Interpretable Ensemble Learning for Materials Property Prediction with
  Classical Interatomic Potentials: Carbon as an Example
Xinyu Jiang Haofan Sun Kamal Choudhary Houlong Zhuang Qiong Nian
Machine learning (ML) is widely used to explore crystