Phase-Field Simulations of Lithium Dendrite Growth with Open-Source Software

Dendrite growth is a long-standing challenge that has limited the applications of rechargeable lithium metal electrodes. Here, we have developed a grand potential-based nonlinear phase-field model to study the electrodeposition of lithium as relevant for a lithium metal anode, using open-source software package MOOSE. The dynamic morphological evolution under a large/small overpotential is studied in two dimensions, revealing important dendrite growth/stable deposition patterns. The corresponding temporal–spatial distributions of ion concentration, overpotential, and driving force are studied, which demonstrate an intimate, dynamic competition between ion transport and electrochemical reactions, resulting in vastly different growth patterns. On the basis of the understanding from this model, we propose a “compositionally graded electrolyte” with higher local ion concentration as a way to potentially suppress dendrite formation. Given the importance of morphological evolution for lithium metal electrodes, widespread applications of phase-field models have been limited in part due to in-house or proprietary software. In order to spur growth of this field, we make all files available to enable future studies to study the many unsolved aspects related to morphology evolution of lithium metal electrodes

Phase-Field Simulations of Lithium Dendrite Growth with Open-Source Software

Dendrite growth is a long-standing challenge that has limited the applications of rechargeable lithium metal electrodes. Here, we have developed a grand potential-based nonlinear phase-field model to study the electrodeposition of lithium as relevant for a lithium metal anode, using open-source software package MOOSE. The dynamic morphological evolution under a large/small overpotential is studied in two dimensions, revealing important dendrite growth/stable deposition patterns. The corresponding temporal–spatial distributions of ion concentration, overpotential, and driving force are studied, which demonstrate an intimate, dynamic competition between ion transport and electrochemical reactions, resulting in vastly different growth patterns. On the basis of the understanding from this model, we propose a “compositionally graded electrolyte” with higher local ion concentration as a way to potentially suppress dendrite formation. Given the importance of morphological evolution for lithium metal electrodes, widespread applications of phase-field models have been limited in part due to in-house or proprietary software. In order to spur growth of this field, we make all files available to enable future studies to study the many unsolved aspects related to morphology evolution of lithium metal electrodes

Phase-Field Simulations of Lithium Dendrite Growth with Open-Source Software

Dendrite growth is a long-standing challenge that has limited the applications of rechargeable lithium metal electrodes. Here, we have developed a grand potential-based nonlinear phase-field model to study the electrodeposition of lithium as relevant for a lithium metal anode, using open-source software package MOOSE. The dynamic morphological evolution under a large/small overpotential is studied in two dimensions, revealing important dendrite growth/stable deposition patterns. The corresponding temporal–spatial distributions of ion concentration, overpotential, and driving force are studied, which demonstrate an intimate, dynamic competition between ion transport and electrochemical reactions, resulting in vastly different growth patterns. On the basis of the understanding from this model, we propose a “compositionally graded electrolyte” with higher local ion concentration as a way to potentially suppress dendrite formation. Given the importance of morphological evolution for lithium metal electrodes, widespread applications of phase-field models have been limited in part due to in-house or proprietary software. In order to spur growth of this field, we make all files available to enable future studies to study the many unsolved aspects related to morphology evolution of lithium metal electrodes

Phase-Field Simulations of Lithium Dendrite Growth with Open-Source Software

Dendrite growth is a long-standing challenge that has limited the applications of rechargeable lithium metal electrodes. Here, we have developed a grand potential-based nonlinear phase-field model to study the electrodeposition of lithium as relevant for a lithium metal anode, using open-source software package MOOSE. The dynamic morphological evolution under a large/small overpotential is studied in two dimensions, revealing important dendrite growth/stable deposition patterns. The corresponding temporal–spatial distributions of ion concentration, overpotential, and driving force are studied, which demonstrate an intimate, dynamic competition between ion transport and electrochemical reactions, resulting in vastly different growth patterns. On the basis of the understanding from this model, we propose a “compositionally graded electrolyte” with higher local ion concentration as a way to potentially suppress dendrite formation. Given the importance of morphological evolution for lithium metal electrodes, widespread applications of phase-field models have been limited in part due to in-house or proprietary software. In order to spur growth of this field, we make all files available to enable future studies to study the many unsolved aspects related to morphology evolution of lithium metal electrodes

Phase-Field Simulations of Lithium Dendrite Growth with Open-Source Software

Dendrite growth is a long-standing challenge that has limited the applications of rechargeable lithium metal electrodes. Here, we have developed a grand potential-based nonlinear phase-field model to study the electrodeposition of lithium as relevant for a lithium metal anode, using open-source software package MOOSE. The dynamic morphological evolution under a large/small overpotential is studied in two dimensions, revealing important dendrite growth/stable deposition patterns. The corresponding temporal–spatial distributions of ion concentration, overpotential, and driving force are studied, which demonstrate an intimate, dynamic competition between ion transport and electrochemical reactions, resulting in vastly different growth patterns. On the basis of the understanding from this model, we propose a “compositionally graded electrolyte” with higher local ion concentration as a way to potentially suppress dendrite formation. Given the importance of morphological evolution for lithium metal electrodes, widespread applications of phase-field models have been limited in part due to in-house or proprietary software. In order to spur growth of this field, we make all files available to enable future studies to study the many unsolved aspects related to morphology evolution of lithium metal electrodes

Identifying Descriptors for Solvent Stability in Nonaqueous Li–O₂ Batteries

One crucial challenge in developing rechargeable Li–O₂ batteries is to identify a stable solvent that is resistant to decomposition in the electrochemical environment of Li₂O₂. We attempt to identify descriptors that could be used to test for solvent stability. We build on the recent quantitative experimental results on oxygen consumption and release during discharge and charge respectively. We limit our focus to understanding trends in oxidative stability of solvents and based on a systematic treatment of the electrochemical environment of Li₂O₂, we propose that, to a first approximation, the highest occupied molecular orbital (HOMO) level could be a good descriptor. We demonstrate that this descriptor correlates well with the experimentally measured degree of rechargeability. We utilize this descriptor to screen a large number of solvents and identify several solvents that could enhance the rechargeability of nonaqueous Li–O₂ batteries. We provide a comprehensive compilation of available computational and experimental data of several key solvent parameters that we believe will be the genesis for an ‘electrolyte genome’

Trade-Offs in Capacity and Rechargeability in Nonaqueous Li–O₂ Batteries: Solution-Driven Growth versus Nucleophilic Stability

The development of high-capacity rechargeable Li–O₂ batteries requires the identification of stable solvents that can promote a solution-based discharge mechanism, which has been shown to result in higher discharge capacities. Solution-driven discharge product growth requires dissolution of the adsorbed intermediate LiO₂*, thus generating solvated Li⁺ and O₂^– ions. Such a mechanism is possible in solvents with high Gutmann donor or acceptor numbers. However, O₂^– is a strong nucleophile and is known to attack solvents via proton/hydrogen abstraction or substitution. This kind of a parasitic process is extremely detrimental to the battery’s rechargeability. In this work, we develop a thermodynamic model to describe these two effects and demonstrate an anticorrelation between solvents’ stability and their ability to enhance capacity via solution-mediated discharge product growth. We analyze the commonly used solvents in the same framework and describe why solvents that can promote higher discharge capacity are also prone to degradation. Solvating additives for practical Li–O₂ batteries will have to be outliers to this observed anticorrelation

Solvent Degradation in Nonaqueous Li‑O₂ Batteries: Oxidative Stability versus H‑Abstraction

