Interpretable, Physics-Informed Learning Reveals Sulfur Adsorption and Poisoning Mechanisms in 13-Atom Icosahedra Nanoclusters

TL;DR

This study combines DFT and machine learning to understand sulfur adsorption and poisoning mechanisms on 13-atom transition metal nanoclusters, providing insights for designing sulfur-tolerant catalysts.

Contribution

It introduces a physics-informed machine learning approach to map sulfur adsorption and poisoning on transition metal nanoclusters, revealing key descriptors and periodic trends.

Findings

Metal-sulfur interaction dominates adsorption energy

Moderate structural distortions upon sulfur adsorption for most metals

Ti, Zr, Hf form a balanced group with strong binding and minimal restructuring

Abstract

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$d$ to 5$d$). 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 penalties are usually moderate but can be significant for a few metals, suggesting these are more likely to restructure when sulfur is adsorbed. Using unsupervised \textit{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 \ce{Ti}, \ce{Zr}, and \ce{Hf} as a balanced group that combines strong sulfur binding with minimal structural change. Additional DFT calculations for \ce{SO2} 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.