Yulia Pimonova

Sakib Matin, Emily Shinkle, Yulia Pimonova et al.

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. Datasets generated with high-fidelity QC methods are typically restricted to small molecules and may be missing energy gradients, which make it difficult to train accurate MLIPs. We present an ensemble knowledge distillation (EKD) method to improve MLIP accuracy when trained to energy-only datasets. First, multiple teacher models are trained to QC energies and then generate atomic forces for all configurations in the dataset. Next, the student MLIP is trained to both QC energies and to ensemble-averaged forces generated by the teacher models. We apply this workflow on the ANI-1ccx dataset where the configuration energies computed at the coupled cluster level of theory. The resulting student MLIPs achieve new state-of-the-art accuracy on the COMP6 benchmark and show improved stability for molecular dynamics simulations.

Saptati Datta, Nicolas W. Hengartner, Yulia Pimonova et al.

Meta-learning has emerged as a powerful paradigm for leveraging information across related tasks to improve predictive performance on new tasks. In this paper, we propose a statistical framework for analyzing meta-learning through the lens of predictor subspace characterization and quantification of task diversity. Specifically, we model the shared structure across tasks using a latent subspace and introduce a measure of diversity that captures heterogeneity across task-specific predictors. We provide both simulation-based and theoretical evidence indicating that achieving the desired prediction accuracy in meta-learning depends on the proportion of predictor variance aligned with the shared subspace, as well as on the accuracy of subspace estimation.

Yulia Pimonova, Michael G. Taylor, Alice Allen et al.

Chemists in search of structure-property relationships face great challenges due to limited high quality, concordant datasets. Machine learning (ML) has significantly advanced predictive capabilities in chemical sciences, but these modern data-driven approaches have increased the demand for data. In response to the growing demand for explainable AI (XAI) and to bridge the gap between predictive accuracy and human comprehensibility, we introduce LAMeL - a Linear Algorithm for Meta-Learning that preserves interpretability while improving the prediction accuracy across multiple properties. While most approaches treat each chemical prediction task in isolation, LAMeL leverages a meta-learning framework to identify shared model parameters across related tasks, even if those tasks do not share data, allowing it to learn a common functional manifold that serves as a more informed starting point for new unseen tasks. Our method delivers performance improvements ranging from 1.1- to 25-fold over standard ridge regression, depending on the domain of the dataset. While the degree of performance enhancement varies across tasks, LAMeL consistently outperforms or matches traditional linear methods, making it a reliable tool for chemical property prediction where both accuracy and interpretability are critical.