A Hybrid Human-computer Approach to the Extraction of Scientific Facts from the Literature. - Roselyne Tchoua; Kyle Chard; Debra Audus; Jian Qin; Juan de Pablo; Ian T. Foster (ICCS, 2016)

Towards a Hybrid Human-Computer Scientific Information Extraction Pipeline. - Roselyne B. Tchoua; Kyle Chard; Debra J. Audus; Logan T. Ward; Joshua Lequieu; Juan J. de Pablo; Ian T. Foster (eScience, 2017)

Active Learning Yields Better Training Data for Scientific Named Entity Recognition. - Roselyne Tchoua; Aswathy Ajith; Zhi Hong; Logan T. Ward; Kyle Chard; Debra Audus; Shrayesh Patel; Juan de Pablo; Ian T. Foster (eScience, 2019)

Creating Training Data for Scientific Named Entity Recognition with Minimal Human Effort. - Roselyne B. Tchoua; Aswathy Ajith; Zhi Hong; Logan T. Ward; Kyle Chard; Alexander Belikov 003; Debra J. Audus; Shrayesh Patel; Juan J. de Pablo; Ian T. Foster (ICCS (1), 2019)

A machine learning workflow for molecular analysis: application to melting points. - Ganesh Sivaraman; Nicholas E. Jackson; Benjamín Sánchez-Lengeling; Álvaro Vázquez-Mayagoitia; Alán Aspuru-Guzik; Venkatram Vishwanath; Juan J. de Pablo (Mach. Learn. Sci. Technol., 2020)

OpenAWSEM with Open3SPN2: A fast, flexible, and accessible framework for large-scale coarse-grained biomolecular simulations. - Wei Lu; Carlos Bueno; Nicholas P. Schafer; Joshua Moller; Shikai Jin; Xun Chen; Mingchen Chen; Xinyu Gu; Aram Davtyan; Juan J. de Pablo; Peter G. Wolynes (PLoS Comput. Biol., 2021)

Prediction of Diblock Copolymer Morphology via Machine Learning. - Hyun Park; Boyuan Yu; Juhae Park; Ge Sun; Emad Tajkhorshid; Juan J. de Pablo; Ludwig Schneider (CoRR, 2023)

Chromatin structures from integrated AI and polymer physics model. - Eric R. Schultz; Soren Kyhl; Rebecca Willett; Juan J. de Pablo (PLoS Comput. Biol., 2025)