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The stripping reaction of lithium will greatly impact the cyclability and safety of Li metal batteries. However, lithium pits' nucleation and growth, the origin of uneven stripping, are still poorly understood. In this study, we analyze the nucleation mechanism of Li pits and their morphology evolution with a large population and electrode area (>0.45 cm²). We elucidate the dependence of pit size and density on current density and overpotential, which is aligned with classical nucleation theory. With laser scanning confocal microscope, we reveal the preferential stripping on certain crystal grains and a new stripping mode between pure pitting and stripping without pitting. Descriptors like circularity and the aspect ratio (R) of pit radius to depth are used to quantify the evolution of lithium pits in three dimensions. As pits grow, growth predominates along the through-plane direction, surpassing the expanding rate at the in-plane direction. After analyzing more than 1000 pits at each condition, we validate that the overpotential is inversely related to the pit radius and exponentially related to the rate of nucleation. With this established nucleation-overpotential relationship, we can better understand and predict the evolution of surface area and roughness of lithium electrodes under different stripping conditions. The knowledge and methodology developed in this work will significantly benefit lithium metal batteries' charging/discharging profile design and the assessment of large-scale lithium metal foils. DOI of publication: 10.1021/acsami.4c01530 Acknowledgments: H.Z., M.U., and F.S. thank the support from the Assistant Secretary for Energy Efficiency and Renewable Energy, Office of Vehicle Technologies of the US Department of Energy through the Advanced Battery Materials Research Program. F.S. thanks the support from the National Science Foundation under Grant No. 2239690. 

Quantitative Relative Judgment Aggregation (QRJA) is a new research topic in (computational) social choice. In the QRJA model, agents provide judgments on the relative quality of different candidates, and the goal is to aggregate these judgments across all agents. In this work, our main conceptual contribution is to explore the interplay between QRJA in a social choice context and its application to ranking prediction. We observe that in QRJA, judges do not have to be people with subjective opinions; for example, a race can be viewed as a "judgment" on the contestants' relative abilities. This allows us to aggregate results from multiple races to evaluate the contestants' true qualities. At a technical level, we introduce new aggregation rules for QRJA and study their structural and computational properties. We evaluate the proposed methods on data from various real races and show that QRJA-based methods offer effective and interpretable ranking predictions. 

Designing the solid–electrolyte interphase (SEI) is critical for stable, fast-charging, low-temperature Li-ion batteries. Fostering a “fluorinated interphase,” SEI enriched with LiF, has become a popular design strategy. Although LiF possesses low Li-ion conductivity, many studies have reported favorable battery performance with fluorinated SEIs. Such a contradiction suggests that optimizing SEI must extend beyond chemical composition design to consider spatial distributions of different chemical species. In this work, we demonstrate that the impact of a fluorinated SEI on battery performance should be evaluated on a case-by-case basis. Sufficiently passivating the anode surface without impeding Li-ion transport is key. We reveal that a fluorinated SEI containing excessive and dense LiF severely impedes Li-ion transport. In contrast, a fluorinated SEI with well-dispersed LiF (i.e., small LiF aggregates well mixed with other SEI components) is advantageous, presumably due to the enhanced Li-ion transport across heterointerfaces between LiF and other SEI components. An electrolyte, 1 M LiPF₆in 2-methyl tetrahydrofuran (2MeTHF), yields a fluorinated SEI with dispersed LiF. This electrolyte allows anodes of graphite, μSi/graphite composite, and pure Si to all deliver a stable Coulombic efficiency of 99.9% and excellent rate capability at low temperatures. Pouch cells containing layered cathodes also demonstrate impressive cycling stability over 1,000 cycles and exceptional rate capability down to −20 °C. Through experiments and theoretical modeling, we have identified a balanced SEI-based approach that achieves stable, fast-charging, low-temperature Li-ion batteries. 

Bilateral trade is one of the most natural and important forms of economic interaction: A seller has a single, indivisible item for sale, and a buyer is potentially interested. The two parties typically have different, privately known valuations for the item, and ideally, they would like to trade if the buyer values the item more than the seller. The celebrated impossibility result by Myerson and Satterthwaite shows that any mechanism for this setting must violate at least one important desideratum. In this paper, we investigate a richer paradigm of bilateral trade, with many self-interested buyers and sellers on both sides of a single trade who cannot be excluded from the trade. We show that this allows for more positive results. In fact, we establish a dichotomy in the possibility of trading efficiently. If in expectation, the buyers value the item more, we can achieve efficiency in the limit. If this is not the case, then efficiency cannot be achieved in general. En route, we characterize trading mechanisms that encourage truth-telling, which may be of independent interest. We also evaluate our trading mechanisms experimentally, and the experiments align with our theoretical results. 

