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The Problem with Traditional Material Development

The conventional approach to material development relies heavily on trial-and-error methodologies. This process is not only slow but also resource-intensive, with no guarantee of success. Researchers often spend months — or even years — experimenting with different formulations, only to hit roadblocks. The result? Delayed time-to-market and inflated R&D budgets.

Polymerize addresses these challenges head-on by replacing guesswork with data-driven predictions. By harnessing the power of AI, Polymerize enables businesses to cut development time to 1/3rd and achieve R&D cost savings of up to 60%.

Introducing Polymerize: The Future of Material Development

Polymerize is Asia’s leading cloud-based material informatics platform, designed to revolutionize how materials are developed. Its suite of products and services empowers researchers to predict formulations, optimize experiments, and make informed decisions — all while saving time and money.

Polymerize’s Top 3 Products & Services

1. Polymerize Labs

Polymerize Labs is the cornerstone of Polymerize’s offerings. This AI-powered platform predicts formulations and material properties, enabling researchers to 3x their research speed. By replacing trial-and-error with AI-driven decision-making, Polymerize Labs generates accurate predictions and even creates work orders to streamline experiments. The result? A significant reduction in time spent on experiments and a faster path to innovation.

2. Polymerize Connect

Collaboration is key to successful R&D, and Polymerize Connect makes it easier than ever. This platform facilitates seamless communication and data sharing among researchers, integrating effortlessly with existing workflows. By enhancing collaboration, Polymerize Connect accelerates the material development process and drives innovation.

3. Material Informatics

Polymerize’s Material Informatics solution takes data-driven material development to the next level. It provides accurate predictions of experimental outcomes, allowing researchers to make informed decisions before executing experiments. This not only increases the probability of success but also optimizes R&D efforts, saving time and resources.

How Polymerize Predicts Formulations and Properties

Polymerize’s AI and machine learning capabilities are at the heart of its success. By analyzing vast amounts of data, the platform predicts formulations and material properties with remarkable accuracy. Researchers can input their requirements, and Polymerize generates actionable insights, enabling them to optimize experiments and achieve faster results.

For example, Polymerize can predict what a material’s properties will be before any experiments are conducted. This eliminates the need for repetitive testing and ensures that every experiment is purposeful and efficient.

The Impact of Polymerize on Material Development

The benefits of Polymerize are clear:

Faster time-to-market: Accelerate material development and bring products to market quicker.

Cost savings: Reduce R&D expenses by up to 60%.

Improved accuracy: Increase the success rate of experiments with data-driven predictions.

Polymerize is not just a tool — it’s a game-changer for material science. By eliminating inefficiencies and empowering researchers with AI, Polymerize is shaping the future of material development.

Conclusion

In a world where speed and efficiency are critical, Polymerize stands out as a leader in material informatics. Its innovative solutions — Polymerize Labs, Polymerize Connect, and Material Informatics — are transforming how materials are developed, saving time, and reducing costs.

Polymerize Connect is the ultimate cloud-based data management platform that acts as a bridge between researchers and their experimental data. This platform ensures that the information is organized, accessible, and secure. With Polymerize Connect, project owners have a real-time 360-degree view of their projects and work orders, enabling informed decisions based on desired outcomes. Our cutting-edge technology brings scientists, researchers, and business users together to plan, execute, and manage complex research initiatives. Get one month free trial access to the platform - Register here.

What is a Data Management System (DMS) and a Cloud-based DMS?

A Data Management System (DMS) is a software system used for the creation and management of databases. With DMS, users can create, read, edit, protect, or delete data in a database. The rise of cloud technology has led to an increase in interest and excitement in the tech industry.

A Cloud-based Data Management System (DMS) allows users to share resources and exchange information on devices located in different locations, all connected through an internet connection. The number of these cloud-based DMS is expected to grow in response to the increasing demand for information sharing among multiple stakeholders to foster a collaborative research environment. And as infrastructure becomes more available, these applications will become more economical with even higher returns on investment. According to research and industry surveys, there has been a significant increase in outsourcing DMS tasks to third parties who can provide the services at low cost.

DMS in Polymer Science

The field of combinatorial chemistry, sustainability goals, and high-throughput experimentation has revolutionized the discovery and functional applications of materials in both academic and industrial settings. The result of this increased material discovery is a large amount of experimental data that requires management, organization, analysis and storage.

In the polymer industry/polymer science, there is a pressing need for a chemical and polymer-specific data management system to meet the diverse information requirements of the field. A flexible and standardized polymer data management system is required to meet the diverse needs of users. With the development of computer networks, distributed systems and databases, and workstations, there will be additional challenges in populating and managing data across systems and departments.

To address these challenges, a collaborative and workflow-driven data management system is required that can accommodate diverse use cases and be customized. Polymerize Connect aims to solve these challenges by standardizing unstructured data from multiple sources. In fact, Polymerize Connect can significantly increase R&D efforts by providing a powerful data management system that can be used across various domains, including paints, adhesives, rubbers, specialty chemicals, textiles, automotive etc.

