Timezone: »

Toward Human-AI Co-creation to Accelerate Material Discovery
Dmitry Zubarev · Carlos Raoni Mendes · Emilio Vital Brazil · Renato Cerqueira · Kristin Schmidt · Vinicius Segura · Juliana Ferreira · Daniel Sanders
Event URL: https://openreview.net/forum?id=DQ8aW21wK-b »

There is an increasing need in our society to achieve faster advances in Science to tackle urgent problems, such as climate changes, environmental hazards, sus- tainable water management, sustainable energy systems, pandemics, among others. The urgency of scientific discovery in chemistry carries the extra burden of as- sessing 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 productive 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, gener- ative 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 the context of materials discovery, viability is a complex metric evaluated by SMEs from complementary domains, such as synthetic organic chemistry, process scale-up, intellectual property development, regulatory compli- ance, and such. In this scenario, we observe an excellent opportunity to incorporate AI techniques to support SMEs, as well as the need for a platform to exploit the human-AI interaction focusing on reducing the time until the first discovery and the opportunity costs involved. In this work, we propose a workbench framework for the human-AI Co-creation to accelerate material discovery, which has four main components: generative models, dataset triage, molecule adjudication, and risk assessment.

Author Information

Dmitry Zubarev
Carlos Raoni Mendes (IBM Research)
Emilio Vital Brazil (IBM Research)
Renato Cerqueira (IBM Research)
Renato Cerqueira

Renato Cerqueira is Senior Research Manager at IBM Research Brazil, where he leads the Knowledge-centric Systems group, which creates technologies to enable Human-AI co-creation in knowledge intensive processes, such as scientific discovery, decision making under uncertainty and data interpretation. Renato and his team have been exploring the application of their research to different problems in Materials Design, Geosciences, and Finance, in partnership with several clients and external collaborators. Prior to join IBM Research, Renato was professor at the Department of Informatics at PUC-Rio, where he pursued research on Distributed Systems and Software Engineering and advised several Ph.D. and M.Sc. students. During 2001, he was Visiting Researcher at the University of Illinois at Urbana-Champaign.

Kristin Schmidt
Vinicius Segura
Juliana Ferreira (IBM Research)
Daniel Sanders

More from the Same Authors