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Open Access Publications from the University of California

Scholarly Works (13 results)

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UC Berkeley Electronic Theses and Dissertations (2023)

Anthropogenic climate change represents one of the greatest present threats to human society and global ecology. Rapid and dramatic reductions in greenhouse gas emissions are essential to avoid catastrophic global warming and environmental collapse. Although renewable energy from solar and wind resources is cheap and widely available, these resources are intermittent, and energy storage is needed to reliably supply renewable energy to the electrical grid and power vehicles. In particular, electrochemical energy storage in batteries can be highly efficient and energy-dense, showing tremendous promise to decarbonize the energy and transportation industries.

Metal-ion batteries like lithium-ion batteries (LIBs) are the current state of the art for commercial and research energy storage technologies. To achieve high energy density, metal-ion batteries typically operate at voltages outside of the electrochemical stability window of their electrolytes. As a result, electrolyte components react electrochemically, reducing and/or oxidizing to trigger a complex reaction cascade. In some batteries, such as LIBs with graphitic negative electrodes and electrolytes with certain cyclic and linear carbonate solvents, electrolyte degradation is managed by the formation of a passivation film known as an interphase. Once an interphase forms, these batteries are extremely stable over many years and thousands of charge-discharge cycles. To realize the goal of next-generation energy storage technologies with higher energy densities, including lithium-ion batteries with lithium metal or silicon electrodes as well as multivalent batteries (e.g. magnesium-ion batteries or MIBs), electrolytes must be designed either to avoid decomposition altogether or to react in a controlled manner such that they form stable interphase films.

Herein, I discuss efforts to predict the behavior of reactive electrochemical systems such as battery electrolytes using computational simulations and data science. I begin (Chapter 1) with an introduction to battery electrochemistry, focusing primarily on the role of electrolytes and interphase formation, as well as the inherent challenges of interphase engineering. In Chapter 2, I review the literature on analysis of interphase films using both experimental and theoretical approaches. As I will show, there are few techniques available that can provide mechanistic explanations for electrochemical reactivity accounting for the complex interactions between electrodes, electrolyte species, impurities, and decomposition intermediates and products.

Chapter 3 details a traditional study of electrolyte decomposition using a first-principles quantum chemical method: density functional theory (DFT). Specifically, I use DFT to explain the chemical and thermal instability of lithium hexafluorophosphate (LiPF6) salt in terms of elementary reaction mechanisms. This study highlights the limitations of chemical intuition in understanding reaction cascades and the need for new methods to broadly explore diverse (electro)chemical interactions.

In Chapter 4, I describe such a methodology that combines high-throughput DFT, chemical reaction networks (CRN), and stochastic simulations to predict reaction outcomes and pathways in complex systems with minimal prior knowledge. As a proof of concept (Chapter 5), I apply this approach to reductive electrolyte decomposition and interphase formation in LIBs with ethylene carbonate as a solvent. I recover most previously reported interphase products and predict several new, previously unreported, but chemically plausible species. From this starting point, I apply a kinetic Monte Carlo (kMC) algorithm to construct a model of LIB interphase formation and evolution (Chapter 6). This model successfully captures the expected bilayer structure of the interphase without relying on adjustable parameters or fitting to experiment.

Finally, in Chapter 7, I apply the previously described CRN methods to a new system where significantly less information is known: electrolyte decomposition and gas evolution in MIBs. I show how CRNs can help to interpret experimental spectra (in this case differential electrochemical mass spectroscopy or DEMS) and explain not only why certain species form in abundance but also why other species cannot form due to kinetic limitations.

I conclude (Chapter 8) by highlighting opportunities to apply computational modeling --- in particular using CRNs --- to understand complex (electro)chemical systems. The developments laid out here represent a significant step forward towards the rational design of reactive processes not only in the world of batteries and energy storage but also in electrochemical synthesis, pollution management, and much more. I also point out remaining challenges to studies of reactivity --- both experimental and computational --- and suggest possible avenues for future research to alleviate them.

