John R. Kitchin

According to our database, John R. Kitchin authored at least 17 papers between 2017 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2025

UMA: A Family of Universal Models for Atoms.

Brandon M. Wood, Misko Dzamba, Xiang Fu, Meng Gao, Muhammed Shuaibi, Luis Barroso-Luque, Kareem Abdelmaqsoud, Vahe Gharakhanyan, John R. Kitchin, Daniel S. Levine, Kyle Michel, Anuroop Sriram, Taco Cohen, Abhishek Das, Ammar Rizvi, Sushree Jagriti Sahoo, Zachary W. Ulissi, C. Lawrence Zitnick

CoRR, June, 2025

Beyond Force Metrics: Pre-Training MLFFs for Stable MD Simulations.

Shagun Maheshwari, Janghoon Ock, Adeesh Kolluru, Amir Barati Farimani, John R. Kitchin

CoRR, June, 2025

The Evolving Role of Programming and LLMs in the Development of Self-Driving Laboratories.

John R. Kitchin

CoRR, April, 2025

Uncertainty Quantification in Graph Neural Networks with Shallow Ensembles.

Tirtha Vinchurkar, Kareem Abdelmaqsoud, John R. Kitchin

CoRR, April, 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, April, 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

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, Carl D. Laird, Matthew J. McNenly, Sneha A. Akhade

Comput. Chem. Eng., 2025

2024

Circumventing data imbalance in magnetic ground state data for magnetic moment predictions.

Rohan Yuri Sanspeur, John R. Kitchin

Mach. Learn. Sci. Technol., March, 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., February, 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

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

AdsorbDiff: Adsorbate Placement via Conditional Denoising Diffusion.

Adeesh Kolluru, John R. Kitchin

Proceedings of the Forty-first International Conference on Machine Learning, 2024

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

Proceedings of the Twelfth International Conference on Learning Representations, 2024

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., December, 2023

<i>WhereWulff</i>: 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., April, 2023

2019

pybliometrics: Scriptable bibliometrics using a Python interface to Scopus.

Michael E. Rose, John R. Kitchin

SoftwareX, 2019

2017

Automating data sharing through authoring tools.

John R. Kitchin, Ana E. Van Gulick, Lisa Zilinski

Int. J. Digit. Libr., 2017