arXiv subjects

Ashley Dale

Publications and source records attributed to Ashley Dale.

3 recordsLinked to original sources

arXiv2026

Accelerating Electrochemical Impedance Spectroscopy Measurements by Reducing Reliance on Noisy Low-Frequency Data

Electrochemical impedance spectroscopy (EIS) is a powerful tool for probing kinetic and transport processes in electrochemical systems, but its practical use is often limited by the long acquisition time and noise sensitivity of low-frequency measurements. Here, we present a statistical inference-assisted framework that reduces the reliance on low-frequency sampling by increasing the sampling density in cleaner high-frequency regions. Using AutoEIS and Bayesian inference, the augmented high-frequency data are fitted to selected equivalent circuit models (ECMs) to reconstruct the full impedance spectrum and quantify parameter uncertainty. To evaluate reconstruction performance, we introduce the critical frequency (fc), defined as the highest cutoff frequency at which the full EIS response can still be reliably recovered. The results show that additional high-frequency sampling can not only shift fc to higher values but also reduce the total measurement time. The specific improvement depends on the noise level of the EIS data, the number of added data points, and the ECM structure. Overall, this work provides a practical framework for designing fast and efficient EIS data acquisition and offers guidance for applying partial-frequency EIS reconstruction in real electrochemical characterization.

physics.data-an↗

arXiv2026

Building informative materials datasets beyond targeted objectives

Materials science data collection can be expensive, making the reuse and long-term utility of datasets critical important for future discovery campaigns. In practice, researchers prioritize a subset of properties due to research interests. However, ignoring a subset of outcomes in data collection campaigns potentially generate datasets poorly suited for future learning tasks. Here, we present a framework for dataset construction that maximizes informativeness for target properties of interest while preserving performance on untargeted ones. Our approach uses diversity-aware selection to ensure broad coverage of the materials space. In noisy experimental dataset construction, we find that without our diversity-aware framework, prediction performance on untargeted properties can degrade by up to 40% relative to random sampling, whereas applying our framework yields improvements of up to 10% . For targeted properties, performance can degrade with respect to random sampling by up to 12.5% without diversity, while our framework achieves gains of up to 25%. Incorporating diversity into dataset construction not only preserves informativeness for the targeted properties, but also improves materials coverage for potential future objectives. As a result, the constructed datasets remain broadly informative across considered and unconsidered outcomes, ensuring unbiased quality entries and mitigating cold-start limitations in subsequent modeling and discovery campaigns.

cond-mat.mtrl-sci↗

arXiv2026

Materials Acceleration Platform for Electrochemistry: a Platform for Autonomous Electrochemistry

Corrosion testing is slow, labor-intensive, and sensitive to operator technique, limiting the generation of large, high-quality datasets for data-driven materials discovery. The Materials Acceleration Platform for Electrochemistry (MAP-E) is an autonomous, high-throughput system, capable of performing parallel electrochemical experiments. It integrates robotic liquid handling, sample transfer with a multi-channel potentiostatic control to extract corrosion metrics without human intervention. Validation against an ASTM G61-analog benchmark demonstrates good reproducibility, with a standard deviation of 75 mV in pitting potential across 32 automated measurements. The platform was then employed to autonomously construct pH-chloride stability diagrams for 304 stainless steel using an uncertainty-driven sampling strategy on a Gaussian process surrogate model. This approach reduces operator involvement and accelerates the exploration of environmental spaces. The MAP-E establishes a framework for autonomous electrochemical experimentation, enabling generation of corrosion datasets that inform materials discovery, alloy design, and durability assessment in service environments.

cond-mat.mtrl-sci↗