Equivariant representations for molecular Hamiltonians and N-center atomic-scale properties

DFT Studies of Graphene-Functionalised Derivatives of Capecitabine

Zeitschrift für Naturforschung A ◽

10.1515/zna-2017-0290 ◽

2017 ◽

Vol 72 (12) ◽

pp. 1131-1138 ◽

Cited By ~ 1

Author(s):

Mehdi Aramideh ◽

Mahmoud Mirzaei ◽

Ghadamali Khodarahmi ◽

Oğuz Gülseren

Keyword(s):

Density Functional ◽

Density Functional Theory Calculations ◽

Atomic Scale ◽

Phase System ◽

Molecular Models ◽

Functional Theory ◽

Atomic Properties ◽

Scale Properties ◽

Derivatives Of

AbstractCancer is one of the major problems for so many people around the world; therefore, dedicating efforts to explore efficient therapeutic methodologies is very important for researchers of life sciences. In this case, nanostructures are expected to be carriers of medicinal compounds for targeted drug design and delivery purposes. Within this work, the graphene (Gr)-functionalised derivatives of capecitabine (CAP), as a representative anticancer, have been studied based on density functional theory calculations. Two different sizes of Gr molecular models have been used for the functionalisation of CAP counterparts, CAP-Gr3 and CAP-Gr5, to explore the effects of Gr-functionalisation on the original properties of CAP. All singular and functionalised molecular models have been optimised and the molecular and atomic scale properties have been evaluated for the optimised structures. Higher formation favourability has been obtained for CAP-Gr5 in comparison with CAP-Gr3 and better structural stability has been obtained in the water-solvated system than the isolated gas-phase system for all models. The CAP-Gr5 model could play a better role of electron transferring in comparison with the CAP-Gr3 model. As a concluding remark, the molecular properties of CAP changed from singular to functionalised models whereas the atomic properties remained almost unchanged, which is expected for a carrier not to use significant perturbations to the original properties of the carried counterpart.

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Ca3Mn2O7 structural path unraveled by atomic-scale properties: A combined experimental and ab initio study

Physical Review B ◽

10.1103/physrevb.101.064103 ◽

2020 ◽

Vol 101 (6) ◽

Cited By ~ 2

Author(s):

P. Rocha-Rodrigues ◽

S. S. M. Santos ◽

I. P. Miranda ◽

G. N. P. Oliveira ◽

J. G. Correia ◽

...

Keyword(s):

Ab Initio ◽

Atomic Scale ◽

Ab Initio Study ◽

Scale Properties ◽

Structural Path

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Atomic-scale properties of jogs along 1/2〈110〉{1¯10} edge dislocations in MgO

Scripta Materialia ◽

10.1016/j.scriptamat.2020.02.013 ◽

2020 ◽

Vol 181 ◽

pp. 66-69

Author(s):

Jian-Hui Zhai ◽

Pierre Hirel ◽

Philippe Carrez

Keyword(s):

Atomic Scale ◽

Scale Properties ◽

Edge Dislocations

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Characterisation of the temperature-dependent M1 to R phase transition in W-doped VO2 nanorod aggregates by Rietveld refinement and theoretical modelling

Physical Chemistry Chemical Physics ◽

10.1039/d0cp01058h ◽

2020 ◽

Vol 22 (15) ◽

pp. 7984-7994

Author(s):

Lei Miao ◽

Ying Peng ◽

Dianhui Wang ◽

Jihui Liang ◽

Chaohao Hu ◽

...

Keyword(s):

Rietveld Refinement ◽

First Principles ◽

Atomic Scale ◽

Theoretical Modelling ◽

First Principles Calculations ◽

Temperature Dependent ◽

Macro Scale ◽

Scale Properties ◽

W Doping ◽

Synchrotron Xrd

Synchrotron XRD Rietveld refinement is combined with first-principles calculations to probe the effect of W doping on the IMT mechanism in VO2 nanorods, providing insights into the connection between atomic-scale phenomena and macro-scale properties.

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Machine Learning at the Atomic Scale

CHIMIA International Journal for Chemistry ◽

10.2533/chimia.2019.972 ◽

2019 ◽

Vol 73 (12) ◽

pp. 972-982 ◽

Cited By ~ 2

Author(s):

Félix Musil ◽

Michele Ceriotti

Keyword(s):

Machine Learning ◽

Short Review ◽

Atomic Scale ◽

Structure Property ◽

Computationally Efficient ◽

Property Relations ◽

Scale Modeling ◽

Phase Systems ◽

Scale Properties ◽

Physical Phenomena

Statistical learning algorithms are finding more and more applications in science and technology. Atomic-scale modeling is no exception, with machine learning becoming commonplace as a tool to predict energy, forces and properties of molecules and condensed-phase systems. This short review summarizes recent progress in the field, focusing in particular on the problem of representing an atomic configuration in a mathematically robust and computationally efficient way. We also discuss some of the regression algorithms that have been used to construct surrogate models of atomic-scale properties. We then show examples of how the optimization of the machine-learning models can both incorporate and reveal insights onto the physical phenomena that underlie structure–property relations.

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Probing three-dimensional surface force fields with atomic resolution: Measurement strategies, limitations, and artifact reduction

Beilstein Journal of Nanotechnology ◽

10.3762/bjnano.3.73 ◽

2012 ◽

Vol 3 ◽

pp. 637-650 ◽

Cited By ~ 23

Author(s):

Mehmet Z Baykara ◽

Omur E Dagdeviren ◽

Todd C Schwendemann ◽

Harry Mönig ◽

Eric I Altman ◽

...

Keyword(s):

Force Fields ◽

Image Interpretation ◽

Three Dimensional ◽

Surface Force ◽

Interaction Force ◽

Atomic Scale ◽

Atomic Resolution ◽

Spatial Dimensions ◽

Scale Properties ◽

Measurement Strategies

Noncontact atomic force microscopy (NC-AFM) is being increasingly used to measure the interaction force between an atomically sharp probe tip and surfaces of interest, as a function of the three spatial dimensions, with picometer and piconewton accuracy. Since the results of such measurements may be affected by piezo nonlinearities, thermal and electronic drift, tip asymmetries, and elastic deformation of the tip apex, these effects need to be considered during image interpretation. In this paper, we analyze their impact on the acquired data, compare different methods to record atomic-resolution surface force fields, and determine the approaches that suffer the least from the associated artifacts. The related discussion underscores the idea that since force fields recorded by using NC-AFM always reflect the properties of both the sample and the probe tip, efforts to reduce unwanted effects of the tip on recorded data are indispensable for the extraction of detailed information about the atomic-scale properties of the surface.

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