Automating data sharing through authoring tools. - John R. Kitchin; Ana E. Van Gulick; Lisa Zilinski (Int. J. Digit. Libr., 2017)

pybliometrics: Scriptable bibliometrics using a Python interface to Scopus. - Michael E. Rose; John R. Kitchin (SoftwareX, 2019)

WhereWulff: A Semiautonomous Workflow for Systematic Catalyst Surface Reactivity under Reaction Conditions. - Rohan Yuri Sanspeur; Javier Heras-Domingo; John R. Kitchin; Zachary W. Ulissi (J. Chem. Inf. Model., 2023)

Applying Large Graph Neural Networks to Predict Transition Metal Complex Energies Using the tmQM_wB97MV Data Set. - Aaron G. Garrison; Javier Heras-Domingo; John R. Kitchin; Gabriel dos Passos Gomes; Zachary W. Ulissi; Samuel M. Blau (J. Chem. Inf. Model., 2023)

From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction. - Nima Shoghi; Adeesh Kolluru; John R. Kitchin; Zachary W. Ulissi; C. Lawrence Zitnick; Brandon M. Wood (ICLR, 2024)

AdsorbDiff: Adsorbate Placement via Conditional Denoising Diffusion. - Adeesh Kolluru; John R. Kitchin (ICML, 2024)

Rotationally Invariant Latent Distances for Uncertainty Estimation of Relaxed Energy Predictions by Graph Neural Network Potentials. - Joseph Musielewicz; Janice Lan; Matt Uyttendaele; John R. Kitchin (CoRR, 2024)

Generalization of graph-based active learning relaxation strategies across materials. - Xiaoxiao Wang; Joseph Musielewicz; Richard Tran; Sudheesh Kumar Ethirajan; Xiaoyan Fu; Hilda Mera; John R. Kitchin; Rachel C. Kurchin; Zachary W. Ulissi (Mach. Learn. Sci. Technol., 2024)

Opyrability: A Python package for process operability analysis. - Victor Alves; San Dinh; John R. Kitchin; Vitor Gazzaneo; Juan C. Carrasco; Fernando V. Lima (J. Open Source Softw., 2024)

Circumventing data imbalance in magnetic ground state data for magnetic moment predictions. - Rohan Yuri Sanspeur; John R. Kitchin (Mach. Learn. Sci. Technol., 2024)

Multiscale optimization of formic acid dehydrogenation process via linear model decision tree surrogates. - Ethan M. Sunshine; Giovanna Bucci; Tanusree Chatterjee; Shyam Deo; Victoria M. Ehlinger; Wenqin Li; Thomas Moore; Corey Myers; Wenyu Sun; Bo-Xun Wang; Mengyao Yuan; John R. Kitchin; et al. (Comput. Chem. Eng., 2025)

Hyperplane decision trees as piecewise linear surrogate models for chemical process design. - Ethan M. Sunshine; Carolina Colombo Tedesco; Sneha A. Akhade; Matthew J. McNenly; John R. Kitchin; Carl D. Laird (Comput. Chem. Eng., 2025)

A Simultaneous Approach for Training Neural Differential-Algebraic Systems of Equations. - Laurens R. Lueg; Victor Alves; Daniel Schicksnus; John R. Kitchin; Carl D. Laird; Lorenz T. Biegler (CoRR, 2025)

Uncertainty Quantification in Graph Neural Networks with Shallow Ensembles. - Tirtha Vinchurkar; Kareem Abdelmaqsoud; John R. Kitchin (CoRR, 2025)

The Evolving Role of Programming and LLMs in the Development of Self-Driving Laboratories. - John R. Kitchin (CoRR, 2025)

Beyond Force Metrics: Pre-Training MLFFs for Stable MD Simulations. - Shagun Maheshwari; Janghoon Ock; Adeesh Kolluru; Amir Barati Farimani; John R. Kitchin (CoRR, 2025)

UMA: A Family of Universal Models for Atoms. - Brandon M. Wood; Misko Dzamba; Xiang Fu 005; Meng Gao; Muhammed Shuaibi; Luis Barroso-Luque; Kareem Abdelmaqsoud; Vahe Gharakhanyan; John R. Kitchin; Daniel S. Levine 003; Kyle Michel; Anuroop Sriram; et al. (CoRR, 2025)