Jul 2026· Plasma Science and Technology· 0 citations
TL;DR
This work demonstrates the effectiveness of the ML-GA approach in overcoming data limitations and accelerating the design of high-performance OER catalysts with remarkable OER activity with low overpotentials.
Abstract
Developing efficient nickel–iron-based Oxygen Evolution Reaction (OER) catalysts via plasma-assisted electrodeposition holds great promise for the green hydrogen economy due to its compatibility with large-scale industrial manufacturing. However, the vast catalyst design space remains largely unexplored due to the inefficiency of traditional trial-and-error investigation. Herein, we develop a Machine Learning-guided Genetic Algorithm (ML-GA) paradigm with two complementary modes: exploration and exploitation. The exploration mode prioritizes population diversity while maintaining promising predicted performance to broadly sample the chemical space, while the exploitation mode focuses on high-performance regions to identify promising catalysts. Guided by this framework, NiFe/NiS catalysts were prioritized and synthesized via plasma-assisted electrodeposition, which demonstrated remarkable OER activity with low overpotentials of 219 mV and 315 mV at current densities of 10 mA cm
−2
and 1000 mA cm
−2
, respectively. This work demonstrates the effectiveness of the ML-GA approach in overcoming data limitations and accelerating the design of high-performance OER catalysts.
Ru‐based solid‐solution alloys have emerged as attractive substitutes for Pt‐based electrocatalysts in the alkaline hydrogen evolution reaction (HER). However, the vast compositional space of these alloys hinders rapid catalyst discovery. Existing machine‐learning design strategies are largely based on idealized simulation data, limiting their experimental relevance and screening efficiency. Herein, we develop an interpretable machine‐learning framework informed by experiment to facilitate the rapid screening of Ru‐based HER electrocatalysts. Built on an experimental dataset, the framework integrates electronic descriptors, interpretability analysis, a Bayesian‐optimized surrogate model and uncertainty‐aware ranking to efficiently identify promising compositions. Interpretability analysis identifies V, Co, and Ru as the key elements governing catalytic performance, whereas the surrogate model maps HER activity across the ternary alloy space to define a confined high‐performance composition window. Guided by this framework, the optimized V
17
Co
31
Ru
52
catalyst exhibits an overpotential of 61.7 mV at a current density of 100 mA·cm
−2
in 1 M KOH, while maintaining stable operation for 260 h under the same current density. This work provides a rapid, interpretable and experimentally relevant strategy for discovering high‐performance alkaline HER electrocatalysts.
Jing Yang, Jun Yu, Jianming Cai et al.· Materials Genome Engineering...· 0 citations
A genome-inspired materials intelligence framework (GIMI) for inverse design in high-dimensional compositional spaces is proposed, enabling targeted exploration of complex compositional space and accelerating the discovery of high-performance catalysts.
Chen Zhu, H. Yang, Haifeng Wang et al.· ACS Catalysis· 0 citations
Anion exchange membrane water electrolyzers (AEMWEs) are promising for hydrogen production, yet their performance is bottlenecked by the alkaline hydrogen evolution reaction (HER) with sluggish kinetics induced by high water dissociation barriers and imbalanced H*/OH* adsorption-desorption. Herein, interpretable machine learning (ML) is exploited as a core tool for precise catalyst structure optimization, guiding the fabrication of a Ru2Ni3-carbon nanotubes (CNTs) hybrid catalyst. The ML-engineered catalyst exhibits high HER activity, with an ultra-low overpotential of 14 mV at 10 mA cm–2 and a Tafel slope of 34.3 mV dec–1. When integrated into an AEMWE with a NiFe-LDH anode, the system achieves 1.86 V at 1 A cm–2 (80 °C, no iR correction) and maintains stability for 200 h. Experimental and theoretical studies confirm that the ML-tailored Ru2Ni3-CNTs synergy modulates d-band centers, reduces reaction barriers, and optimizes intermediate adsorption, highlighting ML’s pivotal role in rational electrocatalyst design for advanced AEMWEs.
The discovery and development of high-performance catalysts, which is crucial across all catalysis areas, requires advanced technologies and innovative approaches. Recently, machine learning (ML) has shown promise in accelerating this process, but its capability and examples of discovery of truly novel catalysts have remained limited. In this study, we describe an ML approach that goes beyond the traditional element pool, incorporating elements that have not been previously studied, to develop highly efficient catalysts for ethanol synthesis via CO2 hydrogenation. Starting with an initial data set of 58 catalysts (274 data points obtained at reaction temperatures ranging from 240-400 °C), we conducted 24 iterations of a closed-loop discovery system (ML predictions + experimental validation), testing a total of 555 catalysts (2477 data points), and building a large experimental data set. More than 50 catalysts with superior activity were discovered through this data-driven approach. The multielemental Pd(0.8)-Au(0.3)/K(2.5)-Sr(1)-Fe(20)-Zn(4)-Cd(2)-Yb(1)-Re(1)/CeO2(25%)-ZrO2 catalyst, where the numbers in parentheses represent weight percent (wt %), was identified as the most effective catalyst for ethanol synthesis (ethanol space-time yield: 8.2 mmol gcat-1 h-1 with a CO2 conversion of 57.6% and an ethanol selectivity of 23.2% under reaction conditions of 360 °C, 4 MPa, 12 L gcat-1 h-1, H2/CO2 = 3/1). Comprehensive characterizations, including in situ/operando techniques such as X-ray absorption spectroscopy (XAS), ambient-pressure X-ray photoelectron spectroscopy (AP-XPS), and diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS), enable us to highlight the critical roles of each constituting element in improving ethanol synthesis efficiency.
Pengfei Du, Abdellah Ait El Fakir, S. Mine et al.· Journal of the American Chem...· 0 citations
This work demonstrates how machine learning models can accelerate the high-throughput screening of metal oxides’ oxygen defect thermodynamics to identify promising novel TCH candidates and discusses how liquid metal-mediated thermochemical redox can serve as a promising alternative approach due its drastically reduced operating temperatures and promising technoeconomic outlook.
Matthew D. Witman, A. Ambrosini, Sean R. Bishop et al.· ECS Meeting Abstracts· 0 citations
A comprehensive account of ML applications in NRR, covering curated experimental databases, feature engineering based on atomic, structural, and DFT‐derived descriptors, and ML‐guided insights into single‐atom, dual‐atom, alloy, oxide, nitride, and defect‐engineered catalysts are presented.
