Artificial Intelligence (AI) is expected to have a significant impact on the engineering plastic and composite industry. As a result thereof, polymers and composites with new properties can be designed at a much faster pace and will unlock an even broader field of applications. Secondly, there will be an increase in competitiveness, since it will be easier to gain intelligence and insights from large and complex sets of data. AI may reduce the hurdle to engage in radical innovations and eventually become the new technological paradigm, the newcomer companies leapfrog ahead of the formerly leading firms

AI is expected to have a significant impact on polymer research, revolutionizing various aspects of the field. Here are several ways in which AI can influence polymer research:

1.     Materials Design and Discovery

AI can assist in accelerating the process of designing and discovering new polymers with desired properties. By employing machine learning algorithms, AI can analyze large databases of existing polymer data, identify patterns, and generate predictions regarding the properties of new polymer compositions. This can potentially lead to the development of novel materials with tailored characteristics, such as improved strength, flexibility, or biocompatibility.

2.    Property Prediction and Optimization

AI techniques, such as computational modeling and simulation, can aid in predicting the physical, chemical, and mechanical properties of polymers. These predictions can guide experimental efforts, enabling researchers to focus their attention on the most promising polymer candidates. Additionally, AI algorithms can optimize polymer formulations by considering multiple variables and constraints, leading to the creation of polymers with optimized properties for specific applications.

3.     Process Optimization

AI can optimize polymer manufacturing processes to enhance efficiency, reduce costs, and minimize waste. By analyzing sensor data and real-time process variables, AI algorithms can identify process parameters that influence the final properties of polymers. This information can be used to optimize process conditions, such as temperature, pressure, or reaction time, leading to improved product quality and reduced energy consumption.

4.     Polymer Characterization

AI can facilitate the analysis and characterization of polymers. By leveraging image recognition and pattern recognition algorithms, AI can automatically analyze microscopy images, spectroscopic data, or other characterization outputs. This can speed up the analysis process, improve accuracy, and enable more comprehensive characterization of complex polymer structures.

5.     Data-driven Insights

AI can assist in uncovering hidden relationships and trends in polymer research data. By processing vast amounts of experimental data, scientific literature, and patents, AI algorithms can identify correlations, propose hypotheses, and generate new insights. This can guide researchers in formulating new research questions, directing experimental design, and exploring unexplored areas of polymer science.

6.     Sustainable Polymer

Development: AI can contribute to the development of sustainable polymers by optimizing material formulations and manufacturing processes to reduce environmental impact. By considering factors such as recyclability, biodegradability, and eco-friendly synthesis routes, AI can help researchers design and develop polymers with improved sustainability profiles.

Example - Focus on precision medicines

At Mitsubishi Chemical Group, we aim to realize precision medicine that takes into account the causes and phenotypes of diseases in the central nervous system and immuno-inflammation. Our precision medicine approach provides appropriate healthcare to patients at appropriate times, considering the differences in people’s genes, environment, and lifestyles. MT-7117, currently under development for erythropoietic protoporphyria and systemic sclerosis, is our leading precision medicine program.

We are promoting digital technologies to realize precision medicine. For example, we are collaborating with academia and AI venture companies to develop AI technology for drug screening. This technology enables accelerated drug evaluation using large-scale image data, instead of time-consuming data review by highly skilled researchers. We anticipate applying this technology in the future to drug screening based on genetic polymorphism and drug screening using human induced pluripotent stem (iPS) cells from patients.

It's important to note that while AI has the potential to accelerate polymer research, it still requires collaboration between AI experts and polymer scientists to ensure the accurate interpretation and utilization of AI-generated results in a meaningful way. Secondly, practical work in laboratories by bright scientists has been and will remain a source of innovations too!

Are you using AI in your polymer research? What is your experience?

...and yes, this article was generated with support of AI.