1 citations · 2 across the 4 of their papers we have counts for
5 papers
An open benchmark for machine learning-based polymer property prediction
Robert W. Learsch, Nicholas Liesen, Daniel S. Levine +2
Polymer property prediction lacks open, standardized benchmarks that enable rigorous comparison of machine-learning methods, with existing resources covering only a narrow fraction…
The Open Polymers 2026 (OPoly26) Dataset and Evaluations
Daniel S. Levine, Nicholas Liesen, Lauren Chua +12
Polymers-macromolecular systems composed of repeating chemical units-constitute the molecular foundation of living organisms, while their synthetic counterparts drive transformativ…
BOOM: Benchmarking Out-Of-distribution Molecular Property Predictions of Machine Learning Models
Evan R. Antoniuk, Shehtab Zaman, Tal Ben-Nun +10
Data-driven molecular discovery leverages artificial intelligence/machine learning (AI/ML) and generative modeling to filter and design novel molecules. Discovering novel molecules…
EAIRA: Establishing a Methodology for Evaluating AI Models as Scientific Research Assistants
Franck Cappello, Sandeep Madireddy, Robert Underwood +23
Recent advancements have positioned AI, and particularly Large Language Models (LLMs), as transformative tools for scientific research, capable of addressing complex tasks that req…
Active Learning Enables Generation of Molecules that Advance the Known Pareto Front
Evan R. Antoniuk, Peggy Li, Nathan Keilbart +3
Although generative models hold promise for discovering molecules with optimized desired properties, they often fail to suggest synthesizable molecules that improve upon the proper…