    Cover page: On the Exploration of Electrochemical Reaction Cascades
    • Article
    • Peer Reviewed
    UC Berkeley Previously Published Works (2021)
    A broad collection of technologies, including e.g. drug metabolism, biofuel combustion, photochemical decontamination of water, and interfacial passivation in energy production/storage systems rely on chemical processes that involve bond-breaking molecular reactions. In this context, a fundamental thermodynamic property of interest is the bond dissociation energy (BDE) which measures the strength of a chemical bond. Fast and accurate prediction of BDEs for arbitrary molecules would lay the groundwork for data-driven projections of complex reaction cascades and hence a deeper understanding of these critical chemical processes and, ultimately, how to reverse design them. In this paper, we propose a chemically inspired graph neural network machine learning model, BonDNet, for the rapid and accurate prediction of BDEs. BonDNet maps the difference between the molecular representations of the reactants and products to the reaction BDE. Because of the use of this difference representation and the introduction of global features, including molecular charge, it is the first machine learning model capable of predicting both homolytic and heterolytic BDEs for molecules of any charge. To test the model, we have constructed a dataset of both homolytic and heterolytic BDEs for neutral and charged (-1 and +1) molecules. BonDNet achieves a mean absolute error (MAE) of 0.022 eV for unseen test data, significantly below chemical accuracy (0.043 eV). Besides the ability to handle complex bond dissociation reactions that no previous model could consider, BonDNet distinguishes itself even in only predicting homolytic BDEs for neutral molecules; it achieves an MAE of 0.020 eV on the PubChem BDE dataset, a 20% improvement over the previous best performing model. We gain additional insight into the model's predictions by analyzing the patterns in the features representing the molecules and the bond dissociation reactions, which are qualitatively consistent with chemical rules and intuition. BonDNet is just one application of our general approach to representing and learning chemical reactivity, and it could be easily extended to the prediction of other reaction properties in the future.
      Cover page: BonDNet: a graph neural network for the prediction of bond dissociation energies for charged molecules
      • Article
      • Peer Reviewed
      UC Berkeley Previously Published Works (2023)
      Electrolyte decomposition constitutes an outstanding challenge to long-life Li-ion batteries (LIBs) as well as emergent energy storage technologies, contributing to protection via solid electrolyte interphase (SEI) formation and irreversible capacity loss over a battery’s life. Major strides have been made to understand the breakdown of common LIB solvents; however, salt decomposition mechanisms remain elusive. In this work, we use density functional theory to explain the decomposition of lithium hexafluorophosphate (LiPF6) salt under SEI formation conditions. Our results suggest that LiPF6 forms POF3 primarily through rapid chemical reactions with Li2CO3, while hydrolysis should be kinetically limited at moderate temperatures. We further identify selectivity in the proposed autocatalysis of POF3, finding that POF3 preferentially reacts with highly anionic oxygens. These results provide a means of interphase design in LIBs, indicating that LiPF6 reactivity may be controlled by varying the abundance or distribution of inorganic carbonate species or by limiting the transport of PF6- through the SEI.
        Cover page: Elementary Decomposition Mechanisms of Lithium Hexafluorophosphate in Battery Electrolytes and InterphasesCreative Commons 'BY' version 4.0 license
        • Article
        • Peer Reviewed
        UC Berkeley Previously Published Works (2020)
        Thermal storage and transfer fluids have important applications in industrial, transportation, and domestic settings. Current thermal fluids have relatively low specific heats, often significantly below that of water. However, by introducing a thermochemical reaction to a base fluid, it is possible to enhance the fluid's thermal properties. In this work, density functional theory (DFT) is used to screen Diels-Alder reactions for use in aqueous thermal fluids. From an initial set of 52 reactions, four are identified with moderate aqueous solubility and predicted turning temperature near the liquid region of water. These reactions are selectively modified through 60 total functional group substitutions to produce novel reactions with improved solubility and thermal properties. Among the reactions generated by functional group substitution, seven have promising predicted thermal properties, significantly improving specific heat (by as much as 30.5%) and energy storage density (by as much as 4.9%) compared to pure water.
