lithium Metal Anodes

The Li-S secondary battery system has gained popularity owing to their advantage of higher specific energy compared to the Li ion battery. However, it suffers majorly due to the Li...

Screening and Understanding Li Adsorption on Two-Dimensional Metallic Materials by Learning Physics and Physics-Simplified Learning

Understanding and broad screening Li interaction energetics with surfaces are key to the development of materials for a wide range of applications including Li-based electrochemical capacitors, Li sensors, Li separation membranes, and Li-ion batteries. In this work, we build a high-throughput screening scheme to screen Li adsorption energetics on 2D metallic materials. First, density functional theory and graph convolution networks are utilized to calculate the minimum Li adsorption energies for some 2D metallic materials. The data is then used to find a dependence of the minimum Li adsorption energies on the sum of ionization potential, work function of the 2D metal, and coupling energy between Li⁺ and substrate, and the dependence is used to screen all 2D metallic materials. Physics-simplified learning by splitting the property into different contributions and learning or calculating each component is shown to have higher accuracy and transferability for machine learning of complex materials properties.

First-principles materials simulation and design for alkali and alkaline metal ion batteries accelerated by machine learning

The challenge of regeneration of batteries requires a performance improvement in the alkali/alkaline metal ion battery (AMIB) materials, whereas the traditional research paradigm fully based on experiments and theoretical simulations needs massive research and development investment. During the last decade, machine learning (ML) has made breakthroughs in many complex disciplines, which testifies to their high processing speed and ability to capture relationships. Inspired by these achievements, ML has also been introduced to bring a new paradigm for shortening the development of AMIB materials. In this Perspective, the focus will be on how this new ML technology solves the key problems of redox potentials, ionic conductivity and stability parameters in first-principles materials' simulation and design for AMIBs. It is found that ML not only accelerates the property prediction, but also gives physicochemical insights into AMIB materials' design. In addition, the final part of this paper summarizes current achievements and looks forward to the progress of a novel paradigm in direct/inverse design with the increasing number of databases, skills, and ML technologies for AMIBs.

Artificial Intelligence Applied to Battery Research: Hype or Reality?

This is a critical review of artificial intelligence/machine learning (AI/ML) methods applied to battery research. It aims at providing a comprehensive, authoritative, and critical, yet easily understandable, review of general interest to the battery community. It addresses the concepts, approaches, tools, outcomes, and challenges of using AI/ML as an accelerator for the design and optimization of the next generation of batteries—a current hot topic. It intends to create both accessibility of these tools to the chemistry and electrochemical energy sciences communities and completeness in terms of the different battery R&D aspects covered.

Searching for Mechanically Superior Solid-State Electrolytes in Li-Ion Batteries via Data-Driven Approaches

Li-ion solid-state electrolytes (SSEs) have great potential, but their commercialization is limited due to interfacial contact stability issues and the formation and growth of dendrites. In this study, a machine learning regression algorithm was implemented to screen for mechanically superior SSEs among 17,619 candidates. Elasticity information (14,238 structures) was imported from an available database, and their machine learning descriptors were constructed using physiochemical and structural properties. A surrogate model for predicting the shear and bulk moduli exhibited R² values of 0.819 and 0.863, respectively. The constructed model was applied to predict the elastic properties of potential SSEs, and first-principles calculations were conducted for validation. Furthermore, the application of an active learning process, which reduced the prediction uncertainty, was clearly demonstrated to improve the R² score from approximately 0.6-0.8 by adding only 32-63% of new data sets depending on the type of modulus. We believe that the current model and additional data sets can accelerate the process of finding optimal SSEs to satisfy the mechanical conditions being sought.

Machine Learning Boosting the Development of Advanced Lithium Batteries

Lithium batteries (LBs) have many high demands regarding their application in portable electronic devices, electric vehicles, and smart grids. Machine learning (ML) can effectively accelerate the discovery of materials and predict their performances for LBs, which is thus able to markedly enhance the development of advanced LBs. In recent years, there have been many successful examples of using ML for advanced LBs. In this review, the basic procedure and representative methods of ML are briefly introduced to promote understanding of ML by experts in LBs. Then, the application of ML in developing LBs is highlighted for the purpose of attracting more attention to this field. Finally, the challenges and perspectives of ML are noted for the further development of LBs. It is hoped that this review can shed light on the application of ML in developing LBs and boost the development of advanced LBs.

