  There is an increasing need in our society to achieve faster advances in
Science to tackle urgent problems, such as climate changes, environmental
hazards, sustainable energy systems, pandemics, among others. In certain
domains like chemistry, scientific discovery carries the extra burden of
assessing risks of the proposed novel solutions before moving to the
experimental stage. Despite several recent advances in Machine Learning and AI
to address some of these challenges, there is still a gap in technologies to
support end-to-end discovery applications, integrating the myriad of available
technologies into a coherent, orchestrated, yet flexible discovery process.
Such applications need to handle complex knowledge management at scale,
enabling knowledge consumption and production in a timely and efficient way for
subject matter experts (SMEs). Furthermore, the discovery of novel functional
materials strongly relies on the development of exploration strategies in the
chemical space. For instance, generative models have gained attention within
the scientific community due to their ability to generate enormous volumes of
novel molecules across material domains. These models exhibit extreme
creativity that often translates in low viability of the generated candidates.
In this work, we propose a workbench framework that aims at enabling the
human-AI co-creation to reduce the time until the first discovery and the
opportunity costs involved. This framework relies on a knowledge base with
domain and process knowledge, and user-interaction components to acquire
knowledge and advise the SMEs. Currently,the framework supports four main
activities: generative modeling, dataset triage, molecule adjudication, and
risk assessment.
