Related Experiment Video
Updated: Jul 2, 2026
13:58
Probing C84-embedded Si Substrate Using Scanning Probe Microscopy and Molecular Dynamics
Published on: September 28, 2016
Dataset of Optimized Structures of Aliphatic Chains Chemisorbed on Si(110) and Si(111) Surfaces via First-Principles
Sara Marchio1, Francesco Buonocore2, Simone Giusepponi3
1ENEA, Casaccia Research Centre, I-00123, Rome, Italy. sara.marchio@enea.it.
Scientific Data
|July 1, 2026
Summary
We created a dataset of over 700 silicon surface configurations to help predict and optimize electronic and chemical properties for nanoelectronics and biosensing applications.
Area of Science:
- Surface Science
- Materials Science
- Computational Chemistry
Background:
- Organic monolayer functionalization tunes silicon surface properties for advanced applications.
- Predicting and optimizing these properties requires large-scale computational screening.
Purpose of the Study:
- To present an open dataset of density functional theory (DFT) configurations for silicon surface functionalization.
- To support rational design and accelerate data-driven approaches in surface science.
Main Methods:
- Generated over 700 DFT geometry-optimized configurations of organic chains on Si(111) and Si(110) surfaces.
- Explored diverse adsorption geometries for alkyl, alkenyl, and 1-alkynyl functional groups.
- Evaluated configuration stability using Si-C bond dissociation energies.
Main Results:
- The dataset includes a wide range of chemisorbed organic chain configurations on silicon surfaces.
- Relative stabilities were systematically evaluated, providing insights into preferred adsorption geometries.
- Demonstrated dataset versatility with use cases in relevant application domains.
Conclusions:
- The open dataset facilitates computational screening for silicon surface functionalization.
- Accelerates the development of novel materials for nanoelectronics, biosensing, and surface engineering.
- Promotes data-driven discovery in computational surface science.
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