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<p>Transition-metal nanoclusters exhibit structural and electronic properties that depend on their size, often making them superior to bulk materials for heterogeneous catalysis. However, their performance can be limited by sulfur poisoning. Here, we use dispersion-corrected density functional theory (DFT) and physics-informed machine learning to map how atomic sulfur adsorbs and causes poisoning on 13-atom icosahedral clusters from 30 different transition metals (3<i>d</i> to 5<i>d</i>). We measure which sites sulfur prefers to adsorb to, the thermodynamics and energy breakdown, changes in structure, such as bond lengths and coordination, and electronic properties, such as <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\varepsilon _d\)</EquationSource> </InlineEquation>, the HOMO-LUMO gap, and charge transfer. Vibrational analysis reveals true energy minima and provides ZPE-based descriptors that reflect the lattice stiffening upon sulfur adsorption. For most metals, the metal-sulfur interaction mainly determines adsorption energy. At the same time, distortion contributions are generally moderate, but become large in magnitude for a few metals, suggesting a stronger tendency toward adsorption-induced restructuring. Using unsupervised <i>k</i>-means clustering, we identify periodic trends and group metals based on their adsorption responses. Supervised regression models with leave-one-feature-out analysis identify the descriptors that best predict adsorption for new samples. Our results highlight the isoelectronic triad Ti, Zr, and Hf as a balanced group that stands out by combining moderate-to-strong sulfur binding with excellent structural stability. This combination suggests an optimal trade-off between the chemical activation of sulfur-containing species and resistance to poisoning-induced structural degradation. Additional DFT calculations for <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\text {SO}_{2}\)</EquationSource> </InlineEquation> adsorption reveal strong binding and a clear tendency toward dissociation on these clusters, linking electronic states, lattice response, and poisoning strength. These findings offer data-driven guidelines for designing sulfur-tolerant nanocatalysts at the subnanometer scale.</p>

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Interpretable, physics-informed learning reveals sulfur adsorption and poisoning mechanisms in 13-atom icosahedra nanoclusters

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月4日

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  • Raiane Ferreira Monteiro,
  • João Marcos T. Palheta,
  • Tulio Gnoatto Grison,
  • Octávio Rodrigues Filho,
  • Renato Luis Tame Parreira,
  • Diego Guedes-Sobrinho,
  • Celso R. C. Rêgo,
  • Alexandre C. Dias,
  • Krys Elly de Araújo Batista,
  • Maurício J. Piotrowski

摘要

Transition-metal nanoclusters exhibit structural and electronic properties that depend on their size, often making them superior to bulk materials for heterogeneous catalysis. However, their performance can be limited by sulfur poisoning. Here, we use dispersion-corrected density functional theory (DFT) and physics-informed machine learning to map how atomic sulfur adsorbs and causes poisoning on 13-atom icosahedral clusters from 30 different transition metals (3d to 5d). We measure which sites sulfur prefers to adsorb to, the thermodynamics and energy breakdown, changes in structure, such as bond lengths and coordination, and electronic properties, such as \(\varepsilon _d\) , the HOMO-LUMO gap, and charge transfer. Vibrational analysis reveals true energy minima and provides ZPE-based descriptors that reflect the lattice stiffening upon sulfur adsorption. For most metals, the metal-sulfur interaction mainly determines adsorption energy. At the same time, distortion contributions are generally moderate, but become large in magnitude for a few metals, suggesting a stronger tendency toward adsorption-induced restructuring. Using unsupervised k-means clustering, we identify periodic trends and group metals based on their adsorption responses. Supervised regression models with leave-one-feature-out analysis identify the descriptors that best predict adsorption for new samples. Our results highlight the isoelectronic triad Ti, Zr, and Hf as a balanced group that stands out by combining moderate-to-strong sulfur binding with excellent structural stability. This combination suggests an optimal trade-off between the chemical activation of sulfur-containing species and resistance to poisoning-induced structural degradation. Additional DFT calculations for \(\text {SO}_{2}\) adsorption reveal strong binding and a clear tendency toward dissociation on these clusters, linking electronic states, lattice response, and poisoning strength. These findings offer data-driven guidelines for designing sulfur-tolerant nanocatalysts at the subnanometer scale.