What If You Could Start With the Polymer Properties You Need and Work Backwards Toward the Chemistry Most Likely to Deliver Them?
Polymer development traditionally moves forward from chemistry to performance. Researchers select monomers, molecular architecture, additives and processing conditions, prepare candidates, characterize them, and progressively refine the formulation.
Polymer informatics is beginning to make the opposite direction possible. Machine learning models can connect polymer structure, composition and experimental data with properties such as glass-transition temperature, thermal stability, mechanical performance, dielectric behaviour, permeability and other application-relevant characteristics. Once reliable structure-property relationships are established, the same models can help screen large candidate spaces and identify polymer structures or compositions that are more likely to meet defined performance targets.
The opportunity is significant, but polymer data creates challenges that conventional molecular AI does not solve automatically. Repeat-unit representation, molecular-weight distribution, copolymer composition, architecture, morphology, processing history and limited experimental datasets can strongly influence whether a prediction is scientifically useful or misleading.
This advanced expert-led training focuses on how polymer scientists can practically use machine learning, polymer informatics and inverse-design approaches for property prediction, candidate screening and materials discovery without requiring specialist programming expertise. You will examine how polymer data is represented, prepared and interpreted; how predictive models are built and validated; how inverse design changes the conventional development sequence; and where experimental confirmation remains essential before an AI-generated candidate becomes a credible R&D decision.
Why Attend?
Build useful polymer structure-property datasets
Select meaningful polymer representations and descriptors
Predict important polymer performance properties
Evaluate model uncertainty and applicability
Screen candidates against multiple property targets
Apply inverse design to polymer development
Who Should Attend?
This advanced training is particularly relevant for:
Polymer and materials R&D scientists
Polymer synthesis and development chemists
Plastics and compounding formulation scientists
Coatings, adhesives and elastomer researchers
Computational materials and polymer informatics teams
Technical leaders introducing AI into polymer R&D
No specialist AI or programming background is assumed. The session is designed to build on existing polymer and materials knowledge.
Training Outline
Polymer Informatics for R&D
Representing Polymer Chemistry for AI Models
Building Reliable Polymer Datasets
Polymer Property Prediction
Model Validation, Uncertainty and Applicability
Multi-Property Screening and Candidate Prioritization
Inverse Design and Generative Polymer Discovery
Experimental Validation and Practical R&D Applications
Case Studies
Expert Q&A Session
Move From Searching Polymer Chemistry to Designing Toward the Performance You Need.
Use polymer data, machine learning and inverse design more effectively to narrow experimental space, prioritize stronger candidates and make better-informed R&D decisions. Register Now.
