Extending OpenKIM with an Uncertainty Quantification Toolkit for Molecular Modeling. - Yonatan Kurniawan; Cody L. Petrie; Mark K. Transtrum; Ellad B. Tadmor; Ryan S. Elliott; Daniel S. Karls; Mingjian Wen (e-Science, 2022)

Injecting Domain Knowledge from Empirical Interatomic Potentials to Neural Networks for Predicting Material Properties. - Zeren Shui; Daniel S. Karls; Mingjian Wen; Ilia A. Nikiforov; Ellad B. Tadmor; George Karypis (NeurIPS, 2022)

KLIFF: A framework to develop physics-based and machine learning interatomic potentials. - Mingjian Wen; Yaser Afshar; Ryan S. Elliott; Ellad B. Tadmor (Comput. Phys. Commun., 2022)

Chemical reaction networks and opportunities for machine learning. - Mingjian Wen; Evan Walter Clark Spotte-Smith; Samuel M. Blau; Matthew J. McDermott; Aditi S. Krishnapriyan; Kristin A. Persson (Nat. Comput. Sci., 2023)

Fine-Tuning Language Models on Multiple Datasets for Citation Intention Classification. - Zeren Shui; Petros Karypis; Daniel S. Karls; Mingjian Wen; Saurav Manchanda; Ellad B. Tadmor; George Karypis (EMNLP (Findings), 2024)

An information-matching approach to optimal experimental design and active learning. - Yonatan Kurniawan; Tracianne B. Neilsen; Benjamin L. Francis; Alex M. Stankovic; Mingjian Wen; Ilia A. Nikiforov; Ellad B. Tadmor; Vasily V. Bulatov; Vincenzo Lordi; Mark K. Transtrum (CoRR, 2024)

Jobflow: Computational Workflows Made Simple. - Andrew S. Rosen; Max C. Gallant; Janine George; Janosh Riebesell; Hrushikesh Sahasrabuddhe; Jimmy-Xuan Shen; Mingjian Wen; Matthew L. Evans; Guido Petretto; David Waroquiers; Gian-Marco Rignanese; Kristin A. Persson; et al. (J. Open Source Softw., 2024)

HEPOM: Using Graph Neural Networks for the Accelerated Predictions of Hydrolysis Free Energies in Different pH Conditions. - Rishabh D. Guha; Santiago Vargas; Evan Walter Clark Spotte-Smith; Alexander Rizzolo Epstein; Maxwell C. Venetos; Ryan Kingsbury; Mingjian Wen; Samuel M. Blau; Kristin A. Persson (J. Chem. Inf. Model., 2025)