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

Abstract

Machine 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.

Understanding zinc-doped hydroxyapatite structures using first-principles calculations and convolutional neural network algorithm

“…51,52 For example, Ma et al established the relationship between morphology of the HAP nanoparticle and surface energy via deep learning. 53 However, previous work cannot quickly and accurately identify the structural information around the lowest-energy region in the global potential energy surface (PES) for different doping concentrations. Therefore, it would be very difficult to provide meaningful predictions of the corresponding material properties.…”

Computational and data driven molecular material design assisted by low scaling quantum mechanics calculations and machine learning

“…Experimentally observed scanning electron microscope (SEM) or transmission electron microscopy (TEM) pictures are also useful to derive the morphology descriptors of nanoparticles, from which various explainable ML models such as light gradient boosted machine (LightGBM), extreme gradient boosting (XGBoost), support vector machine (SVM), and gradient boosting decision tree (GBDT) could predict the surface energy after being trained with high throughput calculations on different size scales. 211 …”

“…Recently, a modified DeepMoleNet model was proposed to study the relative stability of nanoparticles with the introduction of cut-off approximation for treating a complex system. 211 It can be expected that the multi-level attention neural network is applicable to high-throughput screening of various chemical species to accelerate the rational design of material candidates.…”

XGBoost automates the characterisation of reversibly actuating planar-flow-casted NiTi shape memory alloy foil

“…Hence, a system that can accurately and rapidly predict force and angle being generated by an actuating SMA foil under excitement, has evidently become essential [15][16][17][18][19][20]. Elimination of time-consuming physical testing was a major factor to increase the e ciency of the SMA material characterization and new SMA material discovery [21][22][23][24][25][26].…”

“…After a comparative study using various ensemble machine learning techniques, we employed Extreme Gradient Boosting (XGBoost) [20][21][22][23][24][25][26][27] Regression model in the proposed methodology as the most effective predictor. A large database was generated using in-house automated testing facilities at CSIRO, Australia.…”