Polymerize Connect offers unparalleled benefits for R&D operations. Our platform boasts purpose-designed features that transform experimental data into insightful information that will help in decision making.

Some of its key benefits are:

Handling Complex, Diverse, and High-Volume Data: 

Research institutions often face challenges with the complexity, diversity, and volume of data. Our dedicated data management system minimises human error, enhances resource efficiency, and ensures the production of quality results. It provides a seamless ecosystem for handling demanding data.

Data Transparency: 

Projects frequently require collaboration across multiple teams, which can lead to poor data handling and human error. Polymerize Connect maintains logbooks and assigns a hierarchy of ownership to overcome these issues. It also prevents accidental errors and provides full transparency to all team members, ensuring they are aware of any modifications made.

Data Security: 

Sharing data across teams can be risky, particularly given the complexity and sensitivity of data. Loss of crucial data is also a major concern with scientists. Polymerize Connect establishes effective administrative, preventative, and detection controls to ensure proper access to the database. It features data encryption and security policies integrated at the data and application layer, adhering to the highest security standards and protocols.

Polymerize Connect offers a custom workflow for research needs, including:

Agnostic Database: 

All your historical data can be imported without changing the format, saving you the effort of cleaning or modifying it.

Data Provenance: 

Our platform provides full control over data, with detailed logs kept for each stage, progress, or modification of data. Any data can be traced back to its origin.

Data Analysis: Allow your database to speak to you. Our platform helps you analyze and extract valuable insights or even discover new and hidden relationships. It offers all the functions required to streamline your R&D operations.

Team Collaboration: Polymerize Connect enables more effective collaboration with distributed teams by creating a virtual workspace. Users can send files, comment on experiments/processes, and work on various workflows, enhancing productivity and fostering seamless collaboration.

In the current era of sustainable development, optimization has become complex due to technical, economic, and environmental factors. Industries are increasingly digitizing to gain an advantage in their respective fields.

With a diverse customer base across industries such as chemicals, polymers, paints and coatings, rubber, food packaging, textiles, and other material industries, Polymerize Connect is designed to drive data-driven decisions in any environment.

Our robust data management solution converts research data into valuable assets, integrating all your projects, formulations, spreadsheets, and experimental data into one secure platform.

Our built-in visualization functions allow users to explore and analyze data for deeper insights, expediting research and development.

Founded by Abhijit Vijay Salvekar, Ph.D. & Kunal Sandeep, we at Polymerize provide a cloud-based material informatics platform that enables R&D teams to collaborate across different geographies for chemical companies. Our platform effectively manages all the past & current experimental data, assists in designing new experiments, and generates AI-based recommendations for new formulations and alternative ingredients with desired properties. Overall reducing their R&D costs while accelerating the time-to-market.

Data management features offered by Polymerize:

1) Unified Database: Get all your prehistoric data imported without having to change the data format. This means no extra effort is required in cleaning or modifying the data.

2) Data Provenance: Our platform allows users to have full control over their data. Detailed logs are maintained at each stage of data ingestion and processing.

3) Data Analysis: Allow your data to talk to you. The platform helps you analyse and draw out valuable insights or even discover new relations. Get all the functions required to drive your R&D functions with ease.

4) Team Collaboration: The intuitive design helps boost productivity and team collaboration irrespective of the location. Users can exchange files and notes while discussing and engaging in real-time at each stage of experiments development.

5) Customisable Live Dashboard: Enjoy the benefits of a dynamic, adaptive and customisable dashboard. Get updates on all the data stored inside the system and the latest news on the materials being worked on.

Our in-built data management feature helps to eliminate scattered databases with a single unified workspace, empowering the R&D team with powerful data insight to discover operational issues, and make better data-driven decisions with complete visibility. Spend less time collating and conducting analysis manually.

Enough of Zoom and Google Meets! It's time to meet in person.

Polymerize Labs will be participating in India Chem Expo 2022 on the 2nd and 3rd November 2022, at Pragati Maidan, New Delhi.

We are cordially inviting our community and experts from the Chemical and Materials industry to attend our two-day in-person event.

We will be providing a Product demo session on how to expedite your Research process to save time and cost for your company. Whether you are a Research Scientist or Head of Innovation, it’s great to get hands-on knowledge from our team of experts.

Click the below link to fill the Registration Form and Book Your Time slot.

Registered users will be entitled to receive:

Exclusive gifts at the venue!

Our case study in your mailbox!

The Polymerize Material Informatics R&D Cloud

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.

Fig: DOI – https://doi.org/10.1007/s10853-021-06324-z

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.

SEQUENTIAL LEARNING: 

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:

FUTURE OF MATERIAL INFORMATICS:

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. 

WILL AI REPLACE SCIENTIST?

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.