X. Song, L. Chai, J. Zhang, "Graph Signal Processing Approach to QSAR/QSPR Model Learning of Compounds", IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021.
M. Shuaibi, A. Kolluru, A. Das, A. Grover, A. Sriram, Z. Ulissi, C. Zitnick, “Rotation Invariant Graph Neural Networks using Spin Convolutions”, Preprint, 2021.
K. Tran, W. Neiswnager, J. Yoon, Q. Zhang, E. Xing, Z. Ulissi, “Methods for Comparing Uncertainty Quantifications for Material Property Predictions", Machine Learning: Science and Technology, Volume 1, Number 2, 2021.
J. Gasteiger, M. Shuaibi, A. Sriram, S. Gunnemann, Z. Ulissi, C. Zitnick, A. Das, “GemNet-OC: Developing Graph Neural Networks for Large and Diverse Molecular Simulation Datasets”, Preprint, 2022.
A. Kolluru, N. Shoghi, M. Shuaibi, S. Goyal, A. Das, C. Zitnick, Z. Ulissi, “Transfer Learning using Attentions Across Atomic Systems with Graph Neural Networks (TAAG)”, The Journal of Chemical Physics, 2022.
E. Sunshine, M. Shuaibi, Z. Ulissi, J.Kitchin, “Chemical Properties from Graph Neural Network-Predicted Electron Densities”, The Journal of Physical Chemistry C, 2023.
J. Lan, A. Palizhati, M. Shuaibi, B. Wood, B. Wander, A. Das, M. Uyttendaele, C. Zitnick, Z. Ulissi, “AdsorbML: A Leap in Efficiency for Adsorption Energy Calculations using Generalizable Machine Learning Potentials”, Computational Materials, 2023.
X. Li, R. Chiong, Z. Hu, A. Page, “A Graph Neural Network Model with Local Environment Pooling for Predicting Adsorption Energies”, Computational and Theoretical Chemistry, 2023.
N. Shoghi, A. Kolluru, J Kitchin, Z. Ulissi, C. Zitnick, B. Wood, “From Molecules to Materials: Pre-Training Large Generalizable Models for Atomic Property Prediction”, Preprint, 2024.
J. Ock, T. Tian, J. Kitchin, Z. Ulissi, “Beyond Independent Error Assumptions in large GNN Atomistic Models”, The Journal of Chemical Physics, 2023.
L. Barroso-Luque, M. Shuaibi, X. Fu, B. Wood, M. Dzamba, M. Gao, A. Rizvi, C. Zitnick, Z. Ulissi, “Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models”, Preprint, 2024.
B. Wander, M. Shuaibi, J. Kitchin, Z. Ulissi, “CatTSunami: Accelerating Transition State Energy Calculations with Pretrained Graph Neural Networks”, ACS Catalyst, March 2025.
A. Kensert, G. Desmet, D. Cabooter, "MolGraph: a Python Package for The Implementation of Molecular Graphs and Graph Neural Networks with Tensorflow and Keras", Journal of Computer-Aided Molecular Design, 2025.