TY - JOUR A1 - Zhang, Runze A1 - Sur, Debashish A1 - Li, Kangming A1 - Witt, Julia A1 - Black, Robert A1 - Whittingham, Alexander A1 - Scully, John R. A1 - Hattrick-Simpers, Jason T1 - Bayesian assessment of commonly used equivalent circuit models for corrosion analysis in electrochemical impedance spectroscopy N2 - Electrochemical Impedance Spectroscopy (EIS) is a crucial technique for assessing corrosion of metallic materials. The analysis of EIS hinges on the selection of an appropriate equivalent circuit model (ECM) that accurately characterizes the system under study. In this work, we systematically examined the applicability of three commonly used ECMs across several typical material degradation scenarios. By applying Bayesian Inference to simulated corrosion EIS data, we assessed the suitability of these ECMs under different corrosion conditions and identified regions where the EIS data lacks sufficient information to statistically substantiate the ECM structure. Additionally, we posit that the traditional approach to EIS analysis, which often requires measurements to very low frequencies, might not be always necessary to correctly model the appropriate ECM. Our study assesses the impact of omitting data from low to medium-frequency ranges on inference results and reveals that a significant portion of low-frequency measurements can be excluded without substantially compromising the accuracy of extracting system parameters. Further, we propose simple checks to the posterior distributions of the ECM components and posterior predictions, which can be used to quantitatively evaluate the suitability of a particular ECM and the minimum frequency required to be measured. This framework points to a pathway for expediting EIS acquisition by intelligently reducing low-frequency data collection and permitting on-the-fly EIS measurements. KW - Electrochemical Impedance Spectroscopy (EIS) KW - MAPz@BAM KW - Bayesian Inference KW - Corrosion PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-623392 DO - https://doi.org/10.1038/s41529-024-00537-8 VL - 8 SP - 120 PB - Springer Materials AN - OPUS4-62339 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Hattrick-Simpers, J. A1 - Li, K. A1 - Greenwood, M. A1 - Black, R. A1 - Witt, Julia A1 - Kozdras, M. A1 - Pang, X. A1 - Özcan Sandikcioglu, Özlem T1 - Designing durable, sustainable, high-performance materials for clean energy infrastructure N2 - Civilization and modern societies would not be possible without manmade materials. Considering their production volumes, their supporting role in nearly all industrial processes, and the impact of their sourcing and production on the environment, metals and alloys are and will be of prominent importance for the clean energy transition. The focus of materials discovery must move to more specialized, application-tailored green alloys that outperform the legacy materials not only in performance but also in sustainability and resource efficiency. This white paper summarizes a joint Canadian-German initiative aimed at developing a materials acceleration platform (MAP) focusing on the discovery of new alloy families that will address this challenge. We call our initiative the “Build to Last Materials Acceleration Platform” (B2L-MAP) and present in this perspective our concept of a three-tiered self-driving laboratory that is composed of a simulation-aided pre-selection module (B2L-select), an artificial intelligence (AI)-driven experimental lead generator (B2L-explore), and an upscaling module for durability assessment (B2L-assess). The resulting tool will be used to identify and subsequently demonstrate novel corrosion-resistant alloys at scale for three key applications of critical importance to an offshore, wind-driven hydrogen plant (reusable electrical contacts, offshore infrastructure, and oxygen evolution reaction catalysts). KW - Material Acceleration Platforms (MAPs) KW - Self-driving-labs (SDLs) KW - Automation KW - Artificial Intelligence (AI) KW - Elektrolyse KW - Structural Materials KW - Corrosion PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-568452 DO - https://doi.org/10.1016/j.xcrp.2022.101200 SN - 2666-3864 VL - 4 IS - 1 SP - 1 EP - 11 PB - Cell Press ; Elsevier CY - Maryland Heights, MO AN - OPUS4-56845 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -