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<p>The functionalization of silicon surfaces with organic monolayers is a powerful strategy to tune their electronic and chemical properties, enabling applications in fields such nanoelectronics, biosensing and surface engineering. However, to effectively predict and optimize surface properties, a large-scale DFT-based screening of the broad variety of functional groups is required. To support the rational design of the interfaces, we present an open dataset comprising more than 700 density functional theory geometry-optimized configurations of alkyl, alkenyl, and 1-alkynyl chains chemisorbed on Si(111) and Si(110) surfaces, obtained exploring a wide range of adsorption geometries. The relative stabilities of these configurations are evaluated through Si–C bond dissociation energies. To demonstrate the dataset’s versatility, we highlight two representative use cases, each related to relevant application domains. This open dataset aims to accelerate computational screening and data-driven approaches in surface science.</p>

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Dataset of Optimized Structures of Aliphatic Chains Chemisorbed on Si(110) and Si(111) Surfaces via First-Principles Methods

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月30日

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  • Sara Marchio,
  • Francesco Buonocore,
  • Simone Giusepponi,
  • Massimo Celino

摘要

The functionalization of silicon surfaces with organic monolayers is a powerful strategy to tune their electronic and chemical properties, enabling applications in fields such nanoelectronics, biosensing and surface engineering. However, to effectively predict and optimize surface properties, a large-scale DFT-based screening of the broad variety of functional groups is required. To support the rational design of the interfaces, we present an open dataset comprising more than 700 density functional theory geometry-optimized configurations of alkyl, alkenyl, and 1-alkynyl chains chemisorbed on Si(111) and Si(110) surfaces, obtained exploring a wide range of adsorption geometries. The relative stabilities of these configurations are evaluated through Si–C bond dissociation energies. To demonstrate the dataset’s versatility, we highlight two representative use cases, each related to relevant application domains. This open dataset aims to accelerate computational screening and data-driven approaches in surface science.