Optimizing Molecular Descriptors for Reliable Adsorption Energy Prediction on Transition Metal Nanoclusters
Lucas B. Pena, Felipe V. Calderan, Priscilla Felício-Sousa, Karla F. Andriani, Marcos G. Quiles, Juarez L. F. Da Silva, Breno R. L. Galvão
TL;DR
This paper explores machine learning methods to predict how molecules stick to metal nanoclusters, aiming to speed up catalyst design.
Contribution
The study introduces a new electronic feature to improve the generalizability of adsorption energy predictions for nanoclusters.
Findings
Both Coulomb matrix and many-body tensor descriptors achieved 0.05 eV mean absolute error on test data.
Adding unpaired electrons as a feature improved generalizability on new examples despite higher errors.
Performance dropped significantly on external data with unprecedented examples.
Abstract
Efficient catalytic processes are crucial for converting pollutant molecules into valuable products. Transition-metal nanoclusters show promise as a result of their tunable properties, but identifying active catalysts requires costly studies of the adsorption energetics. Machine learning offers a faster alternative, predicting adsorption energies when trained on suitable descriptors and relatively large density functional theory (DFT) data sets. This study evaluates the predictive power and transferability of two structural descriptors, the Coulomb matrix and the many-body tensor representation, on a diverse nanocluster-adsorbate data set using the random forest regression algorithm. Both descriptors achieved a mean absolute error of 0.05 eV in test data, but performance dropped significantly on an external generated set with unprecedented examples. Adding a simple electronic feature,…
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Taxonomy
TopicsMachine Learning in Materials Science · Electrocatalysts for Energy Conversion · Nanocluster Synthesis and Applications