          Cover page: Aqueous Diels–Alder reactions for thermochemical storage and heat transfer fluids identified using density functional theory
          • Article
          • Peer Reviewed
          UC Berkeley Previously Published Works (2024)
          Activation barriers of elementary reactions are essential to predict molecular reaction mechanisms and kinetics. However, computing these energy barriers by identifying transition states with electronic structure methods (e.g., density functional theory) can be time-consuming and computationally expensive. In this work, we introduce CoeffNet, an equivariant graph neural network that predicts activation barriers using coefficients of any frontier molecular orbital (such as the highest occupied molecular orbital) of reactant and product complexes as graph node features. We show that using coefficients as features offer several advantages, such as chemical interpretability and physical constraints on the network's behaviour and numerical range. Model outputs are either activation barriers or coefficients of the chosen molecular orbital of the transition state; the latter quantity allows us to interpret the results of the neural network through chemical intuition. We test CoeffNet on a dataset of SN2 reactions as a proof-of-concept and show that the activation barriers are predicted with a mean absolute error of less than 0.025 eV. The highest occupied molecular orbital of the transition state is visualized and the distribution of the orbital densities of the transition states is described for a few prototype SN2 reactions.
            Cover page: CoeffNet : predicting activation barriers through a chemically-interpretable, equivariant and physically constrained graph neural network
            • Article
            • Peer Reviewed
            UC Berkeley Previously Published Works (2024)
            Electrolyte decomposition limits the lifetime of commercial lithium-ion batteries (LIBs) and slows the adoption of next-generation energy storage technologies. A fundamental understanding of electrolyte degradation is critical to rationally design stable and energy-dense LIBs. To date, most explanations for electrolyte decomposition at LIB positive electrodes have relied on ethylene carbonate (EC) being chemically oxidized by evolved singlet oxygen (1O2) or electrochemically oxidized. In this work, we apply density functional theory to assess the feasibility of these mechanisms. We find that electrochemical oxidation is unfavorable at any potential reached during normal LIB operation, and we predict that previously reported reactions between the EC and 1O2 are kinetically limited at room temperature. Our calculations suggest an alternative mechanism in which EC reacts with superoxide (O2-) and/or peroxide (O22-) anions. This work provides a new perspective on LIB electrolyte decomposition and motivates further studies to understand the reactivity at positive electrodes.
              Cover page: A Critical Analysis of Chemical and Electrochemical Oxidation Mechanisms in Li-Ion Batteries
              • Article
              • Peer Reviewed
              UC Berkeley Previously Published Works (2022)
              The rational design of electrolytes has been a long-standing challenge in chemistry and materials science. In this work, we demonstrate a computational rationale for improving the performance of weakly coordinating electrolytes in currently challenging multivalent-ion battery applications, based on enhanced thermodynamic and kinetic stability against reductive decomposition. A series of fluorinated alkoxyborate and alkoxyaluminate salts are systematically examined based on their reduction and oxidation potentials and, motivated by NMR spectroscopy, detailed reductive decomposition pathways involving the breaking of Al/B-O, C-O, or C-F bonds are obtained. Based on the decomposition kinetics, the hexafluoro-tert-isopropoxy (hfip) ligand for borates and the trifluoro-tert-butoxy (tftb) ligand for aluminates are identified as promising ligands for constructing the salt anions. This borate prediction corroborates previous experimental work on Mg[B(hfip)4]2and Ca[B(hfip)4]2, in which excellent electrochemical properties were reported. We find that steric factors govern the B-O bond-breaking decomposition kinetics while electronic factors are more important for aluminate salts. There is more charge transfer character in the aluminate transition states compared with borates for Al/B-O bond-breaking decomposition and thus electron-withdrawing ligands tend to stabilize the aluminate transition states. Such molecular-level understandings allow for better design principles for developing new electrolytes with improved stability and performance.
