Benchmarking Active Learning Strategies for Materials Optimization and Discovery. (arXiv:2204.05838v1 [cond-mat.mtrl-sci])

Autonomous physical science is revolutionizing materials science. In these
systems, machine learning controls experiment design, execution, and analysis
in a closed loop. Active learning, the machine learning field of optimal
experiment design, selects each subsequent experiment to maximize knowledge
toward the user goal. Autonomous system performance can be further improved
with implementation of scientific machine learning, also known as inductive
bias-engineered artificial intelligence, which folds prior knowledge of
physical laws (e.g., Gibbs phase rule) into the algorithm. As the number, …

active learning arxiv benchmarking discovery learning materials optimization strategies