arXiv:2510.08906v2 Announce Type: replace-cross Abstract: Training set sampling methods are used to improve model performance and lower data costs in machine learning problems relevant to chemistry. We introduce Gradient Guided Furthest Point Sampling (GGFPS), a simple extension of Furthest Point Sampling (FPS) that leverages molecular force norms to guide efficient sampling of configurational spaces of molecules. Numerical evidence is presented for a toy system (the Styblinski-Tang function) as well as for molecular dynamics trajectories from the MD17 dataset. Our numerical results indicate s
This paper, published on arXiv, introduces a novel method for more efficient machine learning training set selection, leveraging gradient information to improve model performance and reduce data costs, reflecting ongoing research in optimizing AI models.
Improved training set selection methods can significantly enhance the efficiency and accuracy of machine learning models, particularly in fields like chemistry and materials science, accelerating discovery and reducing computational resource requirements.
The ability to more efficiently sample configurational spaces reduces data labeling and computational overhead, potentially democratizing access to advanced AI applications by lowering their cost and complexity.
- · AI researchers and developers
- · Pharmaceutical and materials science industries
- · Cloud computing providers (through increased efficiency)
- · Academia
More precise and efficient machine learning models for chemical and physical simulations become widely accessible.
Accelerated discovery of new molecules, materials, and drug candidates due to faster and more accurate computational predictions.
Enhanced competition in biotech and materials, potentially leading to breakthroughs in areas currently limited by computational constraints.
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Read at arXiv cs.LG