American Chemical Society Spring Meeting
Intellegens will be exhibiting at the American Chemical Society Spring Meeting in Indianapolis in March. Our Dr Tom Whitehead is also presenting on AI-accelerated workflows in the scientific program.
Details of Tom’s talk are provided below.
Visit our exhibition booth (#312) to find out how we’re helping scientists to apply machine learning to design new chemicals and formulations and accelerate their experimental programs.
Presentation details
Speaker: Dr Tom Whitehead, Head of Machine Learning at Intellegens
Title: Value from corporate chemical data: Leveraging historic project data using machine learning
Talk timing: Monday, 27 March, 09:15-09:35
Talk session: Division of Catalysis Science and Technology / AI-Accelerated Scientific Workflow
Talk location: Room 107 – Indiana Convention Center
Abstract:
Large chemical organizations have run dozens of R&D projects over the years: but typically the data from these projects languishes in corporate repositories (or even spreadsheets – not to mention filing cabinets) without providing value to ongoing research. Machine learning and artificial intelligence promise to revolutionize the design, development, and processing of chemicals and formulations, but to do so they need to make the best use of all available data, ideally without being constrained to data from a single chemistry research project. These two problems form natural complements: using legacy data we can provide a firm machine learning-driven foundation for new projects, creating a virtuous cycle where each new project stands on the increasingly lofty shoulders of its forebears.
We will describe projects at Johnson Matthey that used data from past projects to build machine learning models that provided powerful insights into their design of new catalysts, and how this was enabled by a forward-thinking approach to handling experimental data. We will also discuss the gaps in current data storage and processing approaches and how these gaps can be plugged, generating the maximum value from existing data with a minimum of effort.