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

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 resu...

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Bibliographic Details
Published in:npj computational materials 2021-09, Vol.7 (1), p.1-11, Article 142
Main Authors: Liu, Ziteng, Shi, Yinghuan, Chen, Hongwei, Qin, Tiexin, Zhou, Xuejie, Huo, Jun, Dong, Hao, Yang, Xiao, Zhu, Xiangdong, Chen, Xuening, Zhang, Li, Yang, Mingli, Gao, Yang, Ma, Jing
Format: Article
Language:English
Subjects:
639/301
639/925
Anticancer properties
Artificial neural networks
Characterization and Evaluation of Materials
Chemistry and Materials Science
Computational Intelligence
Crystal structure
Crystallinity
Datasets
Deep learning
Density functional theory
Hydroxyapatite
Learning algorithms
Machine learning
Materials Science
Mathematical and Computational Engineering
Mathematical and Computational Physics
Mathematical Modeling and Industrial Mathematics
Nanoparticles
Neural networks
Theoretical
Zeta potential
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Summary: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.
ISSN:2057-3960
2057-3960
DOI:10.1038/s41524-021-00618-1