J. Bonini

When code does not run: reproducibility challenges in materials machine learning benchmarks

Reproducible benchmarks are essential for advancing data-driven materials research by standardizing datasets, prediction tasks, and evaluation protocols. In machine learning benchmarks, reproducibility is often assumed to follow from releasing source code and documented software dependencies. However, dependency specif...