arXiv subjects

Naohiro Fujinuma

Publications and source records attributed to Naohiro Fujinuma.

2 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↗

arXiv2021

Reflections on the future of machine learning for materials research

Applied machine learning (ML) has rapidly spread throughout the physical sciences; in fact, ML-based data analysis and experimental decision-making has become commonplace. We suggest a shift in the conversation from proving that ML can be used to evaluating how to equitably and effectively implement ML for science.We advocate a shift from a "more data, more compute" mentality to a model-oriented approach that prioritizes using machine learning to support the ecosystem of computational models and experimental measurements.We also recommend an open conversation about dataset bias to stabilize productive research through careful model interrogation and deliberate exploitation of known biases. Further, we encourage the community to develop ML methods that connect experiments with theoretical models to increase scientific understanding rather than incrementally optimizing materials. Moreover we envision a future of radical materials innovations enabled by computational creativity tools combined with online visualization and analysis tools that support active outside-the-box thinking inside the scientific knowledge feedback loop. Finally, as a community we must acknowledge ethical issues that can arise from blindly following machine learning predictions and the issues of social equity that will arise if data, code, and computational resources are not readily available to all.

cond-mat.mtrl-sci↗