| eurio:abstract |
Today, energy production and transport are evolving fast to meet challenging environmental targets and growing demand. The Achilles’ heel is energy storage, which is incapable of providing both low cost and high-performance solutions. The answer is not a simple evolution of existing batteries but disruptive technologies that must be discovered fast. The BIG-MAP vision is to develop a modular, closed-loop infrastructure and methodology to bridge physical insights and data-driven approaches to accelerate the discovery of sustainable battery chemistries and technologies. BIG-MAP’s strategy is to cohesively integrate machine learning, computer simulations and AI-orchestrated experiments and synthesis to accelerate battery materials discovery and optimization. The project will be a lever to create the infrastructural backbone of a versatile and chemistry-neutral European Materials Acceleration Platform, capable of reaching a 10-fold increase in the rate of discovery of novel battery materials and interfaces.
- To succeed in this unprecedented international initiative, the BIG-MAP consortium covers the entire battery discovery value chain from atoms to battery cells, totaling 34 partners from 15 countries and spanning world-leading academic experts, research laboratories and industry leaders. The consortium is a joint European battery community effort, and the large-scale European Research Initiative BATTERY 2030+ stands united behind the BIG-MAP consortium. In addition to 13 core partners from BATTERY 2030+, the BIG-MAP consortium includes 21 leading European partners with complementary battery skills and essential competences from critical research areas such as quantum machine learning, deep learning and autonomous synthesis robotics. All partners will work to create an innovative methodology relying on unique competences and cross-cutting initiatives to deliver a shared infrastructure and 12 key demonstrators to showcase the value of AI-orchestrated materials discovery.</td>
</tr>
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<tr>
<td class=”element-table-key”>eurio:startDate</td>
<td class=”element-table-value”>2020-09-01</td>
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<td class=”element-table-value”>Battery Interface Genome - Materials Acceleration Platform</td>
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<td class=”element-table-key”>eurio:signatureDate</td>
<td class=”element-table-value”>2020-06-22</td>
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<td class=”element-table-key”>eurio:rcn</td>
<td class=”element-table-value”>230203</td>
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<tr>
<td class=”element-table-key”>eurio:url</td>
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<td class=”element-table-value”>SIGNED</td>
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<td class=”element-table-key”>eurio:endDate</td>
<td class=”element-table-value”>2024-02-29</td>
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| eurio:duration |
42 |
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https://w3id.org/big-map/resource#bigmap_bf15e03c_4a6e_3ed2_8c1c_184014344ebf |
| eurio:startDate |
2020-09-01 |
| eurio:endDate |
2024-02-29 |
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http://data.europa.eu/s66/resource/organisations/2b38aaf6-6012-3c84-ac39-1992a7f6a150 |
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http://data.europa.eu/s66/resource/fundingschemes/88cf7227-6997-39ae-ab35-a1b29df0c30c |
bigmap_dbe8c8c7_29d5_428b_8210_05bf4d427497
KD11
| rdfs:label |
KD11 - Demonstration of uncertainty-guided hybrid physics and deep-learning battery model |
| eurio:url |
https://www.big-map.eu/key-findings/kd11 |
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en |
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847147 |
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| eurio:abstract |
BIG-MAP has developed an active learning algorithm to speed up the segmentation of battery electrodes. Capturing the complex 3D microstructures can give insight about their operational properties and dynamic changes that occur during cycling. However, segmentation with the near-perfect accuracy required is a challenging task. We use a deep learning algorithm, a U-Net for segmentation and employ active learning to minimize the needed for training data.
Battery electrodes have a complex 3D microstructure, with a near-random spatial organization, which in turn affects their operational properties. Moreover, during cycling, dynamic changes occur in the material’s microstructure. These changes can be investigated with non-destructive 3D imaging techniques, such as X-ray tomography, which produce large experimental datasets at each acquisition/time-step to obtain final 3D volumetric reconstructions with sufficiently high resolution. In a raw 3D volume obtained through tomography, each voxel has a value which can be linked to a material in the sample. Segmentation is the process of attributing a phase to each voxel in the raw volume.
Quantitative analysis requires the segmentation to be precise as this strongly influences the ultimate analysis precision and fidelity. Thus, segmentation of tomographic datasets for quantitative analysis is then a long and challenging process. For complex microstructures, standard segmentation algorithms tend to fall short when aiming for the highest fidelity; machine learning models can then be investigated to improve this. The best results seem to come from either highly specific algorithms or U-Net like based CNNs, both of which are very time consuming, human intensive and require specific setups.
With our algorithm we aim to substantially reduce the human annotations needed by only annotating the data that benefits the model the most. In the initial step, a small number of images are annotated roughly (greatly cutting down the time needed compared to a precise annotation). We also have a large pool of unlabeled data from which we aim to only annotate the samples that will increase the accuracy the most.
We train the model until it no longer improves with the initial data. The algorithm then surveys the unlabelled data pool and chooses the patches that will be most useful, i.e., will increase the accuracy of the model the most. It then outputs its initial guess of the segmentation to the user. The user corrects the segmentation and restarts the training. This process is repeated until either the needed accuracy is reached, or a predefined labelling budget is exhausted.