                Cover page: Reductive Decomposition Kinetics and Thermodynamics That Govern the Design of Fluorinated Alkoxyaluminate/Borate Salts for Mg-Ion and Ca-Ion BatteriesCreative Commons 'BY' version 4.0 license
                • Article
                • Peer Reviewed
                UC Berkeley Previously Published Works (2021)
                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 Li-ion batteries (LIBs), 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. Here we use a reaction network to investigate 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 a vast thermodynamic reaction landscape, calculated with density functional theory. The shortest pathfinding algorithms are employed to explore the reaction landscape and obtain previously proposed formation mechanisms of LEMC as well as several new reaction pathways and intermediates. For example, we identify two novel LEMC formation mechanisms: one which involves LiH generation and another that involves breaking the (CH2)O-C(═O)OLi bond in LEDC. 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 with LEDC, a conclusion that has direct impact on the SEI formation in Li-ion energy storage systems. Finally, the data-driven framework presented here is generally applicable to any electrochemical system and expected to improve our understanding of surface passivation.
                  Cover page: Data-Driven Prediction of Formation Mechanisms of Lithium Ethylene Monocarbonate with an Automated Reaction Network
                  • Article
                  • Peer Reviewed
                  UC Berkeley Previously Published Works (2021)
                  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 networks have been limited in size by chemically inconsistent graph representations of multi-reactant reactions (e.g. A + B → C) that cannot enforce stoichiometric constraints, precluding the use of optimized shortest-path algorithms. Here, we report a chemically consistent graph architecture that overcomes these limitations using a novel multi-reactant representation and iterative cost-solving procedure. Our approach enables the identification of all low-cost pathways to desired products in massive reaction networks containing reactions of any stoichiometry, allowing for the investigation of vastly more complex systems than previously possible. Leveraging our architecture, we construct the first ever electrochemical reaction network from first-principles thermodynamic calculations to describe the formation of the Li-ion solid electrolyte interphase (SEI), which is critical for passivation of the negative electrode. Using this network comprised of nearly 6000 species and 4.5 million reactions, we interrogate the formation of a key SEI component, lithium ethylene dicarbonate. We automatically identify previously proposed mechanisms as well as multiple novel pathways containing counter-intuitive reactions that have not, to our knowledge, been reported in the literature. We envision that our framework and data-driven methodology will facilitate efforts to engineer the composition-related properties of the SEI - or of any complex chemical process - through selective control of reactivity.
                    Cover page: A chemically consistent graph architecture for massive reaction networks applied to solid-electrolyte interphase formation
                    • Article
                    • Peer Reviewed
                    UC Berkeley Previously Published Works (2023)
                    Out-of-equilibrium electrochemical reaction mechanisms are notoriously difficult to characterize. However, such reactions are critical for a range of technological applications. For instance, in metal-ion batteries, spontaneous electrolyte degradation controls electrode passivation and battery cycle life. Here, to improve our ability to elucidate electrochemical reactivity, we for the first time combine computational chemical reaction network (CRN) analysis based on density functional theory (DFT) and differential electrochemical mass spectroscopy (DEMS) to study gas evolution from a model Mg-ion battery electrolyte─magnesium bistriflimide (Mg(TFSI)2) dissolved in diglyme (G2). Automated CRN analysis allows for the facile interpretation of DEMS data, revealing H2O, C2H4, and CH3OH as major products of G2 decomposition. These findings are further explained by identifying elementary mechanisms using DFT. While TFSI- is reactive at Mg electrodes, we find that it does not meaningfully contribute to gas evolution. The combined theoretical-experimental approach developed here provides a means to effectively predict electrolyte decomposition products and pathways when initially unknown.
                      Cover page: Chemical Reaction Networks Explain Gas Evolution Mechanisms in Mg-Ion Batteries
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