What is it about?
Nanoparticles are widely used in medicine, electronics, and environmental applications, but understanding their properties requires detailed knowledge of their structure at the atomic level. In this study, we developed ASCOT, a web-based tool that allows researchers to digitally construct nanoparticles and analyze their properties without performing laboratory experiments. The tool can generate realistic, energy-minimized spherical nanoparticles made of materials such as silver (Ag), copper oxide (CuO), and titanium dioxide (TiO₂).  ASCOT automatically calculates important atomistic descriptors, such as energy per atom, coordination number, and structural order parameters. These descriptors describe how atoms are arranged within the nanoparticle and help distinguish between the inner core and outer surface regions.  The tool requires only a few simple inputs (e.g., material type and particle size), making it accessible even to non-experts. It is integrated into the Enalos Cloud platform, allowing users to quickly generate nanoparticles and obtain data that can be used for further modeling or machine learning applications.
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Why is it important?
Nanoparticle properties—such as reactivity, stability, and toxicity—are strongly influenced by their size, structure, and surface characteristics. However, experimentally characterizing these properties at the atomic level is complex, time-consuming, and expensive. This work is important because it provides a fast, reproducible, and accessible way to generate and characterize nanoparticles computationally. By calculating atomistic descriptors, ASCOT enables researchers to better understand how nanoparticle structure relates to behavior. Importantly, these descriptors can be used as inputs for machine learning models, supporting predictive approaches in nanotechnology, including toxicity assessment and safe-by-design material development.  The tool also promotes standardization and reproducibility in nanoparticle research, addressing a key challenge in the field.
Perspectives
This work highlights the importance of combining computational modeling with user-friendly cloud tools to advance nanoscience. By enabling digital construction of nanoparticles, ASCOT reduces reliance on trial-and-error experimental approaches. From a personal perspective, a key contribution is the ability to distinguish between core and surface regions of nanoparticles, which is critical for understanding their interactions with biological and environmental systems. Looking ahead, integrating ASCOT-generated descriptors with machine learning and experimental datasets could further enhance predictive modeling capabilities. This would support the development of safer and more efficient nanomaterials across a wide range of applications. Ultimately, tools like ASCOT contribute to the growing ecosystem of cloud-based nanoinformatics platforms, accelerating innovation while promoting transparency, accessibility, and sustainability in nanotechnology research.
Dr Antreas Afantitis
NovaMechanics Ltd
Read the Original
This page is a summary of: ASCOT: A web tool for the digital construction of energy minimized Ag, CuO, TiO2 spherical nanoparticles and calculation of their atomistic descriptors, Computational and Structural Biotechnology Journal, December 2024, Elsevier,
DOI: 10.1016/j.csbj.2024.03.011.
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