By only needing annotation for a fraction of the data and providing a suggestion for the segmentation, we can greatly reduce the time needed to annotate electrode microstructures, accelerating research and gaining new insights into how the geometric structure and microstructure of the electrode influence its behavior and are influenced by, e.g., cycling.
|
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| eurio:title |
Demonstration of uncertainty-guided hybrid physics and deep-learning battery model |
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https://w3id.org/big-map/resource#bigmap_39d843f7_61f9_3f38_9f43_9d13d46c99ac |
bigmap_b3a90d84_518a_4adb_b613_0e63f7582cba
KD8
| eurio:language |
en |
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https://raw.githubusercontent.com/BIG-MAP/ProjectKnowledgeGraph/main/assets/img/icon/kd8_icon.png |
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http://data.europa.eu/s66#Result, http://www.w3.org/2002/07/owl#NamedIndividual |
| rdfs:label |
KD8 - Automated Workflow Demonstrator for integrated simulations and experiments |
| skos:altLable |
KD8 |
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| eurio:title |
Automated Workflow Demonstrator for integrated simulations and experiments |
| eurio:abstract |
Partners from DTU, EPFL, PSI, SINTEF and KIT teamed up in June 2022 to demonstrate an international materials acceleration platform (MAP) offering laboratory services through a marketplace. The operability and a proof of concept were demonstrated in an initial demonstration. The findings and lessons learned are published in an open access article1. Based on the identified means of improvement, the team revised the existing framework by restructuring the backend and improving the definition of the interfaces. The work on FINALES 2 was initiated in an intense hackathon of the server development team hosted at the Helmholtz-Institute Ulm in March 2023.
A team of BIG-MAP partners developed and deployed the Fast INtention-Agnostic LEarning Server (FINALES). This software framework provides interfaces specifically designed to connect various units of software and hardware to connect them forming a Materials Acceleration Platform (MAP). Emphasizing the collaborative spirit in this way of doing research, we refer to the connected units as tenants. One of the key design decisions made during the development of FINALES is its passive operation. FINALES does not actively trigger any actions in the MAP, but it works like a bulletin board. Users can request a service and the tenants, who offer corresponding services may pick up a request, process it and post the result back to FINALES, from where the user may collect it. This way of operating the MAP enables all the tenants to work at their own schedule without necessarily interfering with other tenants.
In several hackathons, the team prepared a demonstration , in which an optimizer developed and operated at DTU was configured to optimize electrolyte formulations for minimum density while maximizing viscosity. The optimizer posted requests for formulations to FINALES and specified, whether it would like to get experimental or computational data. Depending on its choice of the origin of the data, either the experimental setup autonomously prepared electrolyte formulations, measured density and viscosity and reported the results or the computational tenant performed its actions autonomously and reported data for ionic conductivity, density and various other quantities, which are obtained from the calculations. Once the requested results are available in the database, the optimizer started the subsequent iteration by predicting a new, promising formulation and requested new data. Since all this communication worked fully autonomous, the system was able to run these iterations for approximately 4.5 h without intervention by the researchers in the first demonstration.
An upgraded version of the pump and valve system of the setup for the Autonomous Synthesis and Analysis of Battery electrolytes experimental (ASAB) tenant, which served as the experimental tenant in the FINALES demonstration.
|
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BIG-MAP has developed a methodology to coordinate multi-site, multi-partners and multi-techniques investigations on standardized battery materials and cells using an array of complementary tools both in-lab and at large scale facilities. This multimodal characterization platform enables an optimized access to multidimensional parameter mapping. Specific workflows were designed and operated to combine experimental tools and acquire complementary sets of data with established fidelity criteria, paving the way to automatized correlative characterization.
Battery materials or cells can be characterized by a wealth of lab-scale and large-scale facility techniques, e.g., for instance, spectroscopies, imaging or diffraction, in many different types of modalities, e.g., low resolution fast-scanning or high resolution, post-mortem or operando, surface or bulk, etc. Each of these techniques may provide key insights into one specific aspect of materials and interfaces behaviors. However, data acquired on different instruments by different teams are usually not comparable or jointly exploitable, as they are not obtained in the same conditions with the same time or space resolutions, and not accompanied by the appropriate ontologized metadata. The single-technique approach has long lived but does not allow to accelerate our understanding of the complexity of battery processes, as well as to establish a more holistic knowledge of what governs the battery behavior, coupled to multiscale modelling, digital and AI enhanced approaches.
BIG-MAP is overcoming these hurdles by coordinating and correlating experiments to accelerate and automatize multiscale characterization. The first task was to list the characterisation experiments available in the consortium together with their technical and logistical characteristics (resolution, energy, observable, delay for measurement, characterisation readiness level CRL, etc.). The matrix regroups 136 available characterisation experiments each described with 27 descriptors, and it is available here. Based on the matrix information, we designed and executed an archetypal experimental workflow involving several facilities across Europe. As a result, pan-European in-lab and Large Scale Facility (LSF) characterization experiments were performed on a selected chemistry (graphite/LNO) probing a large range of temporal and spatial domains with various degrees of data fidelity. Data were stored on the BIG-MAP Archive, with metadata included in the BIG-MAP Notebook and linked to the BattINFO ontology. Samples were centrally produced in a standardized way and cells cycled using standardized protocols. In 18 months, more than 50 Tier-2 experiments were performed on fresh and aged materials using standard and modified electrolytes produced within BIG-MAP, and all extracted parameters were classified to allow for inputs into AI and modelling activities.
BIG-MAP is making an important step toward multiscale correlative characterization, setting the foundations for automated workflow-constructors available through ontology-based applications.
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Multi-modal Characterization Demonstrator capable of running coordinated multi-technique experiments to acquire multi-scale/multi-fidelity data |
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en |
WP4 Modular Robotics and Syntheses
Sensitivity analysis methodology for battery degradation models
Calibrated Uncertainty for Molecular Property Prediction using Ensembles of Message Passing Neural Networks
Training sets based on uncertainty estimates in the cluster-expansion method
bigmap_201e31ee_7090_4ca4_9f86_b41b2ce2566b
Task 2.3 ML in battery compound space
Task 11.1 Integration of multi-fidelity data from all domains
Enabling Modular Autonomous Feedback-Loops in Materials Science through Hierarchical Experimental Laboratory Automation and Orchestration
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The potential of scanning electrochemical probe microscopy and scanning droplet cells in battery research
Robotic cell assembly to accelerate battery research
Task 9.5 Integration of automated experiments and automated simulations
Autonomous Visual Detection of Defects from Battery Electrode Manufacturing
Dynamic Structure Discovery Applied to the Ion Transport in the Ubiquitous Lithium-ion Battery Electrolyte LP30
KD7
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An Open European Platform with BIG-MAP standards & testing protocols for battery materials |
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en |
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https://www.big-map.eu/key-findings/big-map-notebook |
| eurio:abstract |
BIG-MAP has developed an online laboratory notebook to securely store electrochemical and characterizations data using standards and protocols necessary to ensure reproducibility of battery testing and sharing. Metadata necessary to describe cell chemistry and components as well as cycling protocols are collected, and linked to the BattINFO Ontology entries. Demonstration is made that a web-based interface, the notebook, can be successfully linked to the ontology BattINFO.
Two complementary tools have been developed in the BIG-MAP projects: first, the ontology BattINFO that provides a shared vocabulary and taxonomy defining properties, relationships of battery-related concepts; and secondly, the BIG-MAP notebook that ensures standardized collection, reporting, documentation and storage of the collected research data following BIG-MAP standards and protocols. The two tools shall avoid usual battery pitfalls regarding the lack of reporting of information necessary to reproduce data and facilitate data exchange.1
The two tools differ both in their nature and goals, with a philosophical discrepancy between a web-based software developed to ensure proper collection of data (the Notebook), and a taxonomy developed to provide relationship between battery-related concepts (BattINFO). As in any system of this kind, degeneracy will develop over time leading to a lack of one-to-one correspondence between metadata included into the BIG-MAP notebook and BattINFO.
To fully integrate the Notebook into the BIG-MAP architecture, and demonstrate that BattINFO can serve as the central vocabulary ensuring automatic search of battery concepts, an application ontology has been developed. For this, a mapping is made between the BIG-MAP Notebook entries and concepts as listed in BattINFO, providing the lacking relationships between metadata reported by users to ensure consistent comparison and interpretation using BattINFO. Concepts lacking in BattINFO are mapped and stored in the application ontology, helping further development of BattINFO. Overall, creating this link demonstrates how the logic developed in BattINFO can be used to interface a web-based software with automated tools necessary for the deployment of machine learning algorithms.
The impact of such a notebook goes beyond the BIG-MAP project as it now serves as a template to share electrochemical data across different BATTERY 2030+ projects and eventually it will be open for the entire battery community.
|
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With this key demonstrator we have pioneered a methodology to make cell level models at the micrometer scale aware of the molecular mechanisms that govern battery performance at the sub-nanometer level. Because there is not a single simulation method available that governs all relevant scales and processes we developed an integrated chain of modelling methods to conquer this problem. The cornerstone of our scale-bridging approach is a robust set of computational tools, consisting of two integral components: a dedicated set of chemistry-aware simulation methods and a state-of-the-art machine-learning toolkit. The elements of the resulting toolkit were made available in externalizable workflows using widely adopted workflow engines, such as AIIDA and SimStack, to facilitate seamless integration as a driver of materials acceleration platforms for batteries. Our tools enable co-discovery with experimental groups.
The solid electrolyte interphase (SEI) has a critical influence on battery life, performance, and safety, but is extremely hard to characterize by experiments alone. To aid experimental analysis and battery optimization, we developed a bottom-up multiscale approach to SEI formation based on the system-specific characterization of microscopic processes. Initially, we delivered an ASE-based application to replicate SEI microstructures based on a rationally designed initial SEI morphology at the atomic scale by stochastically arranging crystal grains of the inorganic salts that formed during the initial stages of SEI formation and Li-ion migration1,2. We developed reactive molecular dynamics simulations3 and kinetic Monte Carlo (KMC)4 protocols that model the spatiotemporal evolution of organic and inorganic SEI components governed by a set of chemical reactions, diffusion, and aggregation at nanometer resolution utilizing kinetic information computed for specific electrolyte-anode chemistries.
These novel mesoscale models were integrated with atomistic models for specific chemistries and continuum models to account for the microstructure of the battery cell. Combined, these techniques enable unprecedented insights into SEI formation and growth and electrolyte performance. The scale-bridging methodology has been made available in the BIG-MAP App Store. Employing machine learning tools, we have been able to construct surrogate models5 for the growth of the SEI for electrolyte and electrode performance that could potentially be employed in the context of increasingly autonomous experimental protocols for battery characterization optimization.
This approach to multi-scale scale-bridging transcends the boundaries of the BIG-MAP project. It serves as an essential component and template, to streamline in-silico battery research, to facility materials acceleration platforms and also enables the broader participation of the entire battery community.
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AI-enhanced multi-scale demonstrator for accelerated scale-bridging |
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Data Management Plans: the Importance of Data Management in the BIG-MAP Project
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Wiley-VCH on behalf of Chemistry Europe |
| eurio:author |
Ivano Eligio Castelli, Daniel J. Arismendi-Arrieta, Arghya Bhowmik, Isidora Cekic-Laskovic, Simon Clark, Robert Dominko, Eibar Flores, Jackson Flowers, Karina Ulvskov Frederiksen, Jesper Friis, Alexis Grimaud, Karin Vels Hansen, Laurence J. Hardwick, Kersti Hermansson, Lukas Königer, Hanne Lauritzen, Frédéric Le Cras, Hongjiao Li, Sandrine Lyonnard, Henning Lorrmann, Nicola Marzari, Leszek Nied |
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Batteries & Supercaps |
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Data Management Plans: the Importance of Data Management in the BIG-MAP Project |
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http://data.europa.eu/s66/resource/results/9943952d-b401-3659-b11e-c7d8dd3ae6ec |
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10.1002/batt.202100117 |
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2566-6223 |
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Data Management Plans: the Importance of Data Management in the BIG-MAP Project |
| eurio:identifier |
957189_1241480_PUBLI |
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Machine learning based energy-free structure predictions of molecules (closed and open-shell), transition states, and solids
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Nature Communications |
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Machine learning based energy-free structure predictions of molecules (closed and open-shell), transition states, and solids |
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Nature Publishing Group |
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2021 |
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Lemm, Dominik; von Rudorff, Guido Falk; von Lilienfeld, O. Anatole |
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957189_1241479_PUBLI |
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2041-1723 |
Towards autonomous high-throughput multiscale modelling of battery interfaces
WP10 AI Accelerated Materials Discovery
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en |
| eurio:abstract |
A key part of the BIG-MAP effort was focussed on automating the construction of force field models that allow the investigation of battery components (their behaviour, properties and degradation) on molecular length and time scales. Machine learning enables the distillation of expensive electronic structure calculations into fast force fields. An example of such a force field, trained only on inorganic crystal structures obtained from the Materials Project shows stable molecular dynamics simulation of a prototype interface between graphite and LP57 electrolyte (EC/EMC and LiPF6).
The first paper focussed on building up a dataset of small organic solvent models corresponding to LP57 (a mixture of ethylene carbonate (EC) and ethyl-methyl carbonate (EMC)), and fitted with one of the "first generation" machine learning (ML) force field models, specifically Gaussian Approximation Potential (GAP). Careful analysis and iterative training, lead by Ioan-Bogdan Magdau, allowed the correct reproduction of the electrolyte density under a variety of EC/EMC compositions and temperatures. Current work is ongoing on simplifying the fitting protocol, and identifying the correct level of electronic structure theory that is accurate enough to obtain not just static properties, such as the density, but dynamic properties of interest to battery design, such as diffusivity and viscosity.
More recently, we have been working to simplify the protocol to generate force fields and in particular to make it easier to start doing molecular dynamics even before new training is attempted. This is done with so-called 'foundation models', trained to a large variety of structures, not specific to any application. The animation below (created by Cas van der Oord) shows the first attempt, using a model trained only on the inorganic crystal structures of the Materials Project, doing stable molecular dynamics of the interface between graphite and LP57. The system size is still small, and nothing terribly interesting happens in these few hundred picoseconds, but that this level of extrapolation from crystals to organic liquid interfaces is a huge milestone for model building. To help appreciate this, the next video is a reel of some of the crystal structures in the training set of the model.
|
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QML Demonstrator of an interface potential for an experimental electrode-electrolyte system |
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Machine learning 3D-resolved prediction of electrolyte infiltration in battery porous electrodes
bigmap_20b0fbbe_0f13_4a72_b953_3f0d6c7c8237
One-Shot Active Learning for Globally Optimal Battery Electrolyte Conductivity
Modeling the Solid Electrolyte Interphase - Machine Learning as a Game Changer?
Toward the design of chemical reactions: Machine learning barriers of competing mechanisms in reactant space.
Task 7.3 Design the battery interface ontology (BIO)
Task 11.4 Uncertainty-guided spatio-temporal models
KD6
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Development of a community-wide European Battery Interface Ontology |
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| eurio:abstract |
BattINFO provides a shared vocabulary and taxonomy that defines the properties, attributes, and relationships of battery-related concepts, such as cell chemistry, cell design, and performance metrics. This can enable more accurate and consistent data collection, analysis, and interpretation, as well as better comparison and benchmarking of different battery technologies and applications.
Moreover, an ontology can support the development of automated tools for battery design, optimization, and control, such as machine learning models, simulation software, and decision support systems. By leveraging the semantic richness and logic of an ontology, these tools can reason about the interdependencies and trade-offs between different battery parameters and objectives and generate insights and recommendations that are both reliable and actionable.