Applying Machine Learning to Rechargeable Batteries: From the Microscale to the Macroscale

Emerging machine learning (ML) methods are widely applied in chemistry and materials science studies and have led to a focus on data-driven research. This Minireview summarizes the application of ML to rechargeable batteries, from the microscale to the macroscale. Specifically, ML offers a strategy to explore new functionals for density functional theory calculations and new potentials for molecular dynamics simulations, which are expected to significantly enhance the challenging descriptions of interfaces and amorphous structures. ML also possesses a great potential to mine and unveil valuable information from both experimental and theoretical datasets. A quantitative "structure-function" correlation can thus be established, which can be used to predict the ionic conductivity of solids as well as the battery lifespan. ML also exhibits great advantages in strategy optimization, such as fast-charge procedures. The future combination of multiscale simulations, experiments, and ML is also discussed and the role of humans in data-driven research is highlighted.

How Machine Learning Will Revolutionize Electrochemical Sciences

Electrochemical systems function via interconversion of electric charge and chemical species and represent promising technologies for our cleaner, more sustainable future. However, their development time is fundamentally limited by our ability to identify new materials and understand their electrochemical response. To shorten this time frame, we need to switch from the trial-and-error approach of finding useful materials to a more selective process by leveraging model predictions. Machine learning (ML) offers data-driven predictions and can be helpful. Herein we ask if ML can revolutionize the development cycle from decades to a few years. We outline the necessary characteristics of such ML implementations. Instead of enumerating various ML algorithms, we discuss scientific questions about the electrochemical systems to which ML can contribute.

Current and future lithium-ion battery manufacturing

Lithium-ion batteries (LIBs) have become one of the main energy storage solutions in modern society. The application fields and market share of LIBs have increased rapidly and continue to show a steady rising trend. The research on LIB materials has scored tremendous achievements. Many innovative materials have been adopted and commercialized by the industry. However, the research on LIB manufacturing falls behind. Many battery researchers may not know exactly how LIBs are being manufactured and how different steps impact the cost, energy consumption, and throughput, which prevents innovations in battery manufacturing. Here in this perspective paper, we introduce state-of-the-art manufacturing technology and analyze the cost, throughput, and energy consumption based on the production processes. We then review the research progress focusing on the high-cost, energy, and time-demand steps of LIB manufacturing. Finally, we share our views of challenges in LIB manufacturing and propose future development directions for manufacturing research in LIBs.

Essential structural and experimental descriptors for bulk and grain boundary conductivities of Li solid electrolytes

We present a computational approach for identifying the important descriptors of the ionic conductivities of lithium solid electrolytes. Our approach discriminates the factors of both bulk and grain boundary conductivities, which have been rarely reported. The effects of the interrelated structural (e.g. grain size, phase), material (e.g. Li ratio), chemical (e.g. electronegativity, polarizability) and experimental (e.g. sintering temperature, synthesis method) properties on the bulk and grain boundary conductivities are investigated via machine learning. The data are trained using the bulk and grain boundary conductivities of Li solid conductors at room temperature. The important descriptors are elucidated by their feature importance and predictive performances, as determined by a nonlinear XGBoost algorithm: (i) the experimental descriptors of sintering conditions are significant for both bulk and grain boundary, (ii) the material descriptors of Li site occupancy and Li ratio are the prior descriptors for bulk, (iii) the density and unit cell volume are the prior structural descriptors while the polarizability and electronegativity are the prior chemical descriptors for grain boundary, (iv) the grain size provides physical insights such as the thermodynamic condition and should be considered for determining grain boundary conductance in solid polycrystalline ionic conductors.

Design rules for liquid crystalline electrolytes for enabling dendrite-free lithium metal batteries

Dendrite-free electrodeposition of lithium metal is necessary for the adoption of high energy-density rechargeable lithium metal batteries. Here, we demonstrate a mechanism of using a liquid crystalline electrolyte to suppress dendrite growth with a lithium metal anode. A nematic liquid crystalline electrolyte modifies the kinetics of electrodeposition by introducing additional overpotential due to its bulk-distortion and anchoring free energy. By extending the phase-field model, we simulate the morphological evolution of the metal anode and explore the role of bulk-distortion and anchoring strengths on the electrodeposition process. We find that adsorption energy of liquid crystalline molecules on a lithium surface can be a good descriptor for the anchoring energy and obtain it using first-principles density functional theory calculations. Unlike other extrinsic mechanisms, we find that liquid crystals with high anchoring strengths can ensure smooth electrodeposition of lithium metal, thus paving the way for practical applications in rechargeable batteries based on metal anodes.

