Material Design of Porous Hydroxyapatite Ceramics via Inverse Analysis of an Estimation Model for Bone-Forming Ability Based on Machine Learning and Experimental Validation of Biological Hard Tissue Responses.

Hydroxyapatite and ß-tricalcium phosphate have been clinically applied as artificial bone materials due to their high biocompatibility. The development of artificial bones requires the verification of safety and efficacy through animal experiments; however, from the viewpoint of animal welfare, it is necessary to reduce the number of animal experiments. In this study, we utilized machine learning to construct a model that estimates the bone-forming ability of bioceramics from material fabrication conditions, material properties, and in vivo experimental conditions. We succeeded in constructing two models: 'Model 1', which predicts material properties from their fabrication conditions, and 'Model 2', which predicts the bone-formation rate from material properties and in vivo experimental conditions. The inclusion of full width at half maximum (FWHM) in the feature of Model 2 showed an improvement in accuracy. Furthermore, the results of the feature importance showed that the FWHMs were the most important. By an inverse analysis of the two models, we proposed candidates for material fabrication conditions to achieve target values of the bone-formation rate. Under the proposed conditions, the material properties of the fabricated material were consistent with the estimated material properties. Furthermore, a comparison between bone-formation rates after 12 weeks of implantation in the porcine tibia and the estimated bone-formation rate. This result showed that the actual bone-formation rates existed within the error range of the estimated bone-formation rates, indicating that machine learning consistently predicts the results of animal experiments using material fabrication conditions. We believe that these findings will lead to the establishment of alternative animal experiments to replace animal experiments in the development of artificial bones.

Catalyst Design and Feature Engineering to Improve Selectivity and Reactivity in Two Simultaneous Cross-Coupling Reactions.

Highly active catalysts are required in numerous industrial fields; therefore, to minimize costs and development time, catalyst design using machine learning has attracted significant attention. This study focused on a reaction system where two types of cross-coupling reactions, namely, Buchwald-Hartwig type cross-coupling (BHCC) and Suzuki-Miyaura type cross-coupling (SMCC) reactions, occur simultaneously. Constructing a machine-learning model that considers all experimental conditions is essential to accurately predict the product yield for both the BHCC and the SMCC reactions. The objective of this study was to establish explanatory variables x that considered all experimental conditions within the reaction system involving simultaneous cross-couplings and to design catalysts that achieve the target yield and the development of novel reactions. To accomplish this, Bayesian optimization was combined with established variables x to design new catalysts and enhance reaction selectivity. Moreover, the catalyst design in this study successfully pioneered new reactions involving Cu, Rh, and Pt catalysts in a reaction system that did not previously react with transition metals other than Ni or Pd.