Understanding the interaction of the hydroxyapatite (HAp) surface with hydroxyl originating from either the alkalescent physiological environment or HAp itself is crucial for the development of HAp-based biomaterials. Periodical density functional theory calculations were carried out in this study to explore the interaction of the HAp (100), (010) and (001) facets with hydroxyl. Based on a comparison study of Ca-rich, PO₄-rich and Ca–PO₄–OH mixed surfaces, the interaction pattern, interaction energy and effect of an additional water molecule on the Ca–OH interaction were comprehensively studied. The formation of CaOH on the Ca-rich surface was energetically favored on (100) and (001), while Ca(OH)₂ was energetically favored on (010). The Ca–water interaction was competitive, but had lower interaction energy than Ca–OH. Furthermore, Ca–O bonding and its influence on the OH stretching vibration were analyzed. Our calculations suggest that the hydroxyl-coated surface structure is more appropriate than the commonly used Ca-terminated surface model for studying HAp surface activity in its service environments.

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.

Ta addition has considerable effects on the microstructures and mechanical performances of CoCrFeNi alloy systems. Structure search with the special quasirandom structure method and structure identification with first-principles calculations were carried out to investigate the structural, thermodynamic and mechanical properties of CoCrFeNiTa_x (x = 0.0–1.0) high-entropy alloys in the fcc and bcc lattice frameworks. The predicted lattice parameters of identified structures are in agreement with available experiments. Phase transition between the fcc and bcc lattices was predicted for the lowest-energy structures with increasing Ta content. The predicted temperature dependence of specific heat capacity for the identified structures matches well with the Dulong–Petit, Kepp and Debye Models. Both vibration and configuration entropy contribute to the stabilization of alloy systems, while the latter is about 2–3 times greater than the former. The elastic constants and moduli vary with composition and phase structure. Ta atoms have preference to some atoms like Ni, and form relatively strong bonds with adjacent atoms. The introduction of Ta promotes the electron localization and favors the formation of mixed structures.