arXiv subjects

Felix Pfeiffer

Publications and source records attributed to Felix Pfeiffer.

4 recordsLinked to original sources

arXiv2026

Fundamental picture of the conduction mechanism in solid-state polymer electrolytes revealed by terahertz spectroscopy

Solid polymer electrolytes (SPEs) based on cross-linked poly(ethylene oxide) (PEO) encompassing lithium salts have gained significant attention as separators in solid-state lithium metal batteries. Here, we employ terahertz time-domain spectroscopy (THz-TDS), as a noninvasive contact-free technique, to investigate the conduction properties of these cross-linked SPEs and unravel their dependencies on the added lithium salt and the sample temperature. The obtained THz conductivity spectra are dominated by THz absorption bands, which we attribute to resonant vibrations within the polymer matrix of the electrolyte. By careful application of Lorentz model, the conductivity spectra have been analyzed, and the relevant polymer vibration modes have been quantitatively assessed. Calculations based on the density functional theory (DFT) were performed to elucidate the possible microscopic mechanisms of these resonant vibrations. This study sheds light on the relevance of polymer matrix vibrations validating the hopping transport of lithium ions in SPEs which ultimately leads to the technologically relevant ionic conduction in the solid-state polymer-based electrolytes.

cond-mat.soft↗

arXiv2024

Simultaneous Training of First- and Second-Order Optimizers in Population-Based Reinforcement Learning

The tuning of hyperparameters in reinforcement learning (RL) is critical, as these parameters significantly impact an agent's performance and learning efficiency. Dynamic adjustment of hyperparameters during the training process can significantly enhance both the performance and stability of learning. Population-based training (PBT) provides a method to achieve this by continuously tuning hyperparameters throughout the training. This ongoing adjustment enables models to adapt to different learning stages, resulting in faster convergence and overall improved performance. In this paper, we propose an enhancement to PBT by simultaneously utilizing both first- and second-order optimizers within a single population. We conducted a series of experiments using the TD3 algorithm across various MuJoCo environments. Our results, for the first time, empirically demonstrate the potential of incorporating second-order optimizers within PBT-based RL. Specifically, the combination of the K-FAC optimizer with Adam led to up to a 10% improvement in overall performance compared to PBT using only Adam. Additionally, in environments where Adam occasionally fails, such as the Swimmer environment, the mixed population with K-FAC exhibited more reliable learning outcomes, offering a significant advantage in training stability without a substantial increase in computational time.

cs.LG↗

arXiv2024

Attention Mechanisms Don't Learn Additive Models: Rethinking Feature Importance for Transformers

We address the critical challenge of applying feature attribution methods to the transformer architecture, which dominates current applications in natural language processing and beyond. Traditional attribution methods to explainable AI (XAI) explicitly or implicitly rely on linear or additive surrogate models to quantify the impact of input features on a model's output. In this work, we formally prove an alarming incompatibility: transformers are structurally incapable of representing linear or additive surrogate models used for feature attribution, undermining the grounding of these conventional explanation methodologies. To address this discrepancy, we introduce the Softmax-Linked Additive Log Odds Model (SLALOM), a novel surrogate model specifically designed to align with the transformer framework. SLALOM demonstrates the capacity to deliver a range of insightful explanations with both synthetic and real-world datasets. We highlight SLALOM's unique efficiency-quality curve by showing that SLALOM can produce explanations with substantially higher fidelity than competing surrogate models or provide explanations of comparable quality at a fraction of their computational costs. We release code for SLALOM as an open-source project online at https://github.com/tleemann/slalom_explanations.

cs.LG↗

arXiv2023

A review and outlook on anionic and cationic redox in Ni-, Li- and Mn-rich layered oxides LiMeO2 (Me = Li, Ni, Co, Mn)

The present work reviews the charge compensation in Ni based layered oxides (LiNi1-xMexO2 with x <= 0.2, Me = Co, Mn, space group R-3m) relating performance parameters to changes in the electronic and crystallographic structure of the cathode materials. Upon charge and discharge two fundamentally different redox mechanisms are observed: At low and medium states of charge (SOCs) charge compensation takes mainly place at oxygen sites while electron density is shifted from the oxygen lattice to nickel (formation of sigma bonds). At high SOCs the shift of electron density from the transition metals to oxygen (formation of pi bonds) enables an additional redox process but also oxygen release from the transition metal host structure and subsequent detrimental reactions. Depending on the Ni:Co:Mn content, both processes lead to characteristic features in the voltage profile of the cathode materials and performance parameters like the capacity, the cycling stability and the open cell voltage become a function of the composition.

cond-mat.mtrl-sci↗