83 citations · 86 across the 7 of their papers we have counts for
7 papers
Position: AI Agents in Scientific Teams Should Be Studied as Human-Agent Systems
Patrick Emami, Sameera Horawalavithana, Truc Nguyen +11
Large language model-based agents are increasingly deployed as collaborators in scientific discovery yet most current work focuses on the autonomous capabilities of "AI Scientists"…
SCICONVBENCH: Benchmarking LLMs on Multi-Turn Clarification for Task Formulation in Computational Science
Nithin Somasekharan, Youssef Hassan, Shiyao Lin +5
Large Language Models (LLMs) are increasingly deployed as scientific AI as- sistants, and a growing body of benchmarks evaluates their capabilities across knowledge retrieval, reas…
AutoLabs: Cognitive Multi-Agent Systems with Self-Correction for Autonomous Chemical Experimentation
Gihan Panapitiya, Emily Saldanha, Heather Job +1
The automation of chemical research through self-driving laboratories (SDLs) promises to accelerate scientific discovery, yet the reliability and granular performance of the underl…
FragNet: A Graph Neural Network for Molecular Property Prediction with Four Levels of Interpretability
Gihan Panapitiya, Peiyuan Gao, C Mark Maupin +1
Molecular property prediction is essential in a variety of contemporary scientific fields, such as drug development and designing energy storage materials. Although there are many…
Outlier-Based Domain of Applicability Identification for Materials Property Prediction Models
Gihan Panapitiya, Emily Saldanha
Machine learning models have been widely applied for material property prediction. However, practical application of these models can be hindered by a lack of information about how…
Dynamic Molecular Graph-based Implementation for Biophysical Properties Prediction
Carter Knutson, Gihan Panapitiya, Rohith Varikoti +1
Neural Networks (GNNs) have revolutionized the molecular discovery to understand patterns and identify unknown features that can aid in predicting biophysical properties and protei…
Predicting Aqueous Solubility of Organic Molecules Using Deep Learning Models with Varied Molecular Representations
Gihan Panapitiya, Michael Girard, Aaron Hollas +3
Determining the aqueous solubility of molecules is a vital step in many pharmaceutical, environmental, and energy storage applications. Despite efforts made over decades, there are…