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# Toward Better and Smarter Batteries by Combining AI with Multisensory and Self-Healing Approaches

Tejs Vegge, Jean-marie Tarascon, Kristina Edström

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Tejs Vegge, Jean-marie Tarascon, Kristina Edström. Toward Better and Smarter Batteries by Combining AI with Multisensory and Self-Healing Approaches. Advanced Energy Materials, 2021, 11 (23), pp.2100362. 10.1002/aenm.202100362. hal-04104637

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# Toward Better and Smarter Batteries by Combining AI with Multisensory and Self-Healing Approaches

Tejs Vegge,\* Jean-Marie Tarascon,\* and Kristina Edström\*

With an exponentially growing demand for rechargeable batteries, the development of new ultra-performant, fully scalable, and sustainable battery technologies and materials must be accelerated. Creating a holistic, closed-loop infrastructure for materials discovery, manufacturing, and battery testing that utilizes a common data infrastructure and autonomous workflows to bridge big data from all domains of the battery value chain, can pave the way for a transformative reduction in the required time to discovery. By embedding multisensory and self-healing capabilities in future battery technologies and integrating these with AI and physics-aware machine learning models capable of predicting the spatio-temporal evolution of battery materials and interfaces, it will, in time, be possible to identify, predict and prevent potential degradation and failure modes. This will facilitate enhanced battery quality, reliability, and life, for example, by preemptively changing the battery charging conditions or releasing self-healing additives from the separator membrane, akin to preemptive medicine, and form the basis for inverse design of new battery materials, interfaces, and additives. The large-scale and long-term European research initiative BATTERY 2030+ seeks to make this longer-than ten-year vision a reality through the development of a versatile and chemistry neutral “Battery Interface Genome—Materials Acceleration Platform” infrastructure (BIG-MAP).

Prof. T. Vegge
Department of Energy Conversion and Storage
Anker Engelunds Vej
Building 301, Kgs Lyngby DK-2800, Denmark
E-mail: teve@dtu.dk

Prof. J.-M. Tarascon
Chimie du Solide-Energie, UMR 8260
Collège de France
Paris 75231, France
E-mail: Jean-marie.tarascon@college-de-france.fr

Prof. J.-M. Tarascon
Réseau sur le Stockage Electrochimique de l'Energie (RS2E)
Paris 3459, France

Prof. K. Edström
Department of Chemistry
Angstrom Laboratory
Box 538, Uppsala 751 21, Sweden
E-mail: Kristina.Edstrom@kemi.uu.se

The ORCID identification number(s) for the author(s) of this article can be found under https://doi.org/10.1002/aenm.202100362.

© 2021 The Authors. Advanced Energy Materials published by Wiley-VCH GmbH. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

DOI: 10.1002/aenm.202100362

## 1. Introduction

The concurrent transformations of the automotive sector toward e-mobility and the applicability of data-driven approaches in science and energy technology,[1] provides a synergistic opportunity to accelerate the battery discovery and manufacturing processes and to optimize the performance and lifetime of battery cells.[2]

As new high-performance battery materials, chemistries, and cell designs emerge to compete with existing Li-ion batteries,[3] they face a common challenge in controlling the complex dynamic processes occurring at battery interfaces, which span a multitude of time- and length scales.[4] Developing a versatile and chemistry neutral infrastructure that is capable of monitoring, predicting, and controlling the dynamic properties and evolution of these interfaces and interphases like the solid-electrolyte interphase (SEI), is a cornerstone of the long-term roadmap of the large-scale European initiative BATTERY 2030+[5] and the BIG-MAP project in particular.

While the battery development process has historically been limited by a sequential and time-consuming discovery pipeline, the maturation of automated computer simulations at the atomic[6,7] to micro[8] and mesostructural[9] scale, data-based[10] and data-driven[11] approaches to predict materials properties yield new opportunities to accelerate the discovery of battery materials and cell designs.[12] The development of generic procedures for automated translation of information across scales from the atomic and microstructural to the cell level is, however, non-trivial. In the current scheme of multi-scaling, scale bridging from the atomic scale to cell level happens through parameter passing from lower level models to longer length scale models.[13] While recent examples bridge, for example, density functional theory (DFT) with cluster expansion[14] and phase field models to predict microstructures without empirical parameters,[15] extending these with machine learning (ML) approaches like long short-term memory to predict the time evolution of the microstructure, makes it possible to establish complex processing–microstructure–performance relationships.[16]

