793 citations · 1.7k across the 34 of their papers we have counts for
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Beyond Independent Error Assumptions in Large GNN Atomistic Models
Janghoon Ock, Tian Tian, John Kitchin +1
The practical applications of determining the relative difference in adsorption energies are extensive, such as identifying optimal catalysts, calculating reaction energies, and de…
FINETUNA: Fine-tuning Accelerated Molecular Simulations
Joseph Musielewicz, Xiaoxiao Wang, Tian Tian +1
Machine learning approaches have the potential to approximate Density Functional Theory (DFT) for atomistic simulations in a computationally efficient manner, which could dramatica…
Enabling robust offline active learning for machine learning potentials using simple physics-based priors
Muhammed Shuaibi, Saurabh Sivakumar, Rui Qi Chen +1
Machine learning surrogate models for quantum mechanical simulations has enabled the field to efficiently and accurately study material and molecular systems. Developed models typi…