Polymerize: AI Engine Solutions for Material Science and Informatics
Al engine is a powerful pattern recognition and learning tool. Polymerize AI Team developed each segment keeping the material science industry in mind and curated algorithms and processes to meet the needs of RD Scientists. Keeping up with artificial intelligence (AI), Data Science and Analytics helps the industry to create robust data-driven systems for achieving desired USPs. With the Curated learning and optimized algorithms that comprehensively meet growing market demands, the AI engine helps research and development (RD) scientists to gain fruitful insights. Utilizing technologies like Polymerize, data analytics, and machine learning resources to ensure that there is an adequate data-driven innovation/AI-driven approach to innovation. By utilizing AI-powered material informatics and data management solutions, clever industries have infinite potential to prosper in innovative materials and material design.
Polymerize for our Customers:
The emphasis of RD is to seek new technologies, integrate them into the business area, and develop strategies around them that can be transformed into technology or products. RD is the growth engine for any business. To meet the RD goals, Polymerize provides a workspace for efficient and streamlined pipelines and structured roadmap for product development. By speeding up research with ML, unnecessary tests are eliminated, and the finest formulations with the desired properties are forecasted, and vice versa. The researcher can get insights with less experience in material design using Al/ML models to acquire more accurate data.
The RD departments frequently produce breakthroughs that are either too advanced or too late. They just fail to seize the market nerve instantly. Despite being the most prevalent, this element causes significant losses financially as well as in time and effort. Polymerize's data management and data sharing functions aid in internal connectivity. Machine learning (ML) will quicken RD operations in the proper direction in harmony with customer needs by understanding market demands and detecting early industry trends.
Polymerize features seamless integration to Laboratory data management solutions, serving the customer with micro-level learning and macro-level impact. Through Polymerizes’ integrated workflows and an agile learning and feedback loop, one can use AI to identify all historic datasets and achieve the desired goal at each stage of the development process.
AI Engine at Polymerize :
The Al engine is powered by a thoughtfully articulated and fine-tuned set of reliable, consistent, high-performance predictive algorithms created by our team of AI Research Scientists and Engineers. The engine focuses on understanding and solving the core challenges of the Material Science domain making sure the AI does not work as a black-box instead is in sync with the theoretical and academic understanding of the field with domain expertise adding value at each granular level.
The complex, interconnected, dependent, linear, and non-linear interactions between chemicals and processes that enable the material sciences industry to develop revolutionizing materials can be recognized and understood using statistical ML algorithms, ensembles, neural networks, and reinforcement learning algorithms. The AI Engine also feeds on domain-specific theoretical frameworks, concepts, and features like PSP relationships, compound attributes, physiochemical properties, and functional relationships in addition to the recorded experiment data shared by our customers and the data extracted from patent and scientific literature. AI reliably and accurately accelerates the RD effort by layering highly complex models and extensive domain expertise.
Polymerize Exclusive Features of the AI engine :
The forward prediction includes the prediction of properties based on formulation/process parameters and vice versa. Our AI powered platform predicts the combination of formulation in high precision with as low as 25 data points. The integration, selection, ranking, and filtering of numerous traditional ML and AI models that are examined on various performance criteria over numerous iterations results in reliable and superior performance. The various models include linear, random forest, X-boost, gaussian process regressions, etc. At the Individual stage of the procedure, the best-fitted model was validated at every designed checkpoint. Polymerize supports incremental learning and re-learning with every newly performed experiment, and the platform automatically integrates the new data with existing models.
As walking toward the new product design, many RD face the challenge of limited existing knowledge of formulation. Polymerize with as low as 25 data points are fed to the AI engine with the domain expertise backed, and features like process-structure-properties (P-S-P) relationship, atomic properties of chemicals, metadata about the ingredient, contextual processing of experiments have gained the interest of industries. The fetched data from the open-sourced database, designed by Polymerize experts, digital data platforms, and scientific literature, make the AI engine even more precise with the low data set. With the data and domain-centric approach for building AI, the platform provides careful support of external data to customer data by complementing individual shortcomings based on size, complexity, relevancy, and availability. The systematic and synchronous mix of data from multiple sources guarantees repeatability and relevancy for long-term usage.
With the increasing demand for innovative materials, Polymerize supports the industry leaders by providing insights into materials and manufacturing processes. The platform is not only limited to formulation development and predicting properties for cutting down the experiments but also to achieving the defined properties/objectives by conducting AI-optimized experiments. The inverse prediction module has a broad focus on utilizing the relationships and consequences discovered by AI/ML models to develop experiments that are customized to fulfill business goals. The Inverse Module recommends AI-curated, optimized experiments that meet critically specific outcomes demanded by the customer. The recommendations are derived by using state-of-the-art Optimisation algorithms deeply rooted in the fundamental sciences of genetic evolution, bayesian statistics, and molecular theory.
The module can handle multiple objectives simultaneously, confine the bootstrap experiment sequence, and identify the sweet spot.
Data Summary and Analysis
The detailed insights of every performed experiment to understand the data and the interrelationship of the formulation. The data is considered to be the most precious food in the cuisine, and the detailed quantitative and qualitative analysis helps RD business material scientists to design product formulation according to the customer's taste by providing them with tabular analysis, interactive charts, and descriptive statistics.
The Correlation Heatmaps, as our powerful features, mapped the experimental learning with data-based learning. With the insights of these world-class products, Polymerize is one of the best business intelligence software. Statistical plots of distribution, composition, comparison and relationships conveniently provide messages visually to research and development scientists that help them to further improvise the experiments. The AI/ML models of Polymerize work with (a high degree of variability (vast range of composition) work previously with as low as 25 data points. The Data summary module can communicate the quality and data to be modeled with the AI engine.
Polymerize is aware of the fact that ML/AI models can memorize data and predict experiments based on the desired objective, but as we understand the research and development need, Polymerize also provides a strategically developed explainable AI engine that reveals the explainability behind predictions, not just the ANSWER, but an explanation of it.
Polymerize employs various techniques to unlock the mystery of ML models’ black box, including Game Theory approaches, Monte Carlo Simulations and other state of the art explainability algorithms. The micro-learning for each relationship is validated using theory, research, and experience to understand and fully develop trust in the models.
The aforementioned powerful features of Polymerize are interpreted with the learning quantitatively by gaining positive/negative impact at the ingredient-process-property level.
Let the AI Engine do the Hard Work