Jul 2026· Journal of the American Chemical Society· 0 citations· 81 references
Medicine
Abstract
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.
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
Catalytic N2O decomposition in the presence of O2 is a key process for addressing environmental challenges, such as greenhouse gas emissions and ozone layer depletion. However, the identification of efficient catalysts for this reaction remains challenging owing to the limitations of conventional methods. In this study, we employ a machine learning approach designed to accelerate the discovery of effective direct N2O decomposition catalysts. Starting with 51 catalysts and conducting 37 cycles of a closed-loop discovery system (machine-learning prediction + experiment), 633 catalysts are experimentally tested. Over 10 multi-elemental catalysts exhibiting superior activity are identified, surpassing the performance of the originally identified best catalyst. Among them, Rh(1)–Pd(2)/ZrO2_EP exhibits the highest catalytic performance for N2O decomposition. Through control experiments and a combination of ex situ and in situ characterizations, we identify the essential role of each component within the catalyst system. Catalytic N2O decomposition in the presence of O2 is a key process for addressing environmental challenges, yet identifying efficient catalysts for this reaction remains challenging. Here, the authors employ a machine learning approach to accelerate the discovery of effective catalysts for direct N₂O decomposition.
Chenxi He, S. Mine, Yuan Jing et al.· Nature Communications· 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.
This study demonstrates an example of how machine-learning-driven materials discovery can accelerate catalyst design while simultaneously offering an experimental solution to the persistent challenge of high ORR overpotential.
Darik A. Rosser, Anto Felix Sotvik GS, Kevin C. Leonard· ACS Applied Energy Materials· 0 citations
Designing high-performance catalysts for efficient HCHO oxidation under mild conditions is essential, yet remains challenging to achieve through conventional experimental screening. Here, we present a machine-learning-accelerated strategy for designing novel oxide-Ag tandem systems that overcome this limitation by coupling oxide-mediated HCHO activation with subsequent intermediate oxidation on Ag. Guided by an adsorption-energy-based activity descriptor, four theoretically predicted oxide-Ag systems (TiO2, Nb2O5, Ga2O3, SnO2) exhibit markedly enhanced tandem catalytic performance compared to conventional Ag catalysts. In particular, a representative TiO2/Ag-γ-Al2O3, prepared by simple physical mixing of commercial anatase and Ag-γ-Al2O3 catalyst, achieves an HCHO oxidation rate of 0.56 μmol gAg-1 s-1 at 55 °C, surpassing Ag-γ-Al2O3 alone by over 2 orders of magnitude in performance. Combining experimental and theoretical studies further reveals a cascade reaction mechanism, in which TiO2 catalyzes the HCHO-to-methyl formate transformation via a surface OH-mediated pathway, followed by efficient methyl formate oxidation to CO2 on Ag-γ-Al2O3. Particularly, the local oxygen environment over oxide surfaces is identified as a key factor governing HCHO adsorption and conversion. This work establishes a generalizable design principle for tandem catalysts and provides a data-driven framework for advancing low-temperature HCHO oxidation technologies.
Yue Ding, Hui Wang, C. Dong et al.· Environmental Science and Te...· 0 citations
The production of green hydrogen through water splitting requires highly efficient electrocatalysts, but the current trial-and-error-based synthesis or discovery is time-consuming, costly and resource-intensive. Machine learning (ML) provides a powerful, data-driven alternative that can model complex structure-activity relationships across large chemical spaces at orders-of-magnitude speed. This review systematically overviews the life cycle of the electrocatalyst research and development application of ML. First, the thermodynamic and kinetic principles of the hydrogen and oxygen evolution reactions are summarised, along with some well-adopted and accepted activity descriptors. Then we explore data sources, featurization approaches, and algorithms, and discuss the model space, from a simple interpretable model to a graph neural network to a generative model, in the context of the ML toolkit. Strategic applications are discussed for high-throughput virtual screening of alloys and single-atom catalysts, as well as multifunctional activity prediction for overall water splitting, and stability optimisation under operating conditions. The topic of emerging frontiers is highlighted, including high-entropy alloys, amorphous materials, and linking atomic-scale understanding to device-level performance through integration with density functional theory. Finally, the problems of data scarcity, model interpretability and the discrepancy between computational predictions and industrial implementation are discussed, along with future directions for closed-loop discovery and self-driving laboratories. Incorporating ML into electrocatalyst design and combining it with autonomous experimentation will revolutionise this process, from simulation to energy solution, dramatically speeding it up.
Vamsi Krishna Kudapa, Shoaib Mohd, Vijayakumar Sivasundar et al.· Frontiers in Chemistry· 0 citations
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