Data‐Driven Analysis of High‐Throughput Experiments on Liquid Battery Electrolyte Formulations: Unraveling the Impact of Composition on Conductivity**

Krishnamoorthy, Anand Narayanan; Wölke, Christian; Diddens, Diddo; Maiti, Moumita; Mabrouk, Youssef; Yan, Peng; Grünebaum, Mariano; Winter, Martin; Heuer, Andreas; Cekic‐Laskovic, Isidora

doi:10.1002/cmtd.202200008

Cited by 8 publications

(30 citation statements)

References 55 publications

(122 reference statements)

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“…High throughput experimentation (HTE) system. The robotic HTE system 13 , used to acquire the data 12 presented here, is designed for high-throughput operation in a nitrogen atmosphere. The setup designed for the formulation of electrolyte solutions is able to prepare 96 formulations in 8 h by gravimetric dosing of solid and liquid materials into polymer or aluminium vials.…”

Section: Methodsmentioning

confidence: 99%

“…For the measurement, the samples are arranged in groups of eight samples per rack, three of which are mounted on one larger rack. Four of these combined racks can be connected to the Metrohm Autolab potentiostat, which is used for the measurements 13 .…”

Section: Methodsmentioning

confidence: 99%

“…Furthermore, it can help to gain deeper insights regarding composition-property-performance relationships. Fractions of this dataset served as the basis for several machine learning models published elsewhere 11,13,14 . The automated high-throughput experimentation system 13 available at the Helmholtz Institute Münster is used to formulate a variety of electrolyte solutions based on EC, EMC, PC and LiPF 6 .…”

Section: Background and Summarymentioning

confidence: 99%

“…Fractions of this dataset served as the basis for several machine learning models published elsewhere 11,13,14 . The automated high-throughput experimentation system 13 available at the Helmholtz Institute Münster is used to formulate a variety of electrolyte solutions based on EC, EMC, PC and LiPF 6 . Ratios of (PC + EC):EMC of 3:7 and 1:1 are covered in the dataset 12 .…”

Section: Background and Summarymentioning

confidence: 99%

“…The robotic system 13 used for the acquisition of the data is able to dispense liquid and solid components into aluminium or polymer vials with high accuracy. Each formulation is identified by a batch number and measurements are identified by a unique ID stored and reported on the vial through a QR code.…”

Section: Background and Summarymentioning

confidence: 99%

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Conductivity experiments for electrolyte formulations and their automated analysis

et al. 2023

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Electrolytes are considered crucial for the performance of batteries, and therefore indispensable for future energy storage research. This paper presents data that describes the effect of the electrolyte composition on the ionic conductivity. In particular, the data focuses on electrolytes composed of ethylene carbonate (EC), propylene carbonate (PC), ethyl methyl carbonate (EMC), and lithium hexafluorophosphate (LiPF6). The mass ratio of EC to PC was varied, while keeping the mass ratio of (EC + PC) and EMC at fixed values of 3:7 and 1:1. The conducting salt concentration was also varied during the study. Conductivity data was obtained from electrochemical impedance spectroscopy (EIS) measurements at various temperatures. Based on the thus obtained temperature series, the activation energy for ionic conduction was determined during the analysis. The data is presented here in a machine-readable format and includes a Python package for analyzing temperature series of electrolyte conductivity according to the Arrhenius equation and EIS data. The data may be useful e.g. for the training of machine learning models or for reference prior to experiments.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Background and Summarymentioning

confidence: 99%

Section: Background and Summarymentioning

confidence: 99%

Section: Background and Summarymentioning

confidence: 99%

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Conductivity experiments for electrolyte formulations and their automated analysis

et al. 2023

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Formulation Graphs for Mapping Structure-Composition of Battery Electrolytes to Device Performance

Sharma,

Giammona,

Zubarev

et al. 2023

J. Chem. Inf. Model.

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Advanced computational methods are being actively sought to address the challenges associated with the discovery and development of new combinatorial materials, such as formulations. A widely adopted approach involves domaininformed high-throughput screening of individual components that can be combined together to form a formulation. This manages to accelerate the discovery of new compounds for a target application but still leaves the process of identifying the right "formulation" from the shortlisted chemical space largely a laboratory experiment-driven process. We report a deep learning model, the Formulation Graph Convolution Network (F-GCN), that can map the structure-composition relationship of the formulation constituents to the property of liquid formulation as a whole. Multiple GCNs are assembled in parallel that featurize formulation constituents domain-intuitively on the fly. The resulting molecular descriptors are scaled based on the respective constituent's molar percentage in the formulation, followed by integration into a combined formulation descriptor that represents the complete formulation to an external learning architecture. The use case of the proposed formulation learning model is demonstrated for battery electrolytes by training and testing it on two exemplary data sets representing electrolyte formulations vs battery performance: one data set is sourced from the literature about Li/Cu half-cells, while the other is obtained by lab experiments related to lithium-iodide full-cell chemistry. The model is shown to predict performance metrics such as Coulombic efficiency (CE) and specific capacity of new electrolyte formulations with the lowest reported errors. The best-performing F-GCN model uses molecular descriptors derived from molecular graphs (GCNs) that are informed with HOMO− LUMO and electric moment properties of the molecules using a knowledge transfer technique.

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Mechanistic understanding of the correlation between structure and dynamics of liquid carbonate electrolytes: impact of polarization

Maiti

Krishnamoorthy

Mabrouk

et al. 2023

Phys. Chem. Chem. Phys.

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Data‐Driven Analysis of High‐Throughput Experiments on Liquid Battery Electrolyte Formulations: Unraveling the Impact of Composition on Conductivity**

Cited by 8 publications

References 55 publications

Conductivity experiments for electrolyte formulations and their automated analysis

Conductivity experiments for electrolyte formulations and their automated analysis

Formulation Graphs for Mapping Structure-Composition of Battery Electrolytes to Device Performance

Mechanistic understanding of the correlation between structure and dynamics of liquid carbonate electrolytes: impact of polarization

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