22 citations · 59 across the 16 of their papers we have counts for
18 papers
Fine-tuning universal machine learning potentials for transition state search in surface catalysis
Raffaele Cheula, Mie Andersen, John R. Kitchin
Determining transition states (TSs) of surface reactions is central to understanding and designing heterogeneous catalysts but remains computationally prohibitive with density func…
Computational Design of Ductile Additively Manufactured Tungsten-Based Refractory Alloys
Kareem Abdelmaqsoud, Daniel Sinclair, Venkata Satya Surya Amaranth Karra +4
Tungsten exhibits exceptional temperature and radiation resistance, making it well-suited for applications in extreme environments such as nuclear fusion reactors. Additive manufac…
Electronic structure and elasticity of the Ta-W solid solution
Kareem Abdelmaqsoud, John R. Kitchin, Michael Widom
The brittleness or ductility of metals has long been attributed to their elastic constants, with high Poisson ratio, or equivalently high Pugh ratio, favoring greater ductility. Gr…
Prospects for Using Artificial Intelligence to Understand Intrinsic Kinetics of Heterogeneous Catalytic Reactions
Andrew J. Medford, Todd N. Whittaker, Bjarne Kreitz +2
Artificial intelligence (AI) is influencing heterogeneous catalysis research by accelerating simulations and materials discovery. A key frontier is integrating AI with multiscale m…
The Open DAC 2025 Dataset for Sorbent Discovery in Direct Air Capture
Anuroop Sriram, Logan M. Brabson, Xiaohan Yu +12
Identifying useful sorbent materials for direct air capture (DAC) from humid air remains a challenge. We present the Open DAC 2025 (ODAC25) dataset, a significant expansion and imp…
UMA: A Family of Universal Models for Atoms
Brandon M. Wood, Misko Dzamba, Xiang Fu +15
The ability to quickly and accurately compute properties from atomic simulations is critical for advancing a large number of applications in chemistry and materials science includi…
Beyond Force Metrics: Pre-Training MLFFs for Stable MD Simulations
Shagun Maheshwari, Zhengxian Tang, Janghoon Ock +3
Machine-learning force fields (MLFFs) have emerged as a promising solution for speeding up ab initio molecular dynamics (MD) simulations, where accurate force predictions are criti…
Uncertainty Quantification in Graph Neural Networks with Shallow Ensembles
Tirtha Vinchurkar, Kareem Abdelmaqsoud, John R. Kitchin
Machine-learned potentials (MLPs) have revolutionized materials discovery by providing accurate and efficient predictions of molecular and material properties. Graph Neural Network…
A Simultaneous Approach for Training Neural Differential-Algebraic Systems of Equations
Laurens R. Lueg, Victor Alves, Daniel Schicksnus +3
Scientific machine learning is an emerging field that broadly describes the combination of scientific computing and machine learning to address challenges in science and engineerin…
The Evolving Role of Programming and LLMs in the Development of Self-Driving Laboratories
John R. Kitchin
Machine learning and automation are transforming scientific research, yet the implementation of self-driving laboratories (SDLs) remains costly and complex, and it remains difficul…
Open Catalyst Experiments 2024 (OCx24): Bridging Experiments and Computational Models
Jehad Abed, Jiheon Kim, Muhammed Shuaibi +17
The search for low-cost, durable, and effective catalysts is essential for green hydrogen production and carbon dioxide upcycling to help in the mitigation of climate change. Disco…
Accessing Numerical Energy Hessians with Graph Neural Network Potentials and Their Application in Heterogeneous Catalysis
Brook Wander, Joseph Musielewicz, Raffaele Cheula +1
Access to the potential energy Hessian enables determination of the Gibbs free energy, and certain approaches to transition state search and optimization. Here, we demonstrate that…
Improved Uncertainty Estimation of Graph Neural Network Potentials Using Engineered Latent Space Distances
Joseph Musielewicz, Janice Lan, Matt Uyttendaele +1
Graph neural networks (GNNs) have been shown to be astonishingly capable models for molecular property prediction, particularly as surrogates for expensive density functional theor…
CatTSunami: Accelerating Transition State Energy Calculations with Pre-trained Graph Neural Networks
Brook Wander, Muhammed Shuaibi, John R. Kitchin +2
Direct access to transition state energies at low computational cost unlocks the possibility of accelerating catalyst discovery. We show that the top performing graph neural networ…
AdsorbDiff: Adsorbate Placement via Conditional Denoising Diffusion
Adeesh Kolluru, John R Kitchin
Determining the optimal configuration of adsorbates on a slab (adslab) is pivotal in the exploration of novel catalysts across diverse applications. Traditionally, the quest for th…
Generalization of Graph-Based Active Learning Relaxation Strategies Across Materials
Xiaoxiao Wang, Joseph Musielewicz, Richard Tran +6
Although density functional theory (DFT) has aided in accelerating the discovery of new materials, such calculations are computationally expensive, especially for high-throughput e…
From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction
Nima Shoghi, Adeesh Kolluru, John R. Kitchin +3
Foundation models have been transformational in machine learning fields such as natural language processing and computer vision. Similar success in atomic property prediction has b…
Chemical Properties from Graph Neural Network-Predicted Electron Densities
Ethan M. Sunshine, Muhammed Shuaibi, Zachary W. Ulissi +1
According to density functional theory, any chemical property can be inferred from the electron density, making it the most informative attribute of an atomic structure. In this wo…