4 papers
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…