most citedAutomated, LLM enabled extraction of synthesis details for reticular materials from scientific literature

7 citations · 8 across the 6 of their papers we have counts for

collaborators

5 papers

cs.LG2023

Improving Molecular Properties Prediction Through Latent Space Fusion

Eduardo Soares, Akihiro Kishimoto, Emilio Vital Brazil +3

Pre-trained Language Models have emerged as promising tools for predicting molecular properties, yet their development is in its early stages, necessitating further research to enh…

cs.HC2023

Retrospective End-User Walkthrough: A Method for Assessing How People Combine Multiple AI Models in Decision-Making Systems

Vagner Figueredo de Santana, Larissa Monteiro Da Fonseca Galeno, Emilio Vital Brazil +2

Evaluating human-AI decision-making systems is an emerging challenge as new ways of combining multiple AI models towards a specific goal are proposed every day. As humans interact…

cs.AI2023

Human-AI Co-Creation Approach to Find Forever Chemicals Replacements

Juliana Jansen Ferreira, Vinícius Segura, Joana G. R. Souza +4

Generative models are a powerful tool in AI for material discovery. We are designing a software framework that supports a human-AI co-creation process to accelerate finding replace…

cs.LG20231 cited

Position Paper on Dataset Engineering to Accelerate Science

Emilio Vital Brazil, Eduardo Soares, Lucas Villa Real +4

Data is a critical element in any discovery process. In the last decades, we observed exponential growth in the volume of available data and the technology to manipulate it. Howeve…

cs.AI2023

Knowledge-augmented Risk Assessment (KaRA): a hybrid-intelligence framework for supporting knowledge-intensive risk assessment of prospect candidates

Carlos Raoni Mendes, Emilio Vital Brazil, Vinicius Segura +1

Evaluating the potential of a prospective candidate is a common task in multiple decision-making processes in different industries. We refer to a prospect as something or someone t…