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Assessing the frontier: Active learning, model accuracy, and multi-objective candidate discovery and optimization.

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Active learning accelerates materials discovery, but standard error metrics are misleading. This study introduces Pareto shell error for better candidate proposal evaluation and provides insights into acquisition function design for improved performance.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Machine Learning

Background:

  • Iterative machine learning, or active learning, accelerates the discovery of novel chemicals and materials by proposing candidates.
  • Traditional global error metrics for model quality do not accurately predict the success of active learning in discovery tasks and can be misleading.
  • Effective evaluation of model suitability for proposing candidates is crucial for optimizing discovery pipelines.

Purpose of the Study:

  • To introduce a new metric, Pareto shell error, for assessing model suitability in active learning for candidate proposal.
  • To investigate the relationship between acquisition function fidelity and active learning performance using diverse datasets.
  • To develop novel diagnostic tools and provide insights for improving acquisition function design in active learning.

Main Methods:

  • Introduction of the Pareto shell error metric for evaluating candidate proposal models.
  • Analysis of synthetic datasets, an experimental thermoelectric dataset, and a computational organic molecule dataset.
  • Probing the correlation between acquisition function fidelity and active learning performance.

Main Results:

  • Pareto shell error offers a more reliable measure of model suitability for candidate proposal compared to standard global error metrics.
  • The study reveals specific relationships between acquisition function characteristics and active learning performance across different datasets.
  • Identified key factors influencing the effectiveness of acquisition functions in guiding discovery.

Conclusions:

  • Pareto shell error is a valuable tool for judging model performance in active learning for discovery.
  • The findings provide crucial insights for designing more effective acquisition functions, enhancing the efficiency of materials and chemical discovery.
  • Novel diagnostic approaches and design principles for active learning acquisition functions are proposed.