By efficient acquisition and utilization of the huge quantities of battery data generated daily in academia, industry, and by end-users like EV owners, it will not only be possible to enhance the battery performance and lifetime to realize
<figure><img src="image_4.png" /><figcaption>**Figure 1.** The BIG-MAP approach to closed-loop discovery of battery materials, interfaces, and cells using autonomous workflows and multi-sourced and multi-fidelity data from all parts of the battery value chain.</figcaption></figure>

million-mile batteries,[17] for example, through closed-loop optimization (Figure 1) of the charging procedures using ML-based early-prediction models,[2,18,19] but also to inverse design superior battery materials, interfaces, and cell designs by combining the modeling with data from high-throughput experiments and electrochemical testing. Central to this is establishing a framework that is capable encoding and decoding the limiting processes at the interfaces, in BATTERY 2030+ termed the “Battery Interface Genome (BIG)”, and the development of deep learning (DL) models capable of predicting the spatio-temporal evolution of the battery materials and interfaces.[20]

Recent advances in DL to treat systems of comparable complexity, for example, seasonal forecasts of drought based on geospatial data[21] or complex structure prediction, is well captured by DeepMind's AlphaFold 2 outclassing the competitors at CASP14 and reaching experimental accuracy in the prediction of ternary protein structures from the sequence of amino acids alone.[22] Achieving such a feat for battery materials, interfaces, and systems requires a combination of sophisticated models, novel operando characterization techniques, which are challenging to develop due to the complex structure of the details of a battery cells, and access to huge quantities of multi-sourced and multi-fidelity data spanning time- and length scales from electron transfer processes to battery aging. DL opens the door for accelerated approaches to automated multi-scaling. Hierarchical generative latent space models[23] like deep hierarchical variational autoencoders[24] have been used successfully in other fields, but the transferability to battery materials and interfaces requires new methods capable of learning a mapping of the multi-scale battery interface dynamics into multi-resolution hierarchically coupled regularized latent spaces that each encode for structures at different length scales. Such models should be integrated with suitable time evolution predictors in the extended latent space to yield a complete dynamics simulator and need to be validated against experimental and computational data from the battery electrolyte, for example, diffusion processes, and interface/boundary dynamics at different length and time scales. Here, a full integration of multiscale simulations,[16,25] multimodal experimental techniques,[26] multisensory,[27] and self-healing[28] approaches will form the basis for future autonomous inverse design and discovery.

## 2. A Holistic Infrastructure for Autonomous Battery Discovery

A critical bottleneck in the development of fully autonomous platforms[12] for accelerated discovery and development of battery materials, interfaces, and cells is establishing a holistic data infrastructure based on FAIR (findable, accessible, interoperable and reusable) data[29] from all parts of the battery value chain. The infrastructure should be unified by a community-wide battery ontology, that is, a shared knowledge-based representation across all relevant scales, techniques, and fields, capable of converting simulations and real-life observations to a common digital representation. BIG-MAP is developing an battery ontology, BattINFO,[30] which is based upon the European Materials Modelling Ontology community standard[31] and initiatives from other parts of the discovery cycle, for example, materials syntheses.[32] This will help ensure interoperability and be facilitated by open data archives and platforms such as the Materials Cloud[33] that allow users to store, explore, and share data in curated and well-defined formats that can be accessed from other platforms and for multiple purposes. By combining this with standardized and unified experimental protocols for battery testing and characterization that are currently under development, for example, in BATTERY 2030+, Batteries Europe and leading scientific journals in the field like Journal of Power Sources and Joule, it will be possible to develop autonomous workflows that can acquisition and utilize directly comparable and interoperable experimental and computational data. By building upon the experience from existing computational software packages like the Atomic Simulation Environment (ASE)[34] and workflow management tools like FireWorks,[35] AiiDA,[36] or MyQueue,[34] it can become possible to include experimental data with full provenance to accelerate the search for new battery materials.[7] These tools will be made available to the battery community through the BIG-MAP App Store[37] and GitHub registry,[38] and should ultimately facilitate experiments by autonomously launching simulations and simulations initiating and running experiments through the BIG-MAP orchestration infrastructure (Figure 2).

