A chemically consistent graph architecture for massive reaction networks applied to solid-electrolyte interphase formation

Blau, Samuel M.; Patel, H.; Spotte-Smith, Evan Walter Clark; Xie, Xiaowei; Dwaraknath, Shyam; Persson, Kristin A.

doi:10.1039/d0sc05647b

Cited by 51 publications

(80 citation statements)

References 48 publications

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“…The filtering is done by first constructing a small reaction network with only the original molecules and the fragments, considering one-electron redox and one-bond change reactions, according to the previously reported procedure. 51 Pathfinding to each specie mentioned above enable us to identify which fragments are thermodynamically accessible in the present model conditions by overall exergonic reaction pathways (overall ∆G < 0). However, an exception is made for H 2 O to include the fragments OH and H in all charge states, as breaking a bond in water is endergonic in molecular calculations, but will occur favorably in the presence of Li metal (as will be discussed in Sec 2.3 and Sec 3.1).…”

Section: Generation Of Candidate Molecules and Molecular Intermediates Through Fragmentation And Recombinationmentioning

confidence: 99%

“…The reaction network was constructed as a connected graph using the same graph representation as described in previous work. 51 Allowing a broad range of reactions between the molecular species, we chose to include "concerted" reactions (reactions that involve multiple, simultaneous bonds breaking or forming) with up to 5 bond changes. Similar constraints (usually ≤ 2 bonds breaking and ≤2 bonds forming) were imposed in other works.…”

Section: Construction Of Reaction Network and Identification Of Reaction Pathwaysmentioning

confidence: 99%

“…[42][43][44][45][46][47][48][49][50] Recently, a new data-driven methodology was developed to analyze complex electrochemical reactive systems with minimal chemical intuitive bias through the use of computational reaction networks. 51 Using this methodology, pathways from Li + and EC to LEDC under reductive conditions corresponding to Li metal were explored. It was demonstrated that the reaction network automatically recovers previously suggested reaction mechanisms for the formation of LEDC as well as proposed novel, thermodynamically feasible mechanisms that had not previously been reported.…”

Section: Introductionmentioning

confidence: 99%

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Data-Driven Prediction of Formation Mechanisms of Lithium Ethylene Monocarbonate with an Automated Reaction Network

Xie

Spotte-Smith²,

Patel³

et al. 2021

Preprint

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<div><div><div><p>Interfacial reactions are notoriously difficult to characterize and robust prediction of the chemical evolution and associated functionality of the resulting surface film is one of the grand challenges of materials chemistry. The solid–electrolyte interphase (SEI), critical to the operation of Li-ion batteries (LIB), exemplifies such a surface film and despite decades of work, considerable controversy remains regarding the major components of the SEI as well as their formation mechanisms. In this work, we present pioneering results of a newly developed data-driven reaction network addressing the recent question whether lithium ethylene monocarbonate (LEMC) or lithium ethylene dicarbonate (LEDC) is the major organic component of the LIB SEI. Our data-driven, automated methodology is based on a systematic generation of relevant species using a general fragmentation/recombination procedure which provides the basis for our vast thermodynamic reaction landscape, calculated with density functional theory (DFT). The graph implementation of the reaction landscape is subsequently explored using shortest pathfinding algorithms, identifying reactions to LEMC from EC, Li+ and H2O under the electron chemical potential of Li metal. Confirming the viability of our approach, the reaction network automatically recovers previously-proposed formation mechanisms of LEMC from EC and LEDC through hydrolysis, among which the direct hydrolysis of EC under basic conditions is found to be the most kinetically favorable. We also identify several other new reaction pathways to LEMC, illustrating the complex and competitive landscape of possible electrochemical electrolyte decomposition reactions. For example, we recover a LEMC formation mechanism that generates lithium hydride as a by-product and a radical mechanism through breaking the (CH2)O–C(–O)OLi bond in LEDC, neither of which has been proposed previously. Most importantly, we find that all identified paths, which are also kinetically favorable under the explored conditions, require water as a reactant. This condition severely limits the amount of LEMC that can form, as compared to LEDC, a conclusion that has direct impact on our understanding of SEI formation in Li-ion energy storage systems. Finally, we emphasize that our framework demonstrates robust, automated, data-driven predictions of novel interfacial reaction mechanisms and this framework is generally applicable to other reactive systems.</p></div></div></div>

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Section: Generation Of Candidate Molecules and Molecular Intermediates Through Fragmentation And Recombinationmentioning

confidence: 99%

Section: Construction Of Reaction Network and Identification Of Reaction Pathwaysmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Data-Driven Prediction of Formation Mechanisms of Lithium Ethylene Monocarbonate with an Automated Reaction Network

Xie

Spotte-Smith²,

Patel³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…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.…”

Section: A Holistic Infrastructure For Autonomous Battery Discoverymentioning

confidence: 99%

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

Vegge¹,

Tarascon

Edström

2021

Advanced Energy Materials

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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).

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“…Already, our group has used a subset of this data to generate a massive computational reaction network, identifying novel and chemically reasonable reactive pathways to a key SEI product, lithium ethylene dicarbonate (LEDC). 14 It would be possible to take a similar approach to automatically identify pathways to other SEI products of interest, or perhaps to search for novel products not previously identified in experiments.…”

Section: Background and Summarymentioning

confidence: 99%

Quantum Chemical Calculations of Lithium-Ion Battery Electrolyte and Interphase Species

Spotte-Smith

Blau²,

Xie³

et al. 2021

Preprint

Self Cite

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Lithium-ion batteries (LIBs) represent the state of the art in high-density energy storage. To further advance LIB technology, a fundamental understanding of the underlying chemical processes is required. In particular, the decomposition of electrolyte species and associated formation of the solid electrolyte interphase (SEI) is critical for LIB performance. However, SEI formation is poorly understood, in part due to insufficient exploration of the vast reactive space. The Lithium-Ion Battery Electrolyte(LIBE) dataset reported here aims to provide accurate first-principles data to improve the understanding of SEI species and associated reactions. The dataset was generated by fragmenting a set of principal molecules, including solvents, salts, and SEI products, and then selectively recombining a subset of the fragments. All candidate molecules were analyzed at the ωB97X-V/def2-TZVPPD/SMD level of theory at various charges and spin multiplicities. In total, LIBE contains structural,thermodynamic, and vibrational information on over 17,000 unique species. In addition to studies of reactivity in LIBs, this dataset may prove useful for machine learning of molecular and reaction properties.

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A chemically consistent graph architecture for massive reaction networks applied to solid-electrolyte interphase formation

Abstract: Modeling reactivity with chemical reaction networks could yield fundamental mechanistic understanding that would expedite the development of processes and technologies for energy storage, medicine, catalysis, and more. Thus far, reaction...

Cited by 51 publications

References 48 publications

Data-Driven Prediction of Formation Mechanisms of Lithium Ethylene Monocarbonate with an Automated Reaction Network

Data-Driven Prediction of Formation Mechanisms of Lithium Ethylene Monocarbonate with an Automated Reaction Network

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

Quantum Chemical Calculations of Lithium-Ion Battery Electrolyte and Interphase Species

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