Designing an AI assistant for chemical discovery

Abstract

Accelerating the discovery process is synonymous with use of Artificial intelligence (AI). We can harness the power of AI to help predict outcomes, make decisions or generate new artifacts guided by desired attributes. It is important to ensure a seamless integration of the technology with the humans in charge to remediate loss of control and skill, and to engender trust in the capabilities of the AI system to ensure optimal results. In most cases however the subject matter experts (SMEs) may be left guessing as to the capabilities of the new system they are asked to use. They may have to spend time familiarizing themselves with a new interface or learn an entirely new skill like coding to launch and use APIs. We bridge this gap through a human-centered approach to design of such AI systems. We conducted a two-part study with SMEs to understand their needs, wants and expectations in the replacement of PFAS materials and potential role of an AI assistant. First, we interviewed seven chemists using a think-out-loud protocol while attempting to find a fluorine free superacid for photolithography using tools of their choice. This was followed up with questions about what role an AI assistant could play in helping them achieve these same tasks. We gained insights into chemists’ methodology in tackling the discovery process, the types of tools currently used to achieve this, and how an AI assistant could fill gaps in current technologies and provide user-friendly interface that can help experts focus on the innovation process. Next, we organized feedback sessions with six of the same chemists to present storyboards on various design scenarios. The vignettes showcased ideas to support individual as well as collaborative contributions, while utilizing a conversational AI assistant to search, generate, visualize, manipulate and curate solutions.