Abstract
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.
Funder
Ministry of Science and Technology of the People’s Republic of China
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Computer Science Applications,Mechanics of Materials,General Materials Science,Modeling and Simulation
Cited by
24 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献