ASCOT: A web tool for the digital construction of energy minimized Ag, CuO, TiO2 spherical nanoparticles and calculation of their atomistic descriptors

Panagiotis D Kolokathis; Evangelos Voyiatzis; Nikolaos K Sidiropoulos; Andreas Tsoumanis; Georgia Melagraki; Kaido Tämm; Iseult Lynch; Antreas Afantitis

doi:10.1016/j.csbj.2024.03.011

ASCOT: A web tool for the digital construction of energy minimized Ag, CuO, TiO₂ spherical nanoparticles and calculation of their atomistic descriptors

Comput Struct Biotechnol J. 2024 Mar 12:25:34-46. doi: 10.1016/j.csbj.2024.03.011. eCollection 2024 Dec.

Authors

Panagiotis D Kolokathis^{1

2}, Evangelos Voyiatzis³, Nikolaos K Sidiropoulos^{1

2}, Andreas Tsoumanis^{1

3}, Georgia Melagraki⁴, Kaido Tämm⁵, Iseult Lynch^{2

6}, Antreas Afantitis^{1

3

2}

Affiliations

¹ NovaMechanics MIKE, Piraeus 18545, Greece.
² Entelos Institute, Larnaca 6059, Cyprus.
³ NovaMechanics Ltd, Nicosia 1070, Cyprus.
⁴ Division of Physical Sciences and Applications, Hellenic Military Academy, Vari 16672, Greece.
⁵ Institute of Chemistry, University of Tartu, Tartu 50090, Estonia.
⁶ School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, United Kingdom.

Abstract

ASCOT (an acronym derived from Ag-Silver, Copper Oxide, Titanium Oxide) is a user-friendly web tool for digital construction of electrically neutral, energy-minimized spherical nanoparticles (NPs) of Ag, CuO, and TiO₂ (both Anatase and Rutile forms) in vacuum, integrated into the Enalos Cloud Platform (https://www.enaloscloud.novamechanics.com/sabydoma/ascot/). ASCOT calculates critical atomistic descriptors such as average potential energy per atom, average coordination number, common neighbour parameter (used for structural classification in simulations of crystalline phases), and hexatic order parameter (which measures how closely the local environment around a particle resembles perfect hexatic symmetry) for both core (over 4 Å from the surface) and shell (within 4 Å of the surface) regions of the NPs. These atomistic descriptors assist in predicting the most stable NP size based on lowest per atom energy and serve as inputs for developing machine learning models to predict the toxicity of these nanomaterials. ASCOT's automated backend requires minimal user input in order to construct the digital NPs: inputs needed are the material type (Ag, CuO, TiO₂-Anatase, TiO₂-Rutile), target diameter, a Force-Field from a pre-validated list, and the energy minimization parameters, with the tool providing a set of default values for novice users.

Keywords: Ag; Anatase; Automation; CuO; Descriptors; Energy Minimization; Nanoparticle; Rutile; Tenorite; TiO2.