Compositional and Structural Fingerprints of Materials Project Time Split Data for use with Generative Materials Benchmarking Metrics

This is a supporting dataset for <em><strong>matbench-genmetrics</strong></em> _[docs] _[repo], a set of generative materials benchmarking metrics. The compositional (Magpie) and structural (CrystalNN) fingerprints* are produced in _{fingerprint_snapshot.py} using <em><strong>mp-time-split</strong></em> data _[docs] _[repo] _[figshare] and are given in <em>comp_fingerprints.csv</em> (132 features) and <em>struct_fingerprints.csv</em> (61 features), respectively. Each has an additional column in the first position, <em>material_id,</em> which contains the _{Materials Project <em>material_id</em>}. So, in total there are 133 and 62 columns, respectively. There are 40476 entries, plus a header row with labels, so 40477 rows in total. The primary purpose of these datasets is to avoid repeating lengthy calculations each time a <em>matbench-genmetrics</em> benchmark is computed; thus, only the generated structures need to be featurized. The total runtime for the compositional and structural fingerprinting using 6 physical cores (12 virtual cores as determined by <em>multiprocessing.cpu_count()</em>) is approximately 50 minutes and 140 min, respectively. The benchmarks can be used with materials generative models such as _{<em>xtal2png</em>}<em>+</em>_{<em>Imagen</em>}. <br> *The use of Magpie and CrystalNN featurizers are based on the coverage metric from CDVAE _[repo] _[paper]. <br> A small set of dummy data for testing purposes is also included (<em>dummy_comp_fingerprints.csv</em> and <em>dummy_struct_fingerprints.csv</em>)