872 citations · 2.9k across the 35 of their papers we have counts for
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Bridging Molecular Scales with Implicit Score Matching for Bottom-Up Coarse Graining
Patrick G. Sahrmann, Nicholas Lubbers, Benjamin T. Nebgen +4
Molecular dynamics simulations provide a computational microscope for atomic scale processes but remain restricted to relatively small spatial and temporal scales. Coarse-grained m…
Enhancing Molecular Dipole Moment Prediction with Multitask Machine Learning
William Colglazier, Nicholas Lubbers, Sergei Tretiak +2
We present a multitask machine learning strategy for improving the prediction of molecular dipole moments by simultaneously training on quantum dipole magnitudes and inexpensive Mu…
Reactive Chemistry at Unrestricted Coupled Cluster Level: High-throughput Calculations for Training Machine Learning Potentials
Alice E. A. Allen, Rui Li, Sakib Matin +8
Accurately modeling chemical reactions at the atomistic level requires high-level electronic structure theory due to the presence of unpaired electrons and the need to properly des…
Ensemble Knowledge Distillation for Machine Learning Interatomic Potentials
Sakib Matin, Emily Shinkle, Yulia Pimonova +5
The quality of machine learning interatomic potentials (MLIPs) strongly depends on the quantity of training data as well as the quantum chemistry (QC) level of theory used. Dataset…
Teacher-student training improves accuracy and efficiency of machine learning interatomic potentials
Sakib Matin, Alice E. A. Allen, Emily Shinkle +9
Machine learning interatomic potentials (MLIPs) are revolutionizing the field of molecular dynamics (MD) simulations. Recent MLIPs have tended towards more complex architectures tr…
Thermodynamic Transferability in Coarse-Grained Force Fields using Graph Neural Networks
Emily Shinkle, Aleksandra Pachalieva, Riti Bahl +4
Coarse-graining is a molecular modeling technique in which an atomistic system is represented in a simplified fashion that retains the most significant system features that contrib…