  Most machine learning (ML) models in Materials Science are developed by
global geometric features, often falling short in describing localized
characteristics, like molecular adsorption on materials. In this study, we
introduce a local environment framework that extracts local features from
crystal structures to portray the environment surrounding specific adsorption
sites. Upon OC20 database (~20,000 3D entries), we apply our local environment
framework on several ML models, such as random forest, convolutional neural
network, and graph neural network. It is found that our framework achieves
remarkable prediction accuracy in predicting molecular adsorption energy,
significantly outperforming other examined global-environment-based models.
Moreover, the employment of this framework reduces data requirements and
augments computational speed, specifically for deep learning algorithms.
Finally, we directly apply our Local Environment ResNet (LERN) on a small
2DMatPedia database (~2,000 2D entries), which also achieves highly accurate
prediction, demonstrating the model transferability and remarkable data
efficiency. Overall, the prediction accuracy, data-utilization efficiency, and
transferability of our local-environment-based ML framework hold a promising
high applicability across a broad molecular adsorption field, such as catalysis
and sensor technologies.
