22 citations · 59 across the 16 of their papers we have counts for
7 papers · 1 filter
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…