scholarly journals Machine learning on properties of multiscale multisource hydroxyapatite nanoparticles datasets with different morphologies and sizes

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Ziteng Liu ◽  
Yinghuan Shi ◽  
Hongwei Chen ◽  
Tiexin Qin ◽  
Xuejie Zhou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Density Functional ◽  
Structure Property ◽  
Hydroxyapatite Nanoparticles ◽  
Functional Theory ◽  
Scale Functions ◽  
Property Relation ◽  
Geometric Descriptors ◽  
Semi Empirical

AbstractMachine learning models for exploring structure-property relation for hydroxyapatite nanoparticles (HANPs) are still lacking. A multiscale multisource dataset is presented, including both experimental data (TEM/SEM, XRD/crystallinity, ROS, anti-tumor effects, and zeta potential) and computation results (containing 41,976 data samples with up to 9768 atoms) of nanoparticles with different sizes and morphologies at density functional theory (DFT), semi-empirical DFTB, and force field, respectively. Three geometric descriptors are set for the explainable machine learning methods to predict surface energies and surface stress of HANPs with satisfactory performance. To avoid the pre-determination of features, we also developed a predictive deep learning model within the framework of graph convolution neural network with good generalizability. Energies with DFT accuracy are achievable for large-sized nanoparticles from the learned correlations and scale functions for mapping different theoretical levels and particle sizes. The simulated XRD spectra and crystallinity values are in good agreement with experiments.

Calculations of Surface Structure for SrTiO3 Perovskite

2001 ◽  
Vol 672 ◽  
Author(s):  
E. Heifetsa ◽  
R.I. Eglitisb ◽  
E.A. Kotomin ◽  
G. Borstelb
Keyword(s):  
Ab Initio ◽  
Density Functional ◽  
Dipole Moments ◽  
Surface Relaxation ◽  
Surface Layers ◽  
Functional Theory ◽  
Near Surface ◽  
Hartree Fock ◽  
Semi Empirical

ABSTRACTWe present and discuss results of the calculations for SrTiO3 (100) surface relaxation with different terminations (SrO and TiO2) using a semi-empirical shell model (SM) as well as abinitio methods based on Hartree-Fock (HF) and Density Functional Theory (DFT) formalisms. Using the SM, the positions of atoms in 16 near-surface layers placed atop a slab of rigid ions are optimized. This permits us determination of surface rumpling and surfaceinduced dipole moments (polarization) for different terminations. We also compare results of the ab initio calculations based on both HF with the DFT-type electroncorrelation corrections, several DFT with different exchange-correlation functionals, and hybrid exchange techniques. OurSM results for the (100) surfaces are in a good agreement with both our ab initio calculations and LEED experiments.

scholarly journals Assessment of Amyloid Forming Tendency of Peptide Sequences from Amyloid Beta and Tau Proteins Using Force-Field, Semi-Empirical, and Density Functional Theory Calculations

2021 ◽  
Vol 22 (6) ◽  
pp. 3244
Author(s):  
Charuvaka Muvva ◽  
Natarajan Arul Murugan ◽  
Venkatesan Subramanian
Keyword(s):  
Force Field ◽  
Density Functional ◽  
Amyloid Β ◽  
Density Functional Theory Calculations ◽  
Tau Proteins ◽  
Functional Theory ◽  
Energy Profiles ◽  
Α Helix ◽  
Semi Empirical ◽  
Β Sheet

A wide variety of neurodegenerative diseases are characterized by the accumulation of protein aggregates in intraneuronal or extraneuronal brain regions. In Alzheimer’s disease (AD), the extracellular aggregates originate from amyloid-β proteins, while the intracellular aggregates are formed from microtubule-binding tau proteins. The amyloid forming peptide sequences in the amyloid-β peptides and tau proteins are responsible for aggregate formation. Experimental studies have until the date reported many of such amyloid forming peptide sequences in different proteins, however, there is still limited molecular level understanding about their tendency to form aggregates. In this study, we employed umbrella sampling simulations and subsequent electronic structure theory calculations in order to estimate the energy profiles for interconversion of the helix to β-sheet like secondary structures of sequences from amyloid-β protein (KLVFFA) and tau protein (QVEVKSEKLD and VQIVYKPVD). The study also included a poly-alanine sequence as a reference system. The calculated force-field based free energy profiles predicted a flat minimum for monomers of sequences from amyloid and tau proteins corresponding to an α-helix like secondary structure. For the parallel and anti-parallel dimer of KLVFFA, double well potentials were obtained with the minima corresponding to α-helix and β-sheet like secondary structures. A similar double well-like potential has been found for dimeric forms for the sequences from tau fibril. Complementary semi-empirical and density functional theory calculations displayed similar trends, validating the force-field based free energy profiles obtained for these systems.

scholarly journals A machine learning platform for the discovery of materials

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Carl E. Belle ◽  
Vural Aksakalli ◽  
Salvy P. Russo
Keyword(s):  
Machine Learning ◽  
Density Functional ◽  
Gw Approximation ◽  
Dft Methods ◽  
Functional Theory ◽  
Unit Cell Size ◽  
Learning Platform ◽  
Photovoltaic Materials ◽  
Wide Range ◽  
Quantum Espresso

AbstractFor photovoltaic materials, properties such as band gap $$E_{g}$$ E g are critical indicators of the material’s suitability to perform a desired function. Calculating $$E_{g}$$ E g is often performed using Density Functional Theory (DFT) methods, although more accurate calculation are performed using methods such as the GW approximation. DFT software often used to compute electronic properties includes applications such as VASP, CRYSTAL, CASTEP or Quantum Espresso. Depending on the unit cell size and symmetry of the material, these calculations can be computationally expensive. In this study, we present a new machine learning platform for the accurate prediction of properties such as $$E_{g}$$ E g of a wide range of materials.

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