Progress on Lithium Dendrite Suppression Strategies from the Interior to Exterior by Hierarchical Structure Designs

Lithium (Li) metal is promising for high energy density batteries due to its low electrochemical potential (-3.04 V) and high specific capacity (3860 mAh g^-1 ). However, the safety issues impede the commercialization of Li anode batteries. In this work, research of hierarchical structure designs for Li anodes to suppress Li dendrite growth and alleviate volume expansion from the interior (by the 3D current collector and host matrix) to the exterior (by the artificial solid electrolyte interphase (SEI), protective layer, separator, and solid state electrolyte) is concluded. The basic principles for achieving Li dendrite and volume expansion free Li anode are summarized. Following these principles, 3D porous current collector and host matrix are designed to suppress the Li dendrite growth from the interior. Second, artificial SEI, the protective layer, and separator as well as solid-state electrolyte are constructed to regulate the distribution of current and control the Li nucleation and deposition homogeneously for suppressing the Li dendrite growth from exterior of Li anode. Ultimately, this work puts forward that it is significant to combine the Li dendrite suppression strategies from the interior to exterior by 3D hierarchical structure designs and Li metal modification to achieve excellent cycling and safety performance of Li metal batteries.

Shaping the Future of Solid-State Electrolytes through Computational Modeling

Advances and progress in computational research that aims to understand and improve solid-state electrolytes (SSEs) are outlined. One of the main challenges in the development of all-solid-state batteries is the design of new SSEs with high ion diffusivity that maintain chemical and phase stability and thereby provide a wide electrochemical stability window. Solving this problem requires a deep understanding of the diffusion mechanism and properties of the SSEs. A second important challenge is the development of an understanding of the interface between the SSE and the electrode. The role of molecular simulations and modeling in dealing with these challenges is discussed, with reference to examples in the literature. The methods used and issues considered in recent years are highlighted. Finally, a brief outlook about the future of modeling in studying solid-state battery technology is presented.

A review of composite solid-state electrolytes for lithium batteries: fundamentals, key materials and advanced structures

All-solid-state lithium ion batteries (ASSLBs) are considered next-generation devices for energy storage due to their advantages in safety and potentially high energy density.

Machine learning prediction of coordination energies for alkali group elements in battery electrolyte solvents

We combined a data science-driven method with quantum chemistry calculations, and applied it to the battery electrolyte problem. We performed quantum chemistry calculations on the coordination energy (Ecoord) of five alkali metal ions (Li, Na, K, Rb, and Cs) to electrolyte solvent, which is intimately related to ion transfer at the electrolyte/electrode interface. Three regression methods, namely, multiple linear regression (MLR), least absolute shrinkage and selection operator (LASSO), and exhaustive search with linear regression (ES-LiR), were employed to find the relationship between Ecoord and descriptors. Descriptors include both ion and solvent properties, such as the radius of metal ions or the atomic charge of solvent molecules. Our results clearly indicate that the ionic radius and atomic charge of the oxygen atom that is connected to the metal ion are the most important descriptors. Good prediction accuracy for Ecoord of 0.127 eV was obtained using ES-LiR, meaning that we can predict Ecoord for any alkali ion without performing quantum chemistry calculations for ion-solvent pairs. Further improvement in the prediction accuracy was made by applying the exhaustive search with Gaussian process, which yields 0.016 eV for the prediction accuracy of Ecoord.

Fundamentals of inorganic solid-state electrolytes for batteries

In the critical area of sustainable energy storage, solid-state batteries have attracted considerable attention due to their potential safety, energy-density and cycle-life benefits. This Review describes recent progress in the fundamental understanding of inorganic solid electrolytes, which lie at the heart of the solid-state battery concept, by addressing key issues in the areas of multiscale ion transport, electrochemical and mechanical properties, and current processing routes. The main electrolyte-related challenges for practical solid-state devices include utilization of metal anodes, stabilization of interfaces and the maintenance of physical contact, the solutions to which hinge on gaining greater knowledge of the underlying properties of solid electrolyte materials.