Clever Materials: When Models Identify Good Materials for the Wrong Reasons
Authors: Kevin Maik Jablonka
Organizations: Laboratory of Organic and Macromolecular Chemistry (IOMC), Friedrich Schiller University Jena, Humboldtstrasse 10, 07743 Jena, Germany · Helmholtz Institute for Polymers in Energy Applications Jena (HIPOLE Jena), Lessingstrasse 12-14, 07743 Jena, Germany · Center for Energy and Environmental Chemistry Jena (CEEC Jena), Friedrich Schiller University Jena, Philosophenweg 7a, 07743 Jena, Germany · Jena Center for Soft Matter (JCSM), Friedrich Schiller University Jena, Philosophenweg 7, 07743 Jena, Germany
Machine learning can accelerate materials discovery. Models perform impressively on many benchmarks. However, strong benchmark performance does not imply that a model learned chemistry. I test a concrete alternative hypothesis: that property prediction can be driven by bibliographic confounding. Across five tasks spanning MOFs (thermal and solvent stability), perovskite solar cells (efficiency), batteries (capacity), and TADF emitters (emission wavelength), models trained on standard chemical descriptors predict author, journal, and publication year well above chance. When these predicted metadata ("bibliographic fingerprints") are used as the sole input to a second model, performance is sometimes competitive with conventional descriptor-based predictors. These results show that many datasets do not rule out non-chemical explanations of success. Progress requires routine falsification tests (e.g., group/time splits and metadata ablations), datasets designed to resist spurious correlations, and explicit separation of two goals: predictive utility versus evidence of chemical understanding.
Figures & tables
Figure 1
Figure 2: For the classification task of membership in the top-10% of thermally stable MOFs, one can be fooled (by Clever Hans effects). a The model predicts the authors of the associated paper with high accuracy, much better than a random baseline. b This also holds for predicting in which journal the entry was published or the year in which the paper was published c . Using the predicted bibliographic information, a model can predict with high accuracy if the MOF belongs to the top-10% thermally stable ones. However, the effect is smaller — or not even there — if analyzed under a different metric or for a regression setting (see Section A.2 ). The dummy baseline for classification is a stratified random sampling (using the empirical probabilities from the training dataset) and the mean prediction for the regression case.
Figure 3: Measuring performance using accuracy, “Clever Hans models” can achieve a surprisingly good performance in predicting solvent removal stability of MOFs. a MOF descriptors can be used to predict author information meaningfully better than a random baseline. b The journal in which a given MOF stability result has been published can also be predicted with non-trivial accuracy. c The publication year can be predicted with a mean average error of around two years. d A model trained on predicted bibliographic information (actual bibliometric and predicted one) can achieve non-trivial accuracy in correctly predicting the solvent stability of MOFs. Figure 7 shows the performance measured with other metrics.
Figure 4: Clever Hans models can achieve a high accuracy in predicting if a perovskite material is within the top-10% most efficient absorbers. a Composition description can predict which of the ten most prolific authors has been reporting a composition with a micro F1 score of 0.318. b The journal in which a device has been published can be predicted with a similar performance. c The publication year can be predicted meaningfully better than with a mean baseline. d A model trained on this predicted bibliometric information can achieve an accuracy in classifying if an aborber belongs to the top-10% most efficient ones with an accuracy indistinguishable from the model directly trained on composition features.
Figure 5: “Clever Hans models” perform worse than “conventional” models but better than simple baselines in predicting the maximum emission wavelengths of TADF molecules. a Using molecular descriptors, the model can predict the the authors of the paper describing a material with an accuracy of 55.434% among the 1000 most profilic authors. b The model achieves an even higher performance in predicting in which of the ten most common journals a given entry has been published. c The publication year, too, can be predicted with a high performance just based on composition descriptors. d Using predicted bibliometric information, one can achieve a mean absolute error between the “conventional” and naïve baseline models. The performance using actual bibliometric information is not much different from using the correct one.
Figure 6: “Clever Hans models” do not achieve a good performance in predicting battery capacity. a Composition descriptors can be used to determine which of the 500 most prolific authors reported this material with a mediocre F1 score. b The journal in which a material has been reported can be predicted with a similar performance. c The publication year can be predicted better than with the mean baseline. d “Clever Hans” models are not distinguishable from mean predictions ( “Dummy” ) for the prediction of the capacity of battery materials.
Appendix figures & tables11 assets
Supplementary material from the paper’s appendix.
Appendix
Figure 7: Performance of MOF solvent removal stability classification measured with different metrics. In all metrics, “Clever Hans models” outperform simple baselines. In some metrics, such as precision, “Clever Hans models” come close in performance to models directly trained on MOF descriptors ( “Conventional” ).
Figure 8: Parameter sweep for MOF solvent stability. Performance of proxy models as a function of the type and number of predicted bibliometric features.
Figure 9: MOF thermal stability prediction performance across different metrics. The measured difference between conventional and proxy models depends on the evaluation metric chosen.
Figure 10: Parameter sweep analysis for MOF thermal stability. Performance of proxy models as a function of author count thresholds and inclusion of temporal/journal information.
Figure 11: Performance in predicting bibliometric information and in predicting photoconversion efficiencies.
Figure 12: Impact of the number of bibliometric features on the performance of “Clever Hans” models in predicting PCE.
Figure 13: Perovskite PCE prediction performance across metrics. Comparison of conventional and proxy models using different regression evaluation metrics.
Figure 14: Perovskite top-10% classification performance across metrics. Different evaluation metrics reveal varying degrees of Clever Hans effects.
Figure 15: Parameter sweep for perovskite top-10% classification. Performance sensitivity to author count thresholds and temporal/journal features.
Figure 16: TADF wavelength prediction performance across metrics. Moderate proxy learning effects between conventional and baseline performance.
Figure 17: Parameter sweep for TADF wavelength prediction. Performance variation with different bibliometric feature configurations.
School of Computer Science, University of Sheffield, Sheffield, UK · Centre for Machine Intelligence, University of Sheffield, Sheffield, UK · School of Chemical, Materials and Biological Engineering, University of Sheffield, Sheffield, UK +3