Scholarlink

<p>Atomically precise metal nanoclusters bridge the molecular and bulk regimes, but designing bimetallic motifs with targeted stability and reactivity remains challenging. Here, we combine density functional theory (DFT) and physics-grounded predictive artificial intelligence to map the configurational landscape of 13-atom icosahedral nanoclusters X<sub>12</sub>TM, with hosts X = (Ti, Zr, Hf) and Fe, and a single transition-metal dopant spanning the 3<i>d</i>–5<i>d</i> series. Spin-polarized DFT calculations on 240 bimetallic nanoclusters reveal systematic trends in binding and formation energies, distortion penalties, effective coordination number, <i>d</i>-band center, and HOMO-LUMO gap that govern the competition between core-shell (<i>i</i><i>n</i>) and surface-segregated (<i>o</i><i>u</i><i>t</i>) arrangements. We then pretrain a transformer architecture on a curated set of 2968 unary clusters from the Quantum Cluster Database and fine-tune it on bimetallic data to predict formation energies and <i>i</i><i>n</i>/<i>o</i><i>u</i><i>t</i> preference, achieving mean absolute errors of about 0.6–0.7 eV and calibrated uncertainty intervals. The resulting model rapidly adapts to an unseen Fe-host domain with only a handful of labeled examples. At the same time, attention patterns and Shapley attributions highlight size mismatch, <i>d</i>-electron count, and coordination environment as key descriptors. All data, code, and workflows follow FAIR/TRUE principles, enabling reproducible, interpretable screening of unexplored nanocluster chemistries for catalysis and energy conversion.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Teaching a transformer to think like a chemist: predicting nanocluster stability

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月24日

查看全文
  • João Marcos T. Palheta,
  • Octavio Rodrigues Filho,
  • Mohammad Soleymanibrojeni,
  • Alexandre Cavalheiro Dias,
  • Diego Guedes-Sobrinho,
  • Wolfgang Wenzel,
  • Roland Aydin,
  • Celso R. C. Rêgo,
  • Maurício Jeomar Piotrowski

摘要

Atomically precise metal nanoclusters bridge the molecular and bulk regimes, but designing bimetallic motifs with targeted stability and reactivity remains challenging. Here, we combine density functional theory (DFT) and physics-grounded predictive artificial intelligence to map the configurational landscape of 13-atom icosahedral nanoclusters X12TM, with hosts X = (Ti, Zr, Hf) and Fe, and a single transition-metal dopant spanning the 3d–5d series. Spin-polarized DFT calculations on 240 bimetallic nanoclusters reveal systematic trends in binding and formation energies, distortion penalties, effective coordination number, d-band center, and HOMO-LUMO gap that govern the competition between core-shell (in) and surface-segregated (out) arrangements. We then pretrain a transformer architecture on a curated set of 2968 unary clusters from the Quantum Cluster Database and fine-tune it on bimetallic data to predict formation energies and in/out preference, achieving mean absolute errors of about 0.6–0.7 eV and calibrated uncertainty intervals. The resulting model rapidly adapts to an unseen Fe-host domain with only a handful of labeled examples. At the same time, attention patterns and Shapley attributions highlight size mismatch, d-electron count, and coordination environment as key descriptors. All data, code, and workflows follow FAIR/TRUE principles, enabling reproducible, interpretable screening of unexplored nanocluster chemistries for catalysis and energy conversion.