A second critical aspect is the development and implementation of ML and DL models that can efficiently acquisition and utilize the petabytes of multi-sourced and multi-fidelity[4] training
<figure><img src="image_5.png" /><figcaption>**Figure 2.** The data and information flow diagram for autonomous AI-orchestrated battery discovery and development in BIG-MAP and BATTERY 2030+. Reproduced with permission.[5] Copyright 2020, BATTERY 2030+ Roadmap.</figcaption></figure>

data that will become available to reach predictive accuracy for spatio-temporal models of battery interphases.[20] While high-fidelity data from, for example, synchrotron experiments and coupled cluster or Quantum Monte Carlo calculations, has historically been prohibitively expensive, recent DL models like PauliNet[39] and FermiNet[40] have made these accessible to more complicated molecules, and in time hopefully also to battery materials.

The catalysis field has lead the way in identification and use of simple descriptors, scaling relations,[41] and linear/Gaussian regression models.[42-44] Recent advances using ML-based approaches like sure independence screening and sparsifying operator has led to the development of more reliable microkinetic models that can capture a more comprehensive mechanistic understanding.[45]

Important developments are also made for ML potentials capable of describing the spatio-temporal evolution of reactive liquid-solid/amorphous systems with an unprecedented number of atoms and timespan of simulations. Recent work by Deringer et al., using GAP molecular-dynamics simulations on 100 000 silicon atoms were capable of accessing the time scales needed for prediction of distinct electronic features that can be compared directly to ultrafast spectroscopic techniques, and the experimentally relevant length scales for description of (poly-)crystallization in amorphous silicon.[46] Applying such approaches to describe battery materials and interfaces,[47] will enable a direct comparison with experimental operando techniques revealing interfacial phenomena and establish structure-property relations that are out of reach to existing methods.

The reaction complexity at battery interfaces is immensely higher than those encountered during catalytic small molecule conversion. Thus, we expect that more complex models will be needed to ensure predictive accuracy. While specific examples from battery interfaces remain scarce, Persson et al. have recent developed an elegant approach for predicting chemical reaction networks (CRN) for SEI formation in Li-ion.[48] Using a graph-based representation, they autonomously identify both previously proposed mechanisms as well as multiple novel pathways, also containing counter-intuitive reactions.

The developed models also need to be both physics and uncertainty-aware in order to achieve predictive accuracy in the treatment of (electro-)chemical reactions and reaction networks.[49] They should account for possible anthropogenic biases in the training data,[50] for example, for active learning purposes,[51] and balance the exploitation and exploration of computational and experimental space optimally. The sparsity and noise variability in the training data necessitates uncertainty quantification and propagation methods to be integrated within such models to capture predictive uncertainties. While uncertainty quantification of ground state structures can be done using large exiting data sets with, for example, ensemble models,[51] the scarcity of high-fidelity training data for transition state structures[52] makes uncertainty quantification for transition states particularly important in the pursuit of predictive models for (electro-)CRN and transport kinetics governing the formation and evolution of complex interphases like the SEI. Deep generative models can pave the way for inverse design of battery materials and additives[53] to design and create ultra-performant battery interfaces. Ultimately, extracting the physics- and uncertainty-awareness, for example, with explainable AI (XAI),[54] may even expand the intuition of the battery researchers and show new ways to explain what governs complex phenomena like dissolution and solubility.

Going forward, we envision multiple DL-pathways to expedite innovation in battery interfaces, for example, reinforcement learning based chemical space exploration[55] to design more effective additives such as scavangers and SEI formers, and inverse interface design based on features in deep learned electronic charge density at longer length scales.[56] As graph based or other deep models gain popularity for interfacial chemistry in a similar way as for bulk materials, XAI methods should take a center stage to provide interpretation from black box models that otherwise do not provide physical insights. Just as approaches have been successful for simpler models like decision trees and random forests applied to molecular systems.[54] Latest developments in deep graph model explainability[57] can be used to establish atomic
scale structure-property relationships[58] and further extended to interfacial properties.

A third and equally critical step is the translation of the model predictions to implementable procedures for autonomous orchestration of the materials synthesis robotics[59] and manufacturing processes. While recent examples, for example, from Venkat Viswanathan et al. who have demonstrated the ability to autonomous direct the testing of 140 electrolyte formulas and yielding non-intuitive optimum,[60] have shown a path forward, the complexity of the challenges facing BIG-MAP and BATTERY 2030+ will also depend on transformative breakthroughs in a number of other additional areas.

