New adaptive ML framework slashes quantum chemistry data costs

Researchers have developed a new on-the-fly multifidelity machine learning framework designed to optimize data generation for quantum chemistry calculations. This adaptive approach dynamically selects training samples at different fidelity levels, prioritizing lower-cost data until model accuracy is maximized before moving to more expensive, high-fidelity data. Benchmarked against existing methods, the adaptive framework significantly reduces data generation costs, achieving up to a 30-fold improvement over single-fidelity methods and a 5-fold improvement over standard multifidelity approaches, paving the way for more efficient and cost-aware machine learning in the field. AI

IMPACT Reduces data generation costs for quantum chemistry ML, enabling more efficient research.

RANK_REASON The cluster contains a research paper detailing a novel algorithm for machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

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• Machine Learning
• Multifidelity Machine Learning
• Quantum Chemistry

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• infra

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