An AI-assisted Workbench for Material Discovery

Abstract

In our evolving society, many problems such as climate change, sustainable energy systems, pandemics, and others require faster advances. In chemistry, scientific discovery also involves the critical task of assessing risks associated with proposed novel solutions before moving to the experimental stage. Fortunately, recent advances in machine learning and AI have proven successful in addressing some of these challenges. However, there remains a gap in technology that can support the development of end-to-end discovery process, which seamlessly integrate the vast array of available technologies into a flexible, coherent, and orchestrated system. These applications must manage complex knowledge at scale, enabling subject matter experts (SMEs) to efficiently consume and produce knowledge. Moreover, the discovery of novel functional materials heavily relies on the development of exploration strategies in the chemical space. For instance, generative models have gained attention due to their ability to generate vast volumes of novel molecules across material domains. However, the high level of creativity these models exhibit often translates into low viability of the generated candidates. To address these challenges, we propose a workbench framework that facilitates human-AI co-creation, enabling SMEs to reduce time-to-discovery and the associated opportunity costs. This framework relies on a knowledge base with domain and process knowledge and user-interaction components to acquire knowledge and advise the SMEs. The framework currently supports three main activities: generative modeling, dynamic dataset triage, and risk assessment.