Polymer informatics seeks to address practical material development, design, and discovery of polymers using modern data and knowledge-based methods, inspired by emerging artificial intelligence (AI) and machine learning (ML) techniques. Surrogate models are trained in the available polymer data for rapid structural prediction, allowing the testing of promising polymer candidates with specific structural requirements. Data-driven techniques are used to address the unique challenges posed by the expansive diversity of chemical and physical properties of polymers on small and large scales.

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.

    One of the most promising uses of machine learning in materials science is sequential learning (SL). The idea is to employ machine learning as a “lab partner”: given experimental data, a scientist trains a machine learning model to predict material attributes of interest, and then uses that model to evaluate untested candidates. The scientist can choose new candidates to test experimentally using a combination of model output and physical intuition, then add the new data to a database, retrain the model, and repeat the cycle until satisfied. This loop is depicted in the diagram below:

Almost a quarter of total greenhouse gas is accounted to be caused by the production of materials. 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. 

But 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 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. 

AI has been employed in a growing number of sectors in recent years, and machine learning research in the field of materials is quickly progressing, particularly in terms of its ability to synthesize novel materials and anticipate diverse chemical syntheses. Machine Learning can assist humans in overcoming barriers in material design, synthesis, and processing.

The computational chemistry and materials science research process has been upgraded to the third generation. The first generation relates to “structure-performance” calculations, which primarily rely on a local optimization algorithm to predict material performance from the structure. The second technique is “crystal structure prediction,” which uses a global optimization algorithm to anticipate structure and performance based on element composition. Machine learning algorithms are used to anticipate the composition, structure, and performance of elements using physical and chemical data in the third generation, which is described as “statistically driven design.”

The theory’s flaws, on the other hand, have hampered the identification of high-performance materials, and the model’s parameters aren’t entirely consistent with real-world conditions. It is still necessary to use domain expertise to filter out these flaws when utilising AI. As a result, it is truer to say that AI is a tool that scientists use to assist them to make better decisions and that it is driven by the scientist’s domain knowledge and business decision-making. Scientists are not replaced by AI, rather, it saves them time. They can focus their knowledge on more successful initiatives now that they have fewer ineffective experiments to assess.

    There are numerous tools that give advanced AI/ML features; polymerize is one of them. It uses a brilliant synergistic strategy to produce the best results by combining AI/ML model and domain expertise. They offer the appealing feature of combining AI/ML models with Design of Experiments (DoE) to compensate for the lack of a pre-historic experiment dataset. Researchers can not only create an experiment from the ground up but also train their own AI/ML custom model to generate predictions, thanks to a persistent feedback loop mechanism.