For factorization machine based active learning, larger design spaces require substantially larger initial datasets to converge quickly, with recommended sizes spanning 25 points for a 40-bit space to 3,000 for a 160-bit space.
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Optimization of Functional Materials Design with Optimal Initial Data in Surrogate-Based Active Learning
For factorization machine based active learning, larger design spaces require substantially larger initial datasets to converge quickly, with recommended sizes spanning 25 points for a 40-bit space to 3,000 for a 160-bit space.