摘要
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 largesized 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.
基金
This work was supported by the National Key Research and Development Program of China(2017YFB0702601)
the National Natural Science Foundation of China(grant nos.21873045,22033004).We gratefully acknowledge the High Performance Computing Centre of Nanjing University for providing the IBM Blade cluster system and Nanxin Pharm Co.,Ltd.,Nanjing.