BattINFO is defined as part of the larger Elementary Multi-Perspective Materials Ontology (EMMO) and is scheduled for release in conjunction with the first stable release of EMMO. The release of BattINFO fulfils one of the 12 key demonstrators defined for the BIG-MAP project, namely “Development of a community-wide European Battery Interface Ontology”.
|
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KD6 |
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https://www.big-map.eu/key-findings/battinfo |
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847147 |
Task 7.4 Implement the ontology to describe specific case studies
Task 6.5 HTS of inorganic protective coatings
Task 6.3 HTS analysis via selected methods
Phase-field investigation of lithium electrodeposition under different applied overpotentials and operating temperatures
Computationally Efficient Quasi-3D Model of a Secondary Electrode Particle for Enhanced Prediction Capability of the Porous Electrode Model
KD5
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KD5 - HTS SEI Demonstrator using integrated high-throughput electrochemistry and ex situ high throughput spectroscopy to optimized electrolyte formulations and materials |
| eurio:abstract |
BIG-MAP has established a versatile material characterization and performance evaluation module focusing on liquid electrolyte formulations and compatible electrode materials with proven capabilities for lithium-based battery systems. The current state of achievements comprises autonomous high-throughput (HT) formulation, characterization and analysis systems complemented by a traditionally performed characterization approach to obtain reliable and transferable data sets that include both the common experimental targets and the partner-specific designs. The strong interaction and complementarity facilitate a new level of integration in the HT formulation-characterization-performance-analysis-evaluation chain, leading to accelerated identification of lead electrolyte candidates for given cell chemistries and applications.
The well-established high-throughput (HT) framework enables accelerated identification of affordable, electrochemically and thermally outperforming electrolyte candidates exemplified on four defined chemistry tiers. This identification process is based on customized preselection of electrolyte components: conducting salts, solvents/co-solvents, (multi)-functional additives and resulting formulations. The systematic evaluation on electrolyte, electrode and cell level as well as the characterization of concomitant electrolyte│electrode interfaces is carried out over the entire materials lifecycle, including relevant physicochemical, electrochemical and analytical properties and electrolyte/cell performance analysis.
The results are complemented by the results obtained in all other experimental work packages. Acquired data sets, stored on the BIG-MAP Archive, with metadata added to the BIG-MAP Notebook and linked to the BattINFO ontology, are furthermore used for AI-based analysis to recommend novel electrolyte formulations in terms of optimum concentrations and/or different components. Novel holistic and open data formats bundle results with metadata to minimize human error in data handling and maximize utilization of FAIR data principles. Automated data processing frees up human resources and increases the reliability of generated datasets. Apart from breaking new ground on the methodology, the abundance of in-depth data are used to build the BIG.
Through a multi-stage screening pipeline, this HT experimental workflow facilitates identification of hit/lead electrolyte formulations for targeted cell chemistry applications, accompanied by the generation of abundant pertinent data across the entire lifetime of the battery. Having verified the ability to exceed throughput and integrate into existing high-throughput material discovery pipelines for liquid electrolyte formulation studies, the module aims to demonstrate its extensibility by adapting sub-modules to other electrolyte classes and opening up the established methodology to other research categories of interest.
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| eurio:url |
https://www.big-map.eu/key-findings/high-throughput-experimentation-module |
| schema:image |
https://www.big-map.eu/-/media/sites/big-map/highlights/kd_isidora.png?h=273&w=700&hash=76DB91E170E984B68AD3BC3AFF428976 |
| eurio:rcn |
847147 |
| skos:altLabel |
KD5 |
| eurio:title |
HTS SEI Demonstrator using integrated high-throughput electrochemistry and ex situ high throughput spectroscopy to optimized electrolyte formulations and materials |
| eurio:language |
en |
Task 7.2 Design a general battery ontology
WP9 Infrastructure and Interoperability
Task 10.1 Accelerating experiments with active learning
Towards better and smarter batteries by combining AI with multisensory and self-healing approaches
Task 3.4 Verification and Validation of Models for HTS experiments
WP8 Standards and Protocols
Towards a 3D-resolved model of Si/Graphite composite electrodes from manufacturing simulations
Task 4.2 Hardware and software design
Task 2.1 Data from QM calculations
KD12
| rdfs:label |
KD12 - Transferability demonstrated for Li-ion hybrid models to novel battery chemistries |
| skos:altLabel |
KD12 |
| eurio:abstract |
The changing battery landscape requires advanced models that go beyond lithium-ion technology to continue the progress and investigation. The BIG-MAP project has devised innovative methodologies succeed on the adaptation of models from lithium-ion to emerging systems like sodium or magnesium-based batteries, overcoming lithium-ion limitations and promoting sustainable energy storage solutions. It includes machine learning models for predicting battery lifespan and customized experimental methods to test their applicability across different systems.
Many lifetime prediction models rely on time series forecasting using historical cycling data, but face challenges like error accumulation and limited historical context. To address these issues, we propose a regression-based approach integrating domain knowledge for adaptable and transferable battery lifetime forecasting. Our method aims to provide precise estimations of cycle numbers for specific State of Health (SOH) percentiles, improving understanding of battery lifetime prognosis. With a meticulously curated database and advanced post-processing techniques, our model demonstrates exceptional forecasting precision across diverse electrodes and input parameters, eliminating the need for separate methodologies for different battery technologies. Multiple simulations efficiently generate SOH evolution, minimizing errors and showcasing the model's proficiency in accurately understanding capacity decay and forecasting in various scenarios for both lithium-ion and sodium-ion cells.