### 3. Synthesizability, Manufacturability, and Sustainability by Design

The ability to perform reliable predictions of the probability of synthesizability of organic and inorganic materials, say novel battery electrode materials, and interfaces/interphases, plays a central role in the ability to accelerate the discovery process by elimination of redundant and time consuming synthesis steps. Sharing FAIR data from failed syntheses and experiments[61] can therefore play an important role and infrastructural tools that facilitates and incentivise this sharing in an easy way are vital.[62,63]

Automation of organic synthesis and retrosynthesis is to suppress leading the way with, for example, “The Machine” from Marty Burke,[64–66] “The Chemputer” from Lee Cronin[67–69] and the mobile robotic chemist from Andy Cooper,[59] while autonomous synthesis of inorganic energy materials have been limited to a few examples, primarily within metal organic frameworks,[62,70,71] nanoparticles,[72,73] and thin films.[74] Examples like the mobile robot chemist and the Chemputer effectively demonstrate the perspectives of what can be achieved by combing ML-informed experimental design with autonomous synthesis robots. The robotized synthesis operations can be performed faster and be more reproducible, and can in principle be conducted 24–7. Even more effectful is the combination with ML-based decision making and analysis for optimization of the exploration and exploitation of, for example, complex CRN to predict the relative reactivity of the multitude of possible chemical reactions.[75]

Unraveling a generalizable phase and structural stability correlation for inorganic battery materials to the point of predicting synthesizability, manufacturability, and structure-property relations goes beyond a purely thermodynamic analysis, and poses a massive challenge. Recent examples from Chris Wolverton et al. utilizing complex network theory, high-throughput simulations, and experimental discovery times, illustrate a possible way forward toward predicting the likelihood of successful synthesis.[76,77] Such to suppress related approaches, for example, using continuous representation learning of materials for new discoveries using a latent space representation of materials, provide important stepping stones for inverse materials design.[78]

An even greater challenge is the development of approaches capable of encompassing all of the aspects above to enable prediction of the spatio-temporal evolution of organic-inorganic interfaces/interphases inside the battery cell, which should

form the basis for inverse design of battery interfaces across different materials and chemistries. Future work on the development of protective coatings and electrolyte formulations should therefore explicitly include the interactions with the electrode surface as well as the solvent, as recently demonstrated by Berend Smit et al. using Pareto active learning to investigate polymeric dispersants.[79]

Additional complexity will also arise from the desire to integrate manufacturability directly in the discovery process, that is, “manufacturability by design,” where, for example, the strength and ductility of a given electrode composition and/or coating is Pareto-optimized[79] for performance and manufacturability. As a gedankenexperiment, one could imagine developing a magnesium-alloy electrode which is sufficiently ductile and strong[80,81] to match a specific cell design like the Tesla 4680 battery cell,[82] without compromising the quality, reliability, and life (QRL).

This would require efficient utilization of multi-sourced data streams and hinges on data schema that are capable of handling multi-vector data types. As one such example, Franco et al have developed a hybrid workflow that integrates modeling and experimental data at the mesoscale in a data-driven stochastic electrode mesostructure generator algorithm that is used to predict process variables for calendaring[9] and for optimization of manufacturing processes.[83]

Another recent example is a multi-data-stream workflow for quantitative model extraction and prediction by Chueh et al. integrating X-ray microscopy, operando XRD, and electrochemical data into a Bayesian model selection method. By this approach, they unravel a “fictitious phase separation in Li layered oxides” and establish a general need for rate-dependent stability criteria.[84]

Finally, sustainability and recyclability by design would require the developed infrastructure and the predictive models to include aspects like materials processing demands and cost, LCA, and recyclability of critical raw materials (CRM)[85] or even component-level recycling directly in the discovery process. These aspects will be addressed in depth in the following stages of BATTERY 2030+.