Institutional support is crucial for the successful career advancement of all faculty but in particular those who are women. Evolving from the past, in which gender disparities were prevalent in many institutions, recent decades have witnessed significant progress in supporting the career advancement of women faculty in science and academic medicine. However, continued advancement is necessary as previously unrecognized needs and new opportunities for improvement emerge. To identify the needs, opportunities, and potential challenges encountered by women faculty, the Women’s Leadership Committee of the Arteriosclerosis, Thrombosis, and Vascular Biology Council developed an initiative termed GROWTH (Generating Resources and Opportunities for Women in Technology and Health). The committee designed a survey questionnaire and interviewed 19 leaders with roles and responsibilities in faculty development from a total of 12 institutions across various regions of the United States. The results were compiled, analyzed, and discussed. Based on our interviews and analyses, we present the current status of these representative institutions in supporting faculty development, highlighting efforts specific to women faculty. Through the experiences, insights, and vision of these leaders, we identified success stories, challenges, and future priorities. Our article provides a primer and a snapshot of institutional efforts to support the advancement of women faculty. Importantly, this article can serve as a reference and resource for academic entities seeking ideas to gauge their commitment level to women faculty and to implement new initiatives. Additionally, this article can provide guidance and strategies for women faculty as they seek support and resources from their current or prospective institutions when pursuing new career opportunities. 

We study equilibrium computation with extensive-form correlation in two-player turn-taking stochastic games. Our main results are two-fold: (1) We give an algorithm for computing a Stackelberg extensive-form correlated equilibrium (SEFCE), which runs in time polynomial in the size of the game, as well as the number of bits required to encode each input number. (2) We give an efficient algorithm for approximately computing an optimal extensive-form correlated equilibrium (EFCE) up to machine precision, i.e., the algorithm achieves approximation error 𝜀 in time polynomial in the size of the game, as well as log(1/𝜀). Our algorithm for SEFCE is the first polynomial-time algorithm for equilibrium computation with com- mitment in such a general class of stochastic games. Existing algorithms for SEFCE typically make stronger assumptions such as no chance moves, and are designed for extensive-form games in the less succinct tree form. Our algorithm for approximately optimal EFCE is, to our knowledge, the first algorithm that achieves 3 desiderata simultaneously: approximate optimality, polylogarithmic dependency on the approximation error, and compatibility with stochastic games in the more succinct graph form. Existing algorithms achieve at most 2 of these desiderata, often also relying on additional technical assumptions. 

We consider the problem of planning with participation constraints introduced in [24]. In this problem, a principal chooses actions in a Markov decision process, resulting in separate utilities for the principal and the agent. However, the agent can and will choose to end the process whenever his expected onward utility becomes negative. The principal seeks to compute and commit to a policy that maximizes her expected utility, under the constraint that the agent should always want to continue participating. We provide the first polynomial-time exact algorithm for this problem for finite-horizon settings, where previously only an additive ε-approximation algorithm was known. Our approach can also be extended to the (discounted) infinite-horizon case, for which we give an algorithm that runs in time polynomial in the size of the input and log(1/ε), and returns a policy that is optimal up to an additive error of ε. 

We pose and study the problem of planning in Markov decision processes (MDPs), subject to participation constraints as studied in mechanism design. In this problem, a planner must work with a self-interested agent on a given MDP. Each action in the MDP provides an immediate reward to the planner and a (possibly different) reward to the agent. The agent has no control in choosing the actions, but has the option to end the entire process at any time. The goal of the planner is to find a policy that maximizes her cumulative reward, taking into consideration the agent's ability to terminate. We give a fully polynomial-time approximation scheme for this problem. En route, we present polynomial-time algorithms for computing (exact) optimal policies for important special cases of this problem, including when the time horizon is constant, or when the MDP exhibits a "definitive decisions" property. We illustrate our algorithms with two different game-theoretic applications: the problem of assigning rides in ride-sharing and the problem of designing screening policies. Our results imply efficient algorithms for computing (approximately) optimal policies in both applications.