Similar to the preceding battery lifetime prediction model, this model is intended to cater to various NMC cathode materials, encompassing diverse coatings, doping methodologies, and synthesis conditions. The batteries undergo testing under fluctuating temperature conditions.
From Li-ion to Mg metal anode-organic battery: Magnesium (Mg) metal batteries, known for their high capacity, face challenges due to limited practical electrolytes and cathode materials. While progress has been made in Mg electrolytes, organic cathode materials offer promising adaptability. We developed an Mg metal anode setup with an organic cathode using conjugated carbonyl active materials. Our configuration included a half-cell setup with an Mg metal foil anode, organic working electrode, and specific electrolyte. We tested two active materials: anthraquinone-based poly (anthraquinonyl sulfide) (PAQS) and benzoquinone-based poly (hydroquinonyl-benzoquinonyl sulfide) (PHBQS), both operating through carbonyl reduction.
The experimental pathway towards transferability in battery technology is complex due to numerous parameters and compatibility issues between electrode and electrolyte components. Despite challenges, an experimental plan was devised, starting from standard Li-ion technology and gradually transitioning to new chemistries while maintaining parameter consistency. The positive electrode, LiNiO2, with its intercalation properties, was retained, while a common activated carbon negative electrode was employed due to its capacitive redox mechanism. Contingency plans were made to focus on monovalent charge carrier ions (Li, Na, K) with a standardized electrolyte formulation (1M APF6 in EC/EMC) to address adhesion issues with laminated electrodes. Cycling tests revealed significant capacity fading, particularly with KPF6 electrolyte. Operando X-ray diffraction experiments at ALBA synchrotron confirmed the involvement of Na and K in the redox mechanism, despite the presence of Li+ ions in the electrolyte.
We investigated electrolyte systems involving NaPF6 and Mg(ClO4)2 in EC/EMC = 3:7 (LP57), extending beyond Li-ion batteries with LiPF6 electrolytes. While LiPF6 offers high conductivity and stability, challenges like moisture susceptibility persist. Recent research suggests improving LiPF6 electrolytes with specific solvents and co-solvents. Understanding LiPF6 in solvents, including metal cation solvation properties, is crucial for optimizing battery performance and exploring alternative electrolytes, applicable to Na and Mg batteries as well. Atomic-scale simulations via ab initio molecular dynamics (AIMD) explored solvation properties of Li, Na, and Mg cations, aiding insights into cation diffusion within electrolytes. Simulation results revealed distinct solvation structures for LiPF6, NaPF6, and Mg(ClO4)2 electrolytes, with stable trajectories providing insights into cation-solvent interactions.
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| eurio:isResultOf |
https://w3id.org/big-map/resource#bigmap_aaaae45f_1194_49b9_9fa8_75227e7ebbc3 |
| eurio:title |
Transferability demonstrated for Li-ion hybrid models to novel battery chemistries |
| eurio:rcn |
847147 |
| rdf:type |
http://www.w3.org/2002/07/owl#NamedIndividual, http://data.europa.eu/s66#Result |
| eurio:url |
https://www.big-map.eu/key-findings/kd12 |
| eurio:language |
en |
| bigmap:hasLeadPartner |
https://w3id.org/big-map/resource#bigmap_e6828054_2abc_3285_ae50_a049b4b799c5 |
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https://www.big-map.eu/-/media/sites/big-map/kd12.png?h=262&w=500&hash=90761FCE4A5C6904E8F707EF81820FF9 |
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https://raw.githubusercontent.com/BIG-MAP/ProjectKnowledgeGraph/main/assets/img/icon/kd12_icon.png |
Task 1.3 Research Data Management
Task 3.5 Workflow demonstrators
Task 1.2 Participation in the Future Battery Technologies initiative and collaboration with other LC-BAT projects
KD10
| rdfs:label |
KD10 - Modular packages for autonomous analysis of spectroscopic and electrochemical data |
| rdf:type |
http://www.w3.org/2002/07/owl#NamedIndividual, http://data.europa.eu/s66#Result |
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https://www.big-map.eu/-/media/sites/big-map/highlights/kd10.png?h=408&w=600&hash=13079F6EBFC23E79712264502A4A33CF |
| eurio:url |
https://www.big-map.eu/key-findings/modular-packages-for-autonomous-analysis-of-spectroscopic-and-electrochemical-data |
| eurio:abstract |
The BIG-MAP project generates large numbers of spectra during spatial mapping, in situ and operando experiments on battery materials. In this scenario, manual pre-processing of spectra becomes error-prone and prohibitively laborious. In this article we describe the processing tools we have developed to tackle the high-throughput spectral analysis challenge. Whether by harnessing human expertise or by leveraging neural network models, these tools are accelerating the way we uncover scientific insights from spectra.
Spectra are indispensable to understand battery materials. Alongside electrochemical testing and imaging, spectroscopies are one of the main characterisation pillars in the BIG-MAP project. Spectra reveal the properties and state of battery materials at multiple spatial scales, whether these materials are studied in isolation or as part of a battery cell before, during and after cycling. Nearly all spectra consist of (electron, photon) intensity counts indexed according to a scanning variable (e.g. absorption energy). Spectra are consequently a record of the patterns that result from the interaction between the spectroscopic probe and the sample material.
