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We introduce a machine-learning (ML) framework for high-throughputbenchmarking of diverse representations of chemical systems against datasets ofmaterials and molecules. The guiding principle underlying the benchmarkingapproach is to evaluate raw descriptor performance by limiting model complexityto simple regression schemes while enforcing best ML practices, allowing forunbiased hyperparameter optimization, and assessing learning progress throughlearning curves along series of synchronized train-test splits. The resultingmodels are intended as baselines that can inform future method development,next to indicating how easily a given dataset can be learnt. Through acomparative analysis of the training outcome across a diverse set ofphysicochemical, topological and geometric representations, we glean insightinto the relative merits of these representations as well as theirinterrelatedness.

BenchML: an extensible pipelining framework for benchmarking representations of materials and molecules at scale