Polymerization and New Materials Synthesis through AI Deep Learning
New Polymerization Material >< AI Intersect
What if we want to have a new polymer that can be used to accept certain proteins and reject the growth of harmful bacteria? This can prove useful in the medical industry that uses cellular-level innovations. In general, these research efforts are exploring the intersection of materials science and AI, using machine learning and other AI techniques to accelerate the discovery and optimization of new materials with specific properties for a wide range of applications. The development of new polymers is a key area of focus, given the critical role that polymers play in many areas of modern technology, from electronics to energy storage to medicine.
Moreover, it is possible to train an AI model to predict the properties and structures of polymers based on a set of input compounds. This approach is known as "inverse design," where the goal is to predict the desired properties of a polymer and then work backward to identify the monomers and reaction conditions needed to synthesize it.
To train such a model, it would be necessary to provide it with a large dataset of known polymers and their corresponding monomers, reaction conditions, and properties. The model could then use this data to learn patterns and relationships between the input compounds and the resulting polymer structures and properties.
Once the model is trained, it could be used to predict the properties of a polymer based on a set of input compounds and guide the selection of monomers and reaction conditions to synthesize the desired polymer.
AI Predictive Models Enhance Accurate Discovery
Predictive learning models are being used by researchers to expedite the design of new polymers customized to the required functionality or specification. The possibilities are now limited only by the imagination of the AI Model designer. The near future will usher in and be filled with a vast variety of new and more effective but inexpensive polymers and materials all synthesized from accurate predictions by these tried and tested trained AI models.
Making new Polymer Brush Films a Breeze
Source: © Tokyo Institute of Technology
Polymer brush films as per the above illustration consist of monomer chains grown in close proximity to a substrate. The monomers, which look like "bristles" at the nanoscale, form a highly versatile functionality that the resulting polymer bristle coating can selectively adsorb or desorb a variety of chemicals or biological molecules. These polymers can be used to create environmentally friendly, antifouling marine paints that can prevent mussels and other organisms that attach to floating objects like ships in the ocean called "fouling organisms." This negative term originated as a reference to the way they slow the speed of a ship when they build up on the bottom of a hull.
A recent study by a research group led by Associate Professor Tomohiro Hayashi from Tokyo Institute of Technology (Tokyo Tech), Japan, published in ACS Biomaterials Science & Engineering, has used machine learning to predict these interactions and identify the film characteristics that have a significant impact on protein adsorption. The team fabricated 51 different polymer brush films of different thicknesses and densities with five different monomers to train the machine-learning algorithm. They then tested several of these algorithms to see how well their predictions matched up against the measured protein adsorption. "We tested several supervised regression algorithms, namely gradient boosting regression, support vector regression, linear regression, and random forest regression, to select the most reliable and suitable model in terms of prediction accuracy," says Dr. Hayashi.
In their study, the team fabricated 51 polymer brush films of different thicknesses and densities with five monomers to train the machine learning algorithm. They then tested several of these algorithms to see how well their predictions matched up against the measured protein adsorption. "We tested several supervised regression algorithms, namely gradient boosting regression, support vector regression, linear regression, and random forest regression, to select the most reliable and suitable model in terms of prediction accuracy," says Dr. Hayashi.
Source: HAYASHI LABORATORY
Out of these models, the random forest (RF) regression model showed the best agreement with the measured protein adsorption values. Accordingly, the researchers used the RF model to correlate the physical and chemical properties of the polymer brush with its ability to adsorb serum protein and allow for cell adhesion.
"Our analyses showed that the hydrophobicity index, or the relative hydrophobicity, was the most critical parameter. Next in line were the thickness and density of polymer brush films, the number of C-H bonds, the net charge on the monomer, and the density of the films. Monomer molecular weight and the number of O-H bonds, on the other hand, were ranked low in importance," highlights Dr. Hayashi.
Given the highly varied nature of polymer brush films and the multiple factors that affect the monomer-protein interactions, adopting machine learning as a way to optimize polymer brush film properties can provide a good starting point for the efficient design of anti-biofouling materials and functional biomaterials.
Source: Materials provided by Tokyo Institute of Technology.
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