Exfoliating B. mori silk into high aspect ratio nanofibrils facilitated by response surface methodology.

Silk fiber is formed by an assembly of fibrils. The fibrils can be isolated by a top-down mechanical process called microfibrillation and the fibrils are known as microfibrillated silk (MFS). The process involves chopping, milling, enzyme treatment and high-pressure homogenization.

Efficient Bayesian Function Optimization of Evolving Material Manufacturing Processes.

The scale-up of laboratory procedures to industrial production is the main challenge standing between ideation and the successful introduction of novel materials into commercial products. Retaining quality while ensuring high per-batch production yields is the main challenge. Batch processing and other dynamic strategies that preserve product quality can be applied, but they typically involve a variety of experimental parameters and functions that are difficult to optimize because of interdependencies that are often antagonistic.

Optimizing a High-Entropy System: Software-Assisted Development of Highly Hydrophobic Surfaces using an Amphiphilic Polymer.

In materials science, the investigation of a large and complex experimental space is time-consuming and thus may induce bias to exclude potential solutions where little to no knowledge is available. This work presents the development of a highly hydrophobic material from an amphiphilic polymer through a novel, adaptive artificial intelligence approach. The hydrophobicity arises from the random packing of short polymer fibers into paper, a highly entropic, multistep process.

Rapid Bayesian optimisation for synthesis of short polymer fiber materials.

The discovery of processes for the synthesis of new materials involves many decisions about process design, operation, and material properties. Experimentation is crucial but as complexity increases, exploration of variables can become impractical using traditional combinatorial approaches. We describe an iterative method which uses machine learning to optimise process development, incorporating multiple qualitative and quantitative objectives.