Materials Informatics – A Disruptive Technology
Materials science is the scientific domain, focused on understanding the distinct chemical and micro-structural attributes of the materials, correlating with their functional properties. Each existing material class irrespective of polymer, metal, or ceramics consist of unique structural feature making them aptly suitable for various functional applications. Therefore, the property and functional attributes for creating performance-specific material blends and composites remain a challenge in terms of long experimentation, consuming time, and cost expenses.
Have you ever imagined an opposite domain of science and technology i.e., material science and data informatics running hand in hand in accelerating the material innovation process and product development? Is it possible to remove the impenetrable barrier of performance prediction, and reverse engineering in the stochastic domain of Materials Science? With all these uncertainties, Materials Informatics comes up as a disruptive technology in recent days dealing with the emerging needs of the chemical and polymer industry for creating a sustainable solution in terms of designing and developing functional materials with specific performance satisfying the consumer-specific needs.
Benefits of Material informatics
Material informatics provides one step solution to customize the functional requirements of the consumer-specific material solution in terms of optimizing the composition and process parameters. Equipped with strong data-driven approaches alongside the domain knowledge to accelerate material innovation, the Material informatics platform creates a futuristic step toward identifying the data trends and analyzing the underlying mathematical nonlinearity between the ingredients with functional performance attributes. Understanding this specific relationship, later on, develops an opportunity for the researchers to optimize the product based on the requirement, therefore making the material innovation more technologically viable and cost-efficient process.
Some of the application areas of material informatics are as follows:
1. Matching materials and processes.
2. Materials with high service performance.
3. Escalating materials efficiency.
4. Materials designed for enhanced environmental performance.
5. Modelling and simulation – property & failure prediction.
6. Material design for functional performance.
7. Material selection which compliments the product design.
8. Material solutions which promote sustainability.
Polymerize is helping companies with faster material innovation
Material informatics uses the principle of machine learning to accelerate the material innovation process, whereas Polymerize focused to provide solutions combining domain knowledge and advanced analytics. Integrating the vast amount of data generated from various sources such as ongoing research, manufacturing facilities, and customer requirements. Polymerize provide an effective way to scale and adapt the material development process, particularly focusing on polymer technology and organic compound synthesis. In the domain of material science and engineering, when the leading industries are focusing on heavily equipped research infrastructure for material development, our material informatics platform provides a cost-viable way to accelerate the innovation process through data management and artificial intelligence/ machine learning approaches. There are two functional components in polymerize one is ML/AI and the other one is Material science and Engineering. Together we pledge to deliver the material innovation solution towards sustainable environment-friendly growth for the materials technology.
ROI of materials informatics
Return of investment (ROI), is the ratio between the investment benefit and the investment cost. Creating materials for the functional applications is time-consuming and required synthesizing chemicals and engaging an experienced workforce which will take several weeks/ months. On the flip side, AI requires only a few minutes to guide the researchers toward the most likely experiments to bring success. Therefore, the trial and error are replaced with a domain-specific data-driven path. The material informatics platform from Polymerize guides the product formulation process based on the earlier dataset and suggests the most likely condition for reducing the uncertainty. Moreover, the suggested formulations are tested and the results are added to the platform for improving the desired accuracy of the model. Therefore, achieving high-performance adequate formulations quicker than the trial and error method. These are the advantages the material informatics platform of Polymerize brings regarding the product development process.
1. Reducing the number of experimental trials reduces initial investment costs.
2. Extravagant reduction of time leads to fresh profits.
3. Making processes robust, efficient and satisfying multiple objectives helps in the reduction of production costs.
4. Optimization increases product value and brings out unseen discoveries.
5. Codifying knowledge from experiments serves as a digital asset for future research.
Will AI replace scientists?
AI refers to the algorithm that simulates human intelligence by mimicking the cognitive function of perception, learning, and problem-solving. According to one of the founders of AI, Prof. John McCarthy, “Artificial Intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs. Artificial Intelligence is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable”.
So the simple answer to this question is No. AI wouldn’t be replacing the scientists rather it will act as a catalyst to find out the affecting parameters for the desired outcome, thereby empowering the experienced researchers with better data accuracy and informed decisions. This will result in saving time, cost and efforts.
Material informatics means for the future
The innovation of sustainable materials has recently focused on solving energy and environment-friendly solution for numerous functional applications. Which requires expensive experimentation and state of an art research facilities. To cope with the recent market demand, accelerated innovation is necessary. Therefore, linking the domain-specific knowledge with advanced computational and analytical tools paved the way for adapting the material innovation process under specific applications. Ranging from conventional polymer composite and nanocomposites, to recently trending additive manufacturing the involved compositional as well as processing parameters require robust optimization, maximizing productibility alongside the cost viability. Where the simple models or human analytics fail to identify and/ or model the relationship between the compositional aspects and the material’s performance attributes in terms of scientific and technical knowledge, our AI/ML-enabled material informatics platform focused on mapping the stochastic parameters. Therefore, the Material informatics platform by Polymerize comes with a one-step solution for rapid cost reduction in material innovation through accurate predictability and parametric optimization.
Material informatics, digitalization, and material industry
The materials industry is moving forwards toward inventing future-ready materials with an adequate set of functional properties. For achieving their goal, the well-equipped research and development facilities focus on experience and expertise-based domain knowledge for developing futuristic materials. On the other hand, the digitalization of the experimental data with the help of the Material informatics platform from Polymerize helps to optimize the product formulation and process parameters more accurately. The earlier developed dataset during product development through digitalization is used to train the AI model which helps later on in predicting the product composition and parameter optimization vis-à-vis accelerating the invention process. Therefore, digitalization of the experimental data is used to feed the AI model to keep it future ready.
The Material informatics platform by Polymerize helps to digitalize the valuable experimental dataset which not only helps in industries’ future endeavors but also creates an opportunity to flexibly diversify the business from one sector to another listing out the valuable functional properties of the developed material composition. The data trend analysis and parametric heat map help in gathering the scientific outcomes of the experimentation to boost up the domain knowledge, which helps scientists to understand the system more accurately helping in decision making. Additionally, equipped with reverse engineering the desired material combination may be well achieved with the Polymerize platform making the experimentation more logical reducing the unnecessary trials.
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