2 citations · 5 across the 3 of their papers we have counts for
3 papers
cs.LG2026★ 2 cited
A recipe for scalable attention-based MLIPs: unlocking long-range accuracy with all-to-all node attention
Eric Qu, Brandon M. Wood, Aditi S. Krishnapriyan +1
Machine-learning interatomic potentials (MLIPs) have advanced rapidly, with many top models relying on strong physics-based inductive biases. However, as models scale to larger sys…
cs.LG2025★ 2 cited
Transformers Discover Molecular Structure Without Graph Priors
Tobias Kreiman, Yutong Bai, Fadi Atieh +3
Graph Neural Networks (GNNs) are the dominant architecture for molecular machine learning, particularly for molecular property prediction and machine learning interatomic potential…
cs.LG2024★ 1 cited
The Importance of Being Scalable: Improving the Speed and Accuracy of Neural Network Interatomic Potentials Across Chemical Domains
Eric Qu, Aditi S. Krishnapriyan
Scaling has been critical in improving model performance and generalization in machine learning. It involves how a model's performance changes with increases in model size or input…