Accelerating discovery through high-throughput experimentation and machine learning

Canonical methods of molecular discovery and reaction optimization rely on “trial-and-error” approaches and slow experimentation with low discovery rates. By harnessing high-throughput experimentation (HTE) with machine learning (ML) methods, artificial intelligence (AI) and robotics, we have the potential to dramatically accelerate the discovery and preparation of next generation molecules and materials. We will extract, unify, and transform data from literature into actionable intelligence, and generate a robust workflow for the automated synthesis of catalysts and resins at NOVA Chemicals. Through ML models, we will leverage newly-generated data to guide experiments and simulations, enabling rapid molecule development, and culminate in the inverse design of molecules and materials targeting function rather than a particular molecular structure. By combining the expertise, software, and hardware tools of the Hein Lab with the instrumentation and extensive database at NOVA Chemicals, we will create a closed-loop, self-driving laboratory that will (i) be capable of implementing a diverse range of chemical workflows and (ii) create datasets that will be leveraged by AI, allowing users to navigate complex structure-function relationships and experimental landscapes.

Faculty Supervisor:

Jason Hein

Student:

Wei Ling Chiu

Partner:

NOVA Chemicals

Discipline:

Chemistry

Sector:

Manufacturing

University:

University of British Columbia