### 4. Controlling the Interface Dynamics

Understanding, predicting, and controlling the dynamic evolution of battery interfaces remains a grand challenge that spans essentially all existing and future battery chemistries.[4]

Existing and future batteries are—expectedly—predominantly based on insertion electrodes where the active ions are mobile in a host structure. This means that the battery and all its components constantly are in a dynamic situation, where all interfaces are influenced. These changes need to be accommodated to allow the active particles to expand and shrink without cracking and losing contact with each other and the other components. If this very dynamic reaction scheme is not handled during the manufacturing of the battery, it can seriously influence the battery performance. In the case of lithium-ion batteries, the SEI on graphite[86,87] consumes lithium ions, but simultaneously it has a positive effect on preventing co-intercalation of the solvent molecules from the electrolyte to exfoliate the graphite.[88]
Classically, it is the electrode/electrolyte interfaces that have attracted the most research interest experimentally. This is mainly due to the lack of operando methods to follow its formation process and its evolution during battery cycling. The same holds for the cathode electrolyte interface, which is formed due to corrosion processes while thermodynamically unstable electrolytes explain this is the case for negative electrodes.[89] Intensive trial-and-error studies has been devoted to test different organic solvents, salts,[90] and additives within the electrolyte,[86,91] different coatings on the electrode particle surfaces, redox shuttle molecules, etc. All with the ambition to shape artificial interfaces able to give the battery better QRL.

When mastered, interfacial reactivity can also help extend the thermodynamic and kinetic stability of organic electrolytes used in batteries, while when it is not controlled, continuous parasitic reactions may occur, thus limiting the cycle life of the battery. The true reactions and changes in interface morphology and composition can be difficult to trace with experimental methods alone and calls for multi-domain initiatives like BIG-MAP.

Our current understanding relies mainly on post mortem studies using techniques, such as: FTIR,[92] AFM,[93] NMR,[94] XPS/HAXPES,[95] TEM,[96] and TOF-SIMS.[97] All these methods are now pushing toward in situ and operando operations under more realistic conditions (for example ambient pressure XPS),[98] but the cell designs to perform these experiments are still specially made to fit the specific instruments and far from mimicking a real commercial cell. Thanks to the development of powerful synchrotron facilities new and further refined spectroscopic techniques are coming into play with exceptionally high resolution to probe such buried interfaces.

A complete and closed form mathematical description of the whole CRN including the interfaces is enormously challenging, since coupled ionic and electronic transfer reactions in electrochemical relevant environment include usually coupled multi-step reactions.[99,100] Often, these multistep reactions are either tremendously simplified or reaction steps are modeled in ideal environments. In specific cases, it will be possible to combine, for example, DFT level simulations[101] with multisensory data and classical approaches to improve the description of surface reactions, but generic approaches remain limited and an efficient and systematic coupling is still lacking.

The development of BIG type insight is thus critical, linking high-throughput experiments with modeling to address the most important challenges for future battery chemistries: the battery interfaces.

**5. Multisensory Functionalities**

Increasing the battery QRL calls for accurate multisensory monitoring of the batteries functional status at the interfaces, cell and system level.[27] Whatever the battery technology considered, the performance depends on various and intertwined parameters: Electronic and ionic transports processes, phase transformations, and the nature and dynamics of the interfaces as described above. All of these chemical/physical aspects are further affected by the real working conditions of the batteries, which are pushed to theirs limits to meet the growing user's

demands. Thus, the absence of real-time monitoring information to explain the complexity is a bottleneck in the development of more efficient Battery Management Systems (BMS). Today, a BMS typically monitors parameters such as voltage, current, and temperature at the system level to estimate battery QRL and safety. Although monitoring temperature is essential to enhance cycle life and the longevity of the battery, it is not directly measured today at the cell level for EV applications. Moreover, the temperature inside the cell and its large gradient across terminals under specific working conditions are ignored; typically leaving battery packs over-engineered to minimize the risk of premature failure and this by sacrificing more than 20% of the true cell capacity.[102] Fundamental scientific insights are sorely needed to improve the BMS, by accurately monitoring the functional status of battery systems under real working conditions. To probe the success of the sensoring technologies, sophisticated diagnostic tools are a necessity.

Usual approaches to monitor batteries in real field conditions encompass thermocouples, thermistors, pressure gauges,[103] and acoustic sensors[104] placed on the cell surface rather than in its inside, together with the development of on-board electrochemical impedance spectroscopy devices.[105,106]

To contour such a limitation, today's disruptive vision consists of injecting smart functionalities into the battery via the integration and development of various sensing technologies to transmit information in and out of the cell (Figure 3).[94]

Sensors that can measure, with high sensitivity (fidelity) and resolution, changes in multiple parameters, such as chemical composition, strain, temperature, and pressure, and this at various locations/components within the cell (spatially resolved monitoring) are mandatory.

