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MM: Fachverband Metall- und Materialphysik

MM 22: Materials for the Storage and Conversion of Energy

MM 22.2: Vortrag

Mittwoch, 19. März 2025, 16:00–16:15, H23

Splitting Water Without Falling Apart: Accelerating the Understanding of NiFeV LDH via Design of Experiments and Machine Learning — •Juan Manuel Lombardi, Charles Pare, Karsten Reuter, and Christoph Scheurer — Fritz-Haber-Institut der MPG, Berlin

The development of sustainable energy technologies requires efficient, affordable, and durable electrocatalysts. Ni-based layered double hydroxides (LDHs) doped with Fe and V are promising candidates for the oxygen evolution reaction in anion exchange membrane water electrolyzers (AEMWE) due to their tunable structure and exceptional redox properties. However, the mechanisms by which dopants influence catalytic and structural properties are not fully understood, mainly due to the challenges posed by their humongous configurational space. In this study, we efficiently explore the thermally accessible configurations within the complex configurational space of γ-phase Ni LDHs doped with Fe and V. To maximize information gain, we sample the composition space using Design of Experiments (DoE), leverage machine learning interatomic potentials (MLIPs) to sample these configurations, and refine the results with Density Functional Theory (DFT) calculations for first-principles quality predictions. By combining these tools, we develop an optimal protocol to elucidate how the dopants influence material properties. This integrated methodology reveals pathways for optimizing NiFeV LDH compositions, enhancing energy conversion efficiencies while ensuring long-term stability.

Keywords: DoE; DFT; MLIP; layered double hydroxide; LDH