Machine-Learning Chemical Formulation Seeds for Faster R&D
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Chemical and pharmaceutical industries face challenges in shortening product development lifecycles due to trial and error methods, stringent regulatory compliance, and a shortage of skilled manpower, exacerbated by an aging workforce, which hinders the generation of new product formulations.
Innovation Solution
A chemical product formulation system using machine learning (ML) to automatically generate seed formulae from historic experiments data, incorporating data preprocessing, supervised ML models, and analytical rules to ensure compliance with regulatory requirements, enabling efficient synthesis of chemical products.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If trial and error methods are used for developing new chemical products, then product formulations can be generated through experimentation, but the product development lifecycle is extended and time consumption increases
Solution Approach 1:
The system performs preliminary analysis by training machine learning models on historical experiment data before actual product development. This pre-training phase extracts analytical rules and builds predictive models that guide subsequent formulation development, eliminating the need for extensive trial-and-error experimentation and significantly shortening the development lifecycle while maintaining formulation validity
Solution Approach 2:
The system creates a virtual copy of the chemical formulation process through machine learning models that replicate the behavior and outcomes of physical experiments. These digital twins allow researchers to simulate and predict formulation results computationally, reducing the need for repeated physical trials and accelerating product development without sacrificing reliability
2Productivity
If more skilled manpower is deployed in R&D divisions, then new product formulations can be generated more effectively, but the shortage of skilled manpower and aging workforce constraints prevent this
Solution Approach 1:
The machine learning system serves itself by automatically training on historical data, generating analytical rules, and producing formulation recommendations without requiring extensive manual intervention. The system autonomously learns from past experiments and applies this knowledge to new formulation challenges, compensating for the shortage of skilled manpower while maintaining high productivity
Solution Approach 2:
The system replaces the mechanical dependency on skilled human researchers with an automated machine learning-based formulation system. The ML models perform the intellectual work of analyzing historical data, identifying patterns, and generating formulation recommendations, thereby decoupling productivity from the availability of skilled manpower and addressing the aging workforce challenge
3Reliability
If stringent compliance policies are enforced, then regulatory requirements are met and product quality is ensured, but the formulation development process becomes more complex and time-consuming
Solution Approach 1:
The system incorporates compliance checking into the preliminary model training phase by including regulatory requirements as constraints in the objective function. This ensures that formulations generated by the ML system are pre-screened for compliance with quality standards and regulatory policies, eliminating the need for separate compliance verification steps and reducing overall process complexity
Solution Approach 2:
The machine learning system performs multiple functions simultaneously: it optimizes formulation performance, ensures regulatory compliance, and generates interpretable analytical rules all within a single unified framework. The multi-objective optimization approach integrates compliance constraints directly into the formulation generation process, maintaining reliability while avoiding the need for separate compliance checking procedures
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A chemical product formulation system automatically generates seed formulae from historic experiments data for the synthesis of a chemical product. Independent and dependent features are identified from the historic experiments data and feature importance scores are calculated using a supervised machine learning (ML) model. The feature importance scores are used to build data structures from which analytical rules are extracted. The analytical rules are further processed to derive the seed formulae which are user-editable. The intermediate formulae generated via user edits of the seed formulae are further validated and approved in order to be used as the final formulae which are employed for the synthesis of the chemical product.