What Is Material Informatics?

Materials informatics deals with the intersection between materials science and artificial intelligence particularly focusing on developing new materials, predicting the functional properties, and optimizing the composition for accelerated innovation and product development. Materials are like human beings possessing very distinct characteristics/ features generated from their inherent structural attributes and fabrication conditions.

Therefore, the performance and functional attributes of the materials are difficult to forecast and require long-term experimental approaches. These include mechanical performances, chemical behaviour, thermal, and thermo-mechanical properties. This conventional mode of the experimental approach is costly as well as time-consuming. Moreover, the process requires experience/expertise in performance evaluation/ characterization. In this regard, material informatics creates a platform to accelerate this process with the help of modern computing facilities such as artificial intelligence and machine learning to understand the nonlinearity between the product composition and performance attributes thereby accelerating product development.

The Polymer is a popular class of material that requires no introduction as it is largely involved in our daily usage. Therefore, bringing polymer science with material informatics together as polymer informatics will open up a wider perspective. It helps in developing new materials with optimal composition and performance attributes offering a versatile range of functional applications.

Polymerize Informatics with domain-specific expertise in polymer informatics is determined to provide the solution for accelerated innovation with an experienced team of young professionals led by Dr. Abhijit Salvekar and Mr. Kunal Sandeep.

How much data is needed for materials informatics?

The accuracy and acceptability of material informatics are governed by the relevant data availability and reliability. Similarly, the performance of the AI/ML engine runs with the quality and quantity of available data set which is system-specific. However, the system nonlinearity and desired output variables demand adequate data filtering and selection of control factors (directly influencing the outcomes).

In terms of determining the accuracy of the model, the utmost need is to understand the factors/ variables. Because these directly influence the outcome, therefore, require supreme expertise and understanding. With an experienced team of polymer scientists and engineers, Polymerize has successfully demonstrated the caliber to showcase its proficiency in data management and data filtering for developing the material informatics platform successfully.

Driven by the knowledge and experience of our employees, Polymerize has developed an opportunity to manage the available experimental dataset. It not only reduces tedious experimental exercise but also provides a thorough understanding of domains, emphasizing factors majorly influencing the outcomes.

ROI of Materials Informatics

Return of investment (ROI), is the ratio between the investment benefit and the investment cost. Creating materials for functional applications is time-consuming, and required synthesizing chemicals and engaging an experienced workforce to take several weeks/months.

On the flip side, AI requires only a few minutes to guide 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.

Reducing the number of experimental trials reduces initial investment costs.

Extravagant reduction of time leads to fresh profits.
Making processes robust, efficient and satisfying multiple objectives helps in the reduction of production costs.
Optimization increases product value and brings out unseen discoveries.
Codifying knowledge from experiments serves as a digital asset for future research.
Why Digitalization In The Materials Industry Is The Need Of The Hour?

Material industry is conventionally guided by the experience/ expertise of the researchers/scientists. Therefore, developing new material formulation and optimal tuning of the functional performance attributes are the major challenge where the one variable at a time (OVAT) and design of experiment (DOE) approach has been usually followed to understand the impact of different influencing factors on the performance/ fabrication process.

These experiments are time-consuming and require a lot of initial investment for the experimentation purpose thereby expected to be documented. Additionally, the enormous amount of data generated during multiple characterizations (such as mechanical, chemical, thermal, and thermo-mechanical) and functional performance evaluation, are unmanageable. Therefore, the Materials industry needs digitalization! Not only for managing the previous experimental data but also for using the earlier dataset in data feature extraction. Also, helps to understand the primary influencing factors on the outcome.

Contextually, the Material informatics platform by Polymerize would help to monitor the previous experiments in an organized way to classify/ manage the parameters (composition, fabrication process condition, etc.) and analyze the desired outcomes in terms of heat maps, pie-charts, and other interactive plots. Further, the dataset can utilize in the next level for AI/ML to predict the desired formulation/ functional properties. It mostly helps during product development/ process optimization.

Will AI Replace Scientists?

Materials science and engineering comprise different aspects of materials in terms of their structure-property relationship (thermal, mechanical, chemical, thermos-mechanical, etc.), functional performance, processability, and property-based applications. Therefore, from a product development point of view, the scientist and researchers have a complete understanding of the desirable system requirement corroborating the materials’ properties.

Similarly, the synthesis of new materials, optimal tuning of the compositions for desired performance, and process optimization require lots of hit and trial experiments in the lab. Also, they are difficult to forecast due to the complex nonlinearity associated with them. Henceforth based on the domain-specific knowledge/ expertise, the Materials informatics platform offered by Polymerize will guide the researchers/ scientists to reach/optimize the material properties, functional performances, and fabrication process by reducing the tedious trial and error method of repetitive experiments.

But AI won’t be replacing the knowledge and experience of scientists. Rather it will act as a catalyst in creating a scope for the experienced researchers to find out the affecting parameters for the desired outcome. They include the influencing controllable variables that are directly responsible for the experimental results, analyzing the outcomes of material informatics in scientific terms, guiding the material informatics platform towards desired and contextually significant experimental datasets, etc.

Therefore, the role of AI would be to catalyze material innovation. It is without affecting the scientists’ job of understanding and analyzing the inherent characteristics of the materials. However, AI will very soon become an indispensable tool in Materials science and engineering for the scientist to accelerate innovation and product development.

FUTURE OF MATERIAL INFORMATICS:

Material Informatics is at the heart of future material innovations and advanced materials technology. Sustainability is not only a theme for public relations and marketing narratives but it is a critical factor.

Along with designing a circular economy, material efficiency plays a vital role in achieving sustainability. Traditional methods of material innovation require a long time to develop and bring to market, often 20 years. Developing long-term sustainable roots would need even more effort and time. The future of material informatics allows complete domain knowledge to be captured in datasets, design spaces, and AI models, which becomes digital assets that can be reused in the future.

A broad range of industries is rapidly undergoing large-scale transformation powered by cheap computing. Expanded cloud-based database hosting infrastructure, omnipresent data collecting, and sophisticated artificial intelligence (AI). The future end goal is to have fully automated laboratories.