Analysing spectra, the traditional way. When spectra are only a few, experts visually inspect patterns (e.g. peaks) and interpret these within the context of the sample’s known composition, properties, or instead compare to physical models of the probe-sample interaction. Spectra are typically noisy and convolved with artifacts such as outliers and drifting baselines, which complicates pattern identification. Experts generally pre-process each spectrum to facilitate the recognition of relevant patterns. However, manual pre-processing is not only prone to biases that affect reproducibility, but it is also time consuming. The BIG-MAP project generates large numbers of spectra during spectral mapping, in-situ and operando experiments, for which manual pre-processing becomes prohibitively laborious. We have therefore developed tools for high-throughput processing of spectra either by keeping the human in the loop, or by outsourcing pattern recognition to neural network models.
Analysing spectra at scale. The first tool - PRISMA - implements traditional spectral analysis but in a high-throughput fashion. PRISMA implements both a codebase for spectral analysis and a graphical user interface (GUI). The codebase allows for trimming, baseline correction and peak fitting with typical lineshapes, such as Gaussian, Lorentzian and Pseudo-Voight profiles. The GUI enables users to visualize in real time the effects of pre-processing routines and parameters. Hence, PRISMA operates on a human-in-the-loop model, offering intuitive control over spectral processing and delivering results in an accessible *.csv format. We have demonstrated the app's strength via several case studies reported in a peer-reviewed publication.1 PRISMA has been released open source to the service of the battery community,2 and it is currently used by multiple consortium partners and institutions across the world.
Outsourcing spectral analysis to neural networks. Alternatively, we can automate the extraction of patterns from spectra using Convolutional Neural Networks (CNN). Instead of modelling a spectrum as a set of peaks, we leave a CNN to learn spectral patterns from large amounts of data, without heuristic assumptions in an autonomous way. These neural networks have been used to classify spectra into groups (e.g. which spectra characterize a species of bacteria), and to map spectra to the value of numerical properties (e.g. quantify the concentration of a chemical from its spectrum). However, what is the network learning from the data to make predictions? Is it using peaks and their positions as spectroscopists do? Is it learning spurious artifacts? We answered such critical questions by developing a CNN that learns to classify functional groups from infrared spectra. Our model classifies most functional groups with accuracies above 95%. Once we have verified that the CNN is accurate, we use a two-step approach for explaining the network's classification process, and so assess whether it is learning patterns that carry physical information. Our findings not only demonstrate that the CNN learns the characteristic group frequencies of functional groups, but also suggest that, unlike most spectroscopist, it also uses the absence of peaks and anharmonic vibrations to make predictions.3,4 CNNs help us learn spectrum-property relations from large number of spectra. Crucially, understanding what neural networks learn from data is instrumental to assess their ability to generalize, to study how the patterns built upon existing scientific principles, and to justify critical decisions based on model predictions.
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| schema:citation |
https://doi.org/10.1039/D3DD00203A, https://doi.org/10.1002/cmtd.202100094 |
| eurio:title |
Modular packages for autonomous analysis of spectroscopic and electrochemical data |
| bigmap:hasLeadPartner |
https://w3id.org/big-map/resource#bigmap_93f24b16_09c1_31f8_a413_897527554e50 |
| eurio:language |
en |
| skos:altLabel |
KD10 |
| eurio:rcn |
847147 |
Task 5.3 Data acquisition and visualization
KD3
| rdf:type |
http://data.europa.eu/s66#Result, http://www.w3.org/2002/07/owl#NamedIndividual |
| eurio:title |
Demonstration of Autonomous Synthesis Robotics of protective electrode coatings |
| eurio:isResultOf |
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| eurio:rcn |
847147 |
| eurio:url |
https://www.big-map.eu/key-findings/modular-robotic-synthesis-platform |
| bigmap:hasLeadPartner |
https://w3id.org/big-map/resource#bigmap_46b5234b_9b49_30ea_b058_b38bf9935454 |
| schema:video |
https://w3id.org/big-map/resource#bigmap_201e31ee_7090_4ca4_9f86_b41b2ce2566b |
| rdfs:label |
KD3 - Demonstration of Autonomous Synthesis Robotics of protective electrode coatings |
| eurio:abstract |
BIG-MAP has developed a modular robotic synthesis platform. The vision behind the platform is to achieve the fully automated synthesis of battery materials, leveraging the power of machine learning and AI for self-driving experimental materials optimization. A specific focus has been put on the modularity of the platform both on the hardware and software level allowing for the rapid exchange of modules. This will make it flexible and extensible, which is key in a rapidly evolving research environment.
On the hardware side, the platform utilizes a device container system where each container is dedicated to a specific task. These containers can be easily exchanged using a standardized connector system for mechanical positioning, power, and data transfer. Adding or removing a container is as simple as inserting or removing a single plug.
On the software level, the platform achieves seamless orchestration of workflows through standardized interfaces implemented once for each device container. These interfaces rely on industrial protocols such as REST APIs and OPC-UA, ensuring efficient and reliable communication between the central orchestration unit and various kinds of devices that are either custom-made or retrieved from commercial vendors.
First integration tests have proven successful, showcasing the platform's capabilities in fully autonomous optimization. For example, the platform has demonstrated its ability to optimize the recipe for mixing user-defined colors from differently colored liquids, highlighting the practical application of the engineering concepts embedded in both the hardware and software. This paves the way for future integration of modules specifically designed for battery material optimization.
Furthermore, the benefits of modular robotic platforms extend beyond battery research. The methods developed by BIG-MAP are transferrable to a wide range of scientific experiments, promising a broad and impactful reach.
|
| eurio:language |
en |
| skos:altLabel |
KD3 |
KD9
| rdf:type |
http://www.w3.org/2002/07/owl#NamedIndividual, http://data.europa.eu/s66#Result |
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https://w3id.org/big-map/resource#bigmap_aaaae45f_1194_49b9_9fa8_75227e7ebbc3, https://w3id.org/big-map/resource#bigmap_b91eb00a_fe7d_47be_a0b3_efeadba81a83, https://w3id.org/big-map/resource#bigmap_295cc7e2_2be8_4747_a1ca_bd93f84e31f3, https://w3id.org/big-map/resource#bigmap_dbae3b87_eee8_4f89_a80e_f01881ce062b, https://w3id.org/big-map/resource#bigmap_9a9f7579_0e77_43d4_b902_76de1ea597ed, https://w3id.org/big-map/resource#bigmap_b2c14112_53f4_4df0_a219_93fc6c1b0fe4, https://w3id.org/big-map/resource#bigmap_655364d8_f87e_4a10_9c27_b2e70aea00ed, https://w3id.org/big-map/resource#bigmap_c1a3e38f_3da3_4c61_b3d0_b761079a5ad4 |
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| eurio:abstract |
BIG-MAP has developed a platform to share and promote its state-of-the-art tools and methods: the BIG-MAP App Store. This online portal serves as the primary registry of all the apps used and developed in the projects funded by BIG-MAP, offering a one stop solution to explore powerful apps for battery research.
One of the aims of the App Store is to increase the accessibility and exposure of the apps, which cover various aspects of battery design, testing, simulation, and optimization. All the apps are open source and their source code is publicly available, allowing anyone to use and/or modify them for their own or collaborative research or educational purposes. The app store provides links to each app’s homepage, documentation and source code, where users can find detailed information about the app features, requirements, and usage. Currently we are in the process of adding video tutorials to help the installation process and also demonstrate typical use case of every app.
Adding new apps to the App Store is simple and easy process. The app developers only need to fill in some basic metadata, such as the app name, description, keywords, license, and contact details in a standardized template, and after a quick but robust review, the new app is automatically displayed on the portal.
As of now, the App Store hosts more than 25 apps, covering a broad range of topics such as automatic battery assembly with robots, automatic frameworks for various types of simulations, GUIs for running different electronic structure codes like Quantum ESPRESSO, VASP etc., modular tools for electrochemical analysis, machine learning tools, and battery management systems. The app store is continuously updated with new apps as the BIG-MAP consortium continues to produce innovative solutions for battery research.
The App Store is a valuable resource for anyone interested in battery innovation, whether they are researchers, students, educators, or enthusiasts. By providing a central hub for all the BIG-MAP apps, it facilitates the dissemination and adoption of the latest tools and methods for battery research.
|
| eurio:title |
Development of a BIG-MAP App-store for automated analysis modules and workflows |
| schema:citation |
https://doi.org/10.1016/j.matt.2023.07.016 |
| eurio:rcn |
847147 |
| rdfs:label |
KD9 - Development of a BIG-MAP App-store for automated analysis modules and workflows |
| bigmap:hasLeadPartner |
https://w3id.org/big-map/resource#bigmap_6d6921d0_b70d_3e26_b672_aac9fe03381e |
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https://raw.githubusercontent.com/BIG-MAP/ProjectKnowledgeGraph/main/assets/img/icon/kd9_icon.png |
| eurio:language |
en |
| skos:altLabel |
KD9 |
| eurio:url |
https://www.big-map.eu/key-findings/big-map-app-store |
Task 9.3 Development of a software framework for data management, linkage and protection
Task 10.4 Automatic reasoning and materials funnel
Task 3.3 AI enhanced models
Learning the laws of lithium-ion transport in electrolytes using symbolic regression
Task 1.5 Development and implementation of an Exploitation Plan
Task 11.5 Transferability of developed methodology to other materials
Task 11.2 Dynamic interface/interphase descriptors
Task 2.4 Battery simulations at the atomistic level
Task 3.1 Development and validation of multiscale modelling frameworks
Task 6.1 Preparation and characterization of feeding units for the HTS assays
Task 4.5 Combinatorial synthesis and application of inorganic coatings
Task 10.3 Transfer learning and new fundamental insights
WP7 Battery Interface Ontology
WP2 Accelerated atomic-scale simulations
Task 2.2 Train ML potentials
Task 8.2 Creation of working groups and workflow
Task 11.3 Hierarchical latent space multi-scale models
Task 4.4 Synthesis of components for organic protective coatings
Task 1.6 Stakeholder activities and management
Task 7.1 Establish ontology standards to support interoperability and dissemination
WP11 Battery Interface Genome
WP1 Project management and education, exploitation and outreach
Task1.1 Administrative and financial management and reporting
Task 1.4 Intellectual Property management and utilization
hasEuroSciVocClassification
Task 9.1 Robust and reliable workflows for automated simulations
Task 8.3 Implementation of these standards and protocols
Task 2.5 Modelling operando spectroscopies and atomistic-level characterization
Task 4.3 Integration and validation
Task 8.1 Selection and refinement of pre-existing initiatives regarding standards and protocols
Task 10.2 An automated data analysis framework
Task 1.7 Dissemination, outreach and communication
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