Developing rechargeable Li-O₂ batteries hinges on identifying stable solvents resistant to decomposition. Here, we focus on solvent stability against adsorption-induced H-abstraction during discharge. Using a detailed thermodynamic analysis, we show that a solvent’s propensity to resist H-abstraction is determined by its acid dissociation constant, p<i>K</i>_a, in its own environment. Upon surveying hundreds of solvents for their p<i>K</i>_a values in different media, we find linear correlations between the p<i>K</i>_a values across various classes of solvents in any two given media. Utilizing these correlations, we choose DMSO as the common standard to compare the relative stability trends. We construct a stability plot based on the solvent’s HOMO level and its p<i>K</i>_a in DMSO, which reveals that most solvents obey a correlation where solvents with lower HOMO levels tend to have lower p<i>K</i>_a values in DMSO. However, this is at odds with the stability requirement that demands deep HOMO levels and high p<i>K</i>_a values. Thus, stable solvents need to be outliers to this observed correlation

Identifying Descriptors for Solvent Stability in Nonaqueous Li–O₂ Batteries

One crucial challenge in developing rechargeable Li–O₂ batteries is to identify a stable solvent that is resistant to decomposition in the electrochemical environment of Li₂O₂. We attempt to identify descriptors that could be used to test for solvent stability. We build on the recent quantitative experimental results on oxygen consumption and release during discharge and charge respectively. We limit our focus to understanding trends in oxidative stability of solvents and based on a systematic treatment of the electrochemical environment of Li₂O₂, we propose that, to a first approximation, the highest occupied molecular orbital (HOMO) level could be a good descriptor. We demonstrate that this descriptor correlates well with the experimentally measured degree of rechargeability. We utilize this descriptor to screen a large number of solvents and identify several solvents that could enhance the rechargeability of nonaqueous Li–O₂ batteries. We provide a comprehensive compilation of available computational and experimental data of several key solvent parameters that we believe will be the genesis for an ‘electrolyte genome’

Quantifying Uncertainty in Activity Volcano Relationships for Oxygen Reduction Reaction

The oxygen reduction reaction (ORR) is an important electrochemical reaction and a major bottleneck for fuel cells. Due to the existence of a scaling relation between the adsorption energies of two key intermediates involved in ORR, OOH*, and OH*, the electrocatalytic activity for the ORR, to a first approximation, is determined by a single descriptor. This descriptor-based approach has been used to screen for electrocatalyst materials that have an optimal binding energy of oxygen intermediates. However, given that this descriptor-based search relies on several approximations, it is crucial to determine the overall predictability of the descriptor-based model to determine the activity of a catalyst. In this work, we develop a formalism for estimating uncertainty for the activity of a catalyst in an electrocatalytic reaction scheme and apply this framework to determine errors involved in describing the ORR activity. We perform density functional theory calculations using the Bayesian Error Estimation Functional with van der Waals exchange–correlation functional to determine the adsorption energies of ORR intermediates on transition-metal fcc(111) and fcc(100) facets. We show that the error estimates for the adsorption energies calculated with a reference metal surface, chosen here to be Pt(111), are much smaller than those calculated with gas-phase molecules as reference. We demonstrate that Δ<i>G</i>_OH and Δ<i>G</i>_OOH are the optimal descriptors for the 4e^– and the 2e^– ORR, respectively. We show that for the 4e^– ORR with Δ<i>G</i>_OH as the descriptor, the uncertainty in activity is determined by the error associated with the adsorption energy of OH* (∼0.1 eV) for materials that lie on the strong binding leg, and the error involved in the scaling relation between OOH* and OH* (∼0.2 eV) determines the uncertainty in activity for the weak binding leg. We propose a parameter, the expected limiting potential, <i>U</i>_EL, which is the expected value of <i>U</i>_L. The deviation of the expected limiting potential, <i>U</i>_EL, from the thermodynamic limiting potential, <i>U</i>_L, provides a qualitative estimate of the prediction error and can be used to identify trends in predictability. We believe that the concept of the expected limiting potential will be crucial in descriptor-based screening studies for multielectron electrochemical reactions