Great hope has been placed over the last 5–6 years in optical sensing since optical fibers, for example, Fiber Bragg Grating (FBG) sensors, provide several advantages relevant to batteries such as low invasiveness owing to their tiny dimensions ($Ø < 200 \ \mu m$), immunity to electromagnetic radiation, resistance to corrosion while being electronic insulators.[102,107–110] FBGs simply operate by correlating the wavelength of the reflected optical signal with local temperature ($T$), pressure ($P$), and strain ($\epsilon$), which are essential parameters to improve battery monitoring. Using single fiber and the feasibility to multiplex large numbers of FBGS over a short distance, researchers succeed in decoupling thermal from strain and pressure effects to achieve inside cell temperature imaging. Moreover, using the birefringence property of the fiber, Li-dimensional stresses gradients upon cycling could be detected in electrode materials.[111] Besides, correlations between these observables and battery events, including short-circuit, state of charge, and state of health estimations were established. Importantly, by converting thermal events into heat via simple mathematics,[27] researchers have recently demonstrated the feasibility to evaluate the SEI heat of formation as an additional metric, hence getting insights into the cascade reactions involved in the SEI formation during charging. Recently, FBGs were used to conduct optical calorimetry providing access to many thermodynamic parameters pertaining to the battery functioning such as entropy, enthalpy, or more importantly the accumulated heat capacity that is not accessible by isothermal calorimetry.[27] Collectively, these findings have offered a scalable solution for screening electrolyte
<figure><img src="image_6.png" /><figcaption>**Figure 3.** Tracking chemical and thermal events within a battery via FBG's; a) schematic of the SEI interphase; b) the cross section SEM images of SMF and MOF fibers within; c) the ways that the fibers are injected into a 18 650 cell; d) positioning of the fibers for conducting optical calorimetry; e) typical data showing the variation of DT and DP upon cycling from the monitoring of the wavelength shift during the first cycle (left) and second cycle (right). The heat of formation is deduced from thermal events via simple mathematics.</figcaption></figure>

additives and designing battery thermal management systems with enhanced safety.

Although FBGs provide a wide number of battery metrics (T, P, Cp, etc.), they fall short in identifying the nature of the parasitic organic species underlying chemical events or spotting changes in electrolyte composition.[27] To address this issue, another type of optical sensors have been chosen that rely on the interaction of the light travelling along the core fiber with its outside environment via evanescent waves developing within the cladding zone. Those sensors are titled-fiber Bragg gratings sensors (TFBGs)[112] or long-period fiber gratings sensors (LPFGs).[113] They can monitor the variation of the refractive index of the electrolyte medium surrounding the fiber that depends on the salt concentration. This enables tracking both Li inventory and solvent concentration.

A more straightforward approach to interrogate the cell chemistry consists of using unsheathed optical fibers (direct contact between the fiber core and electrode) to simply measure the attenuation of the optical signal by transmission (a technique known as FOEWS).[114] Here, IR makes it possible to identify the nature and composition of chemical species in electrolytes with the sine qua non condition that the optical fiber must transmit light in the mid-infrared range. This is why chalcogenide fibers rather than silica are needed. Plasmonic detection via the deposition of a gold nanometric layer on either a titled Bragg grating[115] or on unsheathed fibers is also intensively pursued to track Li inventory into the electrolyte or Li content into the active electrode material when the fiber is embedded into the electrode. Lastly, the introduction of thermo-fluorescence probes with optical read-out can be envisioned to monitor independently temperature and Li concentration, while other examples include polymers rather than silica fibers that present advantages for pressure detection, pH monitoring while being more adapted to aqueous battery systems.

In short, optical multisensory techniques open a new era for battery research that is solely limited by our imagination and can play a central role in accelerating the battery discovery process through integration with BIG-MAP. The setting of a multi-sensor integration platform that fuses the established models and state indicators algorithms developed via AI is essential for making the concept of battery digital twins a reality. Several authors have recently suggested the development of battery digital twins in both the manufacturing domain and at the BMS level[116,117] and below we discuss how such approaches can be fused with the models presented.

## 6. Preemptive Self-Healing

Smart batteries capable of performing preemptive self-healing can become a reality by integration of generative DL BIG-MAP models into the BMS ("BIG-BMS") and utilization of the multisensory approaches outlined above.[28] Akin to medicine, which relies heavily on the vectorization of drugs for the treatment of diseases, it will be essential to develop a tooling within the battery for on-demand administration of molecules that can solubilize a resistive deposit (as the SEI layer) or inject self-healing functions to restore or prevent a faulty electrode within the battery. This constitutes another transformational change within the battery community.

Equipping batteries with self-repairing systems, in order to improve their lifespan and reliability, is a field of experimentation that starts to enrich the literature, benefiting from the general strategies and formalisms well established for human bodies. Following nature's strategy relying on the use of sacrificial weak bonds for self-repairing, battery scientists have developed molecules or polymers with intrinsic self-healing properties based on dynamic supramolecular assembly such
as hydrogen bonding, electrostatic crosslinking, host-guest, or
van der Waals interactions.[118–120] Functionalized and flexible
polymers having chemical compatibility with the battery com-
ponents were developed with reactive species being produced
in the material when damage occurs.[121] Another self-healing
approach, barely applied until now within the battery com-
munity, is to use microcapsules hosting healing species that
need to be triggered by an external stimulus.[122] A plethora of
self-assembling materials and bio-inspired mechanisms per-
taining to the field of supramolecular chemistry and biology
have equally been tried to exploit radically new smart func-
tionalities enlisting either intrinsic or extrinsic self-healing
processes.[28] For illustration, we highlight the pioneering work
of Choi et al.[123] who used the host/guest interaction between
cyclodextrin and a polymer chain to develop polymeric binders
with supramolecular sliding-link architectures to give them
elastic properties, hence enabling the dissipation of volume
constraints. Equipped with such binders, the performance of
Si electrodes, which undergo volume fluctuations of more than
300% during the insertion and removal of Li, have been drasti-
cally improved, achieving over 1000 cycles. Besides electrodes,
separators, which are today a dead component of the battery
capacity, could serve as a toolbox to host the machinery and to
explore the on-demand administration of healing agents. These
healing agents could be micelles/capsules or polymers capable
of receiving specific molecules and releasing them on-demand
by physical or chemical stimuli to repair the “tissue” that con-
stitutes the electrode/electrolyte or particle/particle interface
provide they are highly resistant against the harsh oxidizing/
reducing chemical environment of the cell.[124]

Self-healing is another fasycinating field that is wide open
for innovation that will greatly benefit for transdisciplinary
approaches enlisting chemists and biologists together with doc-
tors with the ultimate hope to realize at long term in vivo sur-
gical intervention in batteries to enhance their lifespan.

Both sensing and self-healing functionalities are intimately
linked with the ultimate vision in BATTERY 2030+ of creating
smart batteries that intimately integrates these functionalities
with the BIG-models to be integrated in the BMS. The detected
signals from the sensors will be sent to the BIG-BMS and ana-
lyzed. In case of problems or predictions of likely future fail-
ures, the BIG-BMS, will emit a signal to the actuator to trigger
the stimulus of the self-healing process. Time will tell us if such
a vision is realistic, but the pressing needs for bringing QRL to
its maximum is worth the risk.

7. Perspectives

A holistic modular and chemistry neutral approach, integrating all
the critical elements outlined above: BIG-MAP, operando interface
monitoring, multisensory techniques, and self-healing, coupled
with high-throughput syntheses experiments, manufacturability
and recyclability by design, will enable accelerated discovery and
development of next generation of ultra-high performant and
smart batteries. Irrespectively, concentrating the research
and industrial development on "the nearest in time chemistries"
foreseen, as formulated in the European SET-Plan action 7[125]
for the transport sector, or other battery chemistries further away
in the development expectations,[3] a closed-loop discovery cycle

dependent on the fast development of data-driven and data-
centric tools and approaches including autonomous synthesis
robotics[59] and faster and more accurate high-throughput char-
acterization methods will be a prerequisite. In BATTERY 2030+
and BIG-MAP, the focus on decomposing, controlling, and engi-
neering the interfaces in different batteries have a special atten-
tion. Many years of Edisonian trial-and-error based research
have, however, taught the need to use not simple electrolytes, but
those where several different additives working in synergy are
needed to design and develop an efficient SEI.

To describe these scientific needs a collective and
community-wide effort is needed, due to the massive com-
plexity. Accelerated development of such SEIs would thus
greatly benefit from high-throughput techniques and the
AI-orchestrated rationalization and discovery outlined here. To
provide valuable insights into the spatio-temporal evolution of
critical interfaces and interphases, ensuring interoperability
and the coupling of scales is necessary. Usually, the complexity
of electrochemical systems forces a simplification of computer
simulations such that they only qualitatively mimic the phys-
ical reality in the battery. Therefore, even if the proper theory
for performing the necessary statistical averages are derived,
the obtained parameters/descriptors[126] may deviate consider-
ably from the parameters of the materials in their more com-
plicated electrochemical environment.[127] A coupling with the
physics- and uncertainty-aware data-driven methods trained on
a combination of multi-sourced experimental and simulational
data will strongly enhance the quality of the determination of
interface descriptors, features, and parameters, by enriching
the physical simulation with validated correlations between
idealized physics/chemistry based simulations with data of real
materials.

Coupled to and benefiting from the aforementioned, a more
general look into the future, foresees significant efforts in the
direction of manufacturing and recycling “by design.” We
must rethink present-day multistep processes and promote the
development of innovative, simple, and inexpensive one-step
processes to allow the reuse or direct recovery of electrodes
or active materials. It would be even more transformative to
rethink the configuration of prismatic and spiral batteries
where each battery component can be recycled independently.

BATTERY 2030+ has in its ambitious roadmap[5] a targeted
plan for short term, medium term, and long-term efforts that
now need to be implemented starting with the BIG-MAP project.
BATTERY 2030+ will, in the end, provide a closed-loop discovery
platform and process to accelerate a deeper knowledge on inter-
faces and accelerate the pace with which new, more sustainable,
and safer batteries with long-life and higher energy and/or power
efficiencies at a low cost will be developed. Those results will ben-
efit to our society in transforming it to a healthy environment.

Acknowledgements

The authors acknowledge Dr. Arghya Bhowmik for valuable discussions and funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 957189 (BIG-MAP) and No 957213 (BATTERY2030PLUS). T.V. also acknowledges the Novo Nordisk Foundation (SURE #NNF19OC0057822) and K.E. acknowledges the Knut and Alice Wallenberg Foundation (INTELL 139501042) and the Swedish Foundation for Strategic Research (SSF).
## Conflict of Interest

The authors declare no conflict of interest.

## Keywords

Al, batteries, interfaces, multisensory, self-healing

Received: January 31, 2021

Revised: March 8, 2021

Published online: April 30, 2021

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**Tejs Vegge** is professor and head of section for Atomic Scale Materials Modelling at the Technical University of Denmark, working on the development of autonomous methodologies for accelerating materials discovery and innovation processes. He is an elected member of the Academy of Technical Sciences (ATV) and the Danish Government's Commission on green transportation and has published more than 150 papers in the field of computational design of advanced battery materials and next-generation clean energy storage solutions. He serves as coordinator of the Battery Interface Genome—Materials Acceleration Platform (BIG-MAP) project and as member of the executive board of BATTERY2030+.

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**Jean-Marie Tarascon** is professor at the College de France holding the chair "Chemistry of solids—Energy), but much of his early career was spent in USA where he discovered the plastic Li-ion technology. Back to France in 1995, he created the European network of excellence ALISTORE-ERI and more recently the French network on electrochemical energy storage (RS2E). Tarascon's present research is devoted to battery materials/electrolytes, novel reactivity concepts, chemistries beyond Li, and sensing. He is the author of ≈700 scientific papers, detains ≈100 patents and received many honors, with the last ones being the 2020's Balzan prize.

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**Kristina Edström**, professor of Inorganic Chemistry at Uppsala University Sweden coordinates the large-scale European research initiative BATTERY 2030+. She studies Li-ion batteries, Na-ion batteries, solid-state batteries, and other future battery chemistries. She leads the Ångström Advanced Battery Centre. She has more than 280 scientific papers with an H-index of 64. She is elected member of the Royal Academy of Engineering Sciences (IVA), honorary doctor at NTNU, Norway, she received the KTH grand prize, is a Wallenberg Scholar and received the gold medal of IVA. She is scientific coordinator of Batteries Sweden and trustee in the Faraday Institution UK.