8 papers
Interpretable Meta-Learning for Multi-Objective Chemical Search
Antonio Varagnolo, Yulia Pimonova, Michael G. Taylor +2
Navigating the vast space of synthetically accessible molecules demands surrogate models that are interpretable and capable of handling multiple competing objectives at the same ti…
Projected Hessian Learning: Fast Curvature Supervision for Accurate Machine-Learning Interatomic Potentials
Austin Rodriguez, Justin S. Smith, Sakib Matin +3
The Hessian matrix (second derivatives) encodes far richer local curvature of the potential energy surface than energies and forces alone. However, training machine-learning intera…
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…
Meta-Learning Linear Models for Molecular Property Prediction
Yulia Pimonova, Michael G. Taylor, Alice Allen +2
Chemists in search of structure-property relationships face great challenges due to limited high quality, concordant datasets. Machine learning (ML) has significantly advanced pred…
Effects of Structural Allocation of Geometric Task Diversity in Linear Meta-Learning Models
Saptati Datta, Nicolas W. Hengartner, Yulia Pimonova +2
Meta-learning aims to leverage information across related tasks to improve prediction on unlabeled data for new tasks when only a small number of labeled observations are available…
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.…
GPU-Accelerated Charge-Equilibration for Shadow Molecular Dynamics in Python
Mehmet Cagri Kaymak, Nicholas Lubbers, Christian F. A. Negre +2
With recent advancements in machine learning for interatomic potentials, Python has become the go-to programming language for exploring new ideas. While machine-learning potentials…