Nicolas de Andrade Ishiki

High‐entropy alloys (HEAs) are highly promising electrocatalysts, but the vastness of their configurational space severely limits traditional screening approaches. To overcome this challenge, this article proposes a conceptual and methodological paradigm shift toward “Inverse Design” (“property‐to‐material”) by introducing the architecture of REACT2COMPO, a neural network framework designed to deduce the optimal composition and atomic distribution of a nanocatalyst directly from targeted macroscopic catalytic properties. To address the lack of experimental 3D structural data required for its training, we introduce a second network, ATOMOD. This model leverages a multi‐fidelity “Sim‐to‐Real” learning strategy: trained exclusively using in silico generated data, it implicitly reconstructs the 3D geometry of the nanoparticle, layer by layer, from a single 2D transmission electron microscopy (TEM) image. Our results demonstrate the feasibility of achieving accurate 3D geometric reconstruction using TEM while highlighting the need for complementary approaches to resolve elements with similar electron scattering cross‐sections. These findings underscore the importance of multimodal data fusion, integrating TEM with complementary techniques such as extended X‐ray absorption fine structure spectroscopy (EXAFS). While the geometric reconstruction from TEM is already operational, this multimodal strategy opens promising perspectives for overcoming the remaining challenges and achieving full 3D chemical resolution. While the geometric reconstruction from TEM is operational, full 3D chemical resolution remains a challenge. This study lays the theoretical and database foundations for a multimodal fusion integrating EXAFS. Once fully implemented, this multi‐scale framework opens a practical pathway to accelerate the discovery of new functional materials.

Alkali cations substantially affect the kinetics of electrocatalytic reactions such as CO2 reduction and the alkaline hydrogen evolution reaction (HER). In this work, we investigate the effect of the electrolyte through grand-canonical density functional theory (GC-DFT) in conjunction with Quantum Mechanics/Molecular Mechanics (QM/MM) simulations and validate these results with electrochemical experiments of alkaline HER on commercially relevant and benchmark electrocatalysts, Ni and Pt, respectively. Our results show that the cation trend of alkaline hydrogen evolution depends on the electrocatalyst and is reflected in the activation barrier for water dissociation, studied as a proxy for the Volmer step. Meanwhile, the stability of *H and *OH does not depend significantly on the electrolyte cation.

While Fe–N–C materials have shown promising initial oxygen reduction reaction (ORR) activity, they lack durability in acidic medium. Key degradation mechanisms include FeN₄ site demetallation and deactivation by reactive oxygen species. Here we show for mainstream Fe–N–Cs that adding 1 wt.% Pt nanoparticles via a soft polyol method results in well-defined and stable Pt/Fe–N–C hybrids. The Pt addition strongly reduces the H₂O₂ production and Fe leaching rate during ORR, while post mortem Mössbauer spectroscopy reveals that the highly active but unstable Fe(III)N₄ site is partially stabilized. The similar H₂O₂ electroreduction activity of Pt/Fe–N–C and Fe–N–C and other analyses point toward a long-distance electronic effect of Pt nanoparticles in stabilizing FeN₄ sites. Computational chemistry reveals that spin polarization of distant Pt atoms mitigates the structural changes of FeN₄ sites upon adsorption of oxygenated species atop Fe, especially in high-spin state.

Electrochemical techniques have been extensively employed across various fields of research due to their pivotal role in advancing the clean energy transition. This interdisciplinary integration, however, requires special attention to the fundamental aspects of electrochemistry. As the number of researchers adopting these techniques continues to grow — whether they are trained electrochemists or not — the need for standardize parameters and terminology has become increasingly evident in recent years. Moreover, the rise of artificial intelligence applied to science emphasizes even more the importance of such normalizations. This text demonstrates the divergent practices in the representation of reference scales in electrochemical results and initiates a discussion on the need for their standardization.

This paper investigates how the aggregation of bimetallic nanoparticles (NPs) influences the electronic condition of the surface adsorption site and, hence, the performance of materials during the electrooxidation of fuels in an alkaline medium. First, we synthesized Pt-Ni NPs in three configurations: ordered intermetallic, ordinary alloy, and core-shell. The NPs contained Pt and Ni close to a 1:1 Pt/Ni atomic ratio. They had similar particle sizes, which allowed us to evaluate their performance without the influence of these physical parameters. Depending on the structural arrangement of the Pt and Ni atoms in the NP, the electronic condition of the surface adsorption site (Pt) changed significantly. Consequently, the performance of the materials varied whenever they were used as anode material for the electrooxidation of hydrogen, methanol, ethanol, ethylene glycol, and glycerol in an alkaline solution. The electronic condition of the surface site strongly affected the adsorption characteristics of the reactants, intermediates, and products, consequently impacting the material's performance during the electrochemical processes. The approach adopted here could contribute to a better understanding of electrocatalytic processes and the design of selective electrocatalysts.