Active learning potentials for first-principles phase diagrams using replica-exchange nested sampling

The project addresses a high-complexity niche in computational materials science. While it currently lacks community traction (0 stars), it represents a sophisticated combination of replica-exchange nested sampling with active learning for ML potentials. The defensibility lies in the deep domain expertise required to implement and validate these physical simulations, though as a repo, it remains a paper-linked reference implementation.