1 citations · 2 across the 6 of their papers we have counts for
8 papers
On Making -Invariant Networks Optimal
Tomas Karella, Emily Shinkle, Alice Elisabeth Anastasia Allen +3
Respecting a-priori known symmetries of underlying data is a key principle in designing efficient neural architectures. Convolutional neural networks are a canonical example, encod…
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…
Knowledge Distillation of Noisy Force Labels for Improved Coarse-Grained Force Fields
Feranmi V. Olowookere, Sakib Matin, Aleksandra Pachalieva +2
Molecular dynamics simulations are an integral tool for studying the atomistic behavior of materials under diverse conditions. However, they can be computationally demanding in wal…
Optimal Invariant Bases for Atomistic Machine Learning
Alice E. A. Allen, Emily Shinkle, Roxana Bujack +1
The representation of atomic configurations for machine learning models has led to the development of numerous descriptors, often to describe the local environment of atoms. Howeve…
Flexible Moment-Invariant Bases from Irreducible Tensors
Roxana Bujack, Emily Shinkle, Alice Allen +2
Moment invariants are a powerful tool for the generation of rotation-invariant descriptors needed for many applications in pattern detection, classification, and machine learning.…
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…