Plastic pollution is one of the most pressing environmental issues, and the increase in the production of disposable plastics does not help at all. These plastics would often take many years before they degrade, which poisons the environment. This has prompted efforts from nations to create a global treaty to help reduce plastic pollution.
A combination of machine learning and artificial intelligence has accelerated the design of making materials, including plastics, with properties that quickly degrade without harming the environment and super-strong lightweight plastics for aircraft and satellites that would one day replace the metals being used.
The researchers from the Pritzker School of Molecular Engineering (PME) at the University of Chicago published their study in Science Advances on October 21, which shows a way toward designing polymers using a combination of modeling and machine learning.
This is done through computational structuring of almost 2,000 hypothetical polymers that are large enough to train neural links that understand a polymer's properties.
The Challenges in Designing Polymers
People have been using products with polymer, like plastic bottles, for so long as this material is very common in many things in the daily lives of humans.
Polymers are materials that have amorphous and disordered structures that even techniques for studying metals and crystalline materials developed by scientists have a hard time defining it. They are made of large atoms arranged in a very long string that might compromise millions of monomers.
Moreover, the length and sequence can affect the polymer molecule's properties that may vary depending on which the atoms are arranged. Due to that, a trial-and-error method will not be ideal to use because it is only limited, and generating the needed data for a rational design strategy would be very demanding, Phys.org reported.
Fortunately, machine learning could solve this problem as researchers set to answer whether machine learning and AI can predict the properties of polymers based on their sequence. If this might be the case, how large of a dataset would be needed to teach underlying algorithms.
Database for Learning Polymer Sequences and Designing Polymers
The researchers used almost 2,000 computationally structured polymers that have different sequences in creating the database. They also ran molecular simulations to predict its behavior.
Juan de Pablo, Liew Family Professor of Molecular Engineering and lead researcher, said that they are unsure how many are the different polymer sequences needed to learn its behavior as it could be millions. Fortunately, only a few hundred would do, which means that they can now follow the same technique ad create a database to train the machine learning network.
Then the researchers proceeded to use the data that was learned in making the actual design of the new molecules. They were able to demonstrate to specify a desired property from the polymer, and using machine learning generated a set of polymer sequences that lead to specific properties.
Through this, companies can now design products that save the environment and design polymers that do exactly what they want to do. For instance, they could create polymers that could someday replace the metals used in aerospace or those used in biomedical devices. It could allow engineers to more affordable and sustainable polymer materials.
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