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

Abstract

<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>

Toward a Mechanistic Model of Solid–Electrolyte Interphase Formation and Evolution in Lithium-Ion Batteries

“…Interestingly, we observe that the formation of LEMC is essentially unaffected by these competing reductive processes. In agreement with our recent findings based on analysis of CRNs, 39 the most facile path for LEMC formation is direct basic hydrolysis of Li + EC (Scheme 1d). Since we assume that water reduces before significant SEI formation begins, this means that LEMC can form under any electrochemical conditions studied here.…”

“…30,31 However, in spite of decades of careful study, a mechanistic explanation of SEI composition and structure remains elusive. A range of theoretical techniques such as density functional theory (DFT), 32,33 reactive classical 34,35 and ab initio 36,37 molecular dynamics (MD), and computational reaction networks (CRNs) 38,39 have all revealed plausible reaction pathways to key SEI components. Yet the combination of these methods has been unable to move beyond the identification of specific mechanisms toward the true formation process which involves multiproduct dynamics and competitive pathways occurring on time scales ranging from picoseconds 40 to days.…”

Community Reaction Network Reduction for Constructing a Coarse-Grained Representation of Combustion Reaction Mechanisms

“…A range of methods have been developed in the past, 3 including directed relation graph (DRG), 4 sensitivity analysis, 5 path flux analysis (PFA), 6 flux projection tree (FPT), 7 quasi-steady-state approximation, 8 and computational singular perturbation. 9 Recently, Blau et al developed a chemical reaction network for a Li-ion solid electrolyte interphase consisting of more than 6000 species, 10,11 and the database generated by this technique was analyzed by species filters, Monte Carlo trajectories, and heuristic analysis of average trajectories to reduce to 18 network species. 12 There have been a number of alternatives for automatic mechanism generation 13−16 and fragment-based, virtual-reality direct-molecular-dynamics simulations along with network analysis of mechanisms of combustion reactions.…”

Towards a Mechanistic Model of Solid-Electrolyte Interphase Formation and Evolution in Lithium-ion Batteries

“…30,31 However, in spite of decades of careful study, a mechanistic explanation of SEI composition and structure remains elusive. A range of theoretical techniques such as density functional theory (DFT), 32,33 reactive classical 34,35 and ab initio 36,37 molecular dynamics, and computational reaction networks (CRNs) 38,39 have all revealed plausible reaction pathways to key SEI components. Yet the combination of these methods has been unable to move beyond the identification of specific mechanisms towards the true formation process which involves multi-product dynamics and competitive pathways occurring on timescales ranging from picoseconds 40 to days.…”