Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article 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 TiO (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, TiO-Anatase, TiO-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.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10973535 | PMC |
| http://dx.doi.org/10.1016/j.csbj.2024.03.011 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Large-scale Investigations of Water Diffusion in Metal-Organic Frameworks with One-Dimensional Channels.
ACS Appl Mater Interfaces
September 2025
Department of Chemical Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei 106319, Taiwan.
Metal-organic frameworks (MOFs), known for their highly versatile nature, show considerable promise as adsorbents and membranes for water-related applications such as water harvesting and water filtration. One of the key factors that may influence their efficiency is the diffusion of water within MOFs. However, the behaviors and mechanisms of water diffusion in MOFs remain relatively underexplored.
View Article and Find Full Text PDFThermodynamics-inspired high-entropy oxide synthesis.
Nat Commun
September 2025
Department of Materials Science and Engineering, The Pennsylvania State University, University Park, PA, USA.
High-entropy oxide (HEO) thermodynamics transcend temperature-centric approaches, spanning a multidimensional landscape where oxygen chemical potential plays a decisive role. Here, we experimentally demonstrate how controlling the oxygen chemical potential coerces multivalent cations into divalent states in rock salt HEOs. We construct a preferred valence phase diagram based on thermodynamic stability and equilibrium analysis, alongside a high throughput enthalpic stability map derived from atomistic calculations leveraging machine learning interatomic potentials.
View Article and Find Full Text PDFShort-Term Morphological Response of Polypropylene Membranes to Hypersaline Lithium Fluoride Solutions: A Multiscale Modeling Approach.
Int J Mol Sci
July 2025
Department of Computer Engineering, Modelling, Electronics and System Engineering (DIMES), University of Calabria, Via P. Bucci 42C, 87036 Rende, CS, Italy.
Understanding the early-stage physical interactions between polymeric membranes and supersaturated salt solutions is crucial for advancing membrane-assisted crystallization (MCr) processes. In this study, we employed molecular dynamics (MD) simulations to investigate the short-term morphological response of an isotactic polypropylene (PP) membrane in contact with LiF solutions at different concentrations (5.8 M and 8.
View Article and Find Full Text PDFOpportunities and Challenges in Unsupervised Learning: The Case of Aqueous Electrolyte Solutions.
J Chem Theory Comput
August 2025
The "Abdus Salam" International Centre for Theoretical Physics, I-34151 Trieste, Italy.
Machine learning has emerged as a powerful tool in atomistic simulations, enabling the identification of complex patterns in molecular systems, limiting human intervention and bias. However, the practical implementation of these methods presents significant technical challenges, particularly in the selection of hyperparameters and in the physical interpretability of machine-learned descriptors. In this work, we systematically investigate these challenges by applying an unsupervised learning protocol to a fundamental problem in physical chemistry, namely, how ions perturb the local structure of water.
View Article and Find Full Text PDFPredicting intersystem crossing rate constants of alkoxy-radical pairs with structure-based descriptors and machine learning.
Phys Chem Chem Phys
July 2025
Department of Chemistry, University of Helsinki, P.O. Box 55 (A.I. Virtanens plats 1), FIN-00014, Finland.
Peroxy radicals (RO) are ubiquitous intermediates in many oxidation processes, especially in the atmospheric gas phase. The recombination reaction of two peroxy radicals (RO + R'O) has been demonstrated to lead, several steps, to a triplet complex of two alkoxy radicals: (RO˙⋯R'O˙). The different product channels of RO + R'O reactions thus correspond to different reactions of this triplet complex.
View Article and Find Full Text PDF