4 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…
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…
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…
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…