Structural descriptor and surrogate modeling for design of biodegradable scaffolds.

Biodegradable scaffolds are important to regenerative medicine in that they provide an amicable environment for tissue regrowth. However, establishing structure-property (SP) relationships for scaffold design is challenging due to the complexity of the three-dimensional porous scaffold geometry. The complexity requires high-dimensional geometric descriptors. The training of such a SP surrogate model will need a large amount of experimental or simulation data. In this work, a schema of constructing SP relationship surrogates is developed to predict the degraded mechanical properties from the initial scaffold geometry. A new structure descriptor, the extended surfacelet transform (EST), is proposed to capture important details of pores associated with the degradation of scaffolds. The efficiency is further enhanced with principal component analysis to reduce the high-dimensional EST data into a low-dimensional representation. The schema also includes a kinetic Monte Carlo biodegradation model to simulate the biodegradation of polymer scaffolds and to generate the training data for the formation of SP relationships. The schema is demonstrated with the design of polycaprolactone biodegradable scaffolds by connecting the initial scaffold geometry to the degraded compressive modulus.

Computational Analysis and Optimization of Geometric Parameters for Fibrous Scaffold Design.

Bioresorbable tissue scaffolds are a promising potential treatment for soft-tissue injuries, such as tendon and ligament rupture. These materials provide temporary support to the injured tissues and provide biological cues that promote healing. Previous work has shown that fiber alignment, diameter, and spacing affect cell morphology and migration, which impact healing of the target tissue. However, previous work has not fully characterized the isolated effects of fiber alignment, diameter, and spacing on cell morphology and migration, nor has it revealed the ideal combinations of diameter and spacing to promote cell migration and elongation on fibrous scaffolds. To clarify these effects, a mesoscale model was formulated to describe cell movement on a fibrous scaffold and analyze the isolated effects of fiber alignment, diameter, and spacing. After analyzing the isolated effects, an optimization was performed to find combinations of fiber diameter and spacing that maximized cell elongation and migration, which may lead to improved healing of the target tissue. This analysis may ultimately aid the design of scaffold materials to improve outcomes after tendon or ligament rupture.

Coarse-Grained Force Field Calibration Based on Multiobjective Bayesian Optimization to Simulate Water Diffusion in Poly-ε-caprolactone.

Molecular dynamics at the atomistic scale is increasingly being used to predict material properties and speed up the material design and development process. However, the accuracy of molecular dynamics predictions is sensitively dependent on the force fields. In the traditional force field calibration process, a specific property, predicted by the model, is compared with the experimental observation and the force field parameters are adjusted to minimize the difference. This leads to the issue that the calibrated force fields are not generic and robust enough to predict different properties. Here, a new calibration method based on multiobjective Bayesian optimization is developed to speed up the development of molecular dynamics force fields that are capable of predicting multiple properties accurately. This is achieved by reducing the number of simulation runs to generate the Pareto front with an efficient sequential sampling strategy. The methodology is demonstrated by generating a new coarse-grained force field for polycaprolactone, where the force field can predict the mechanical properties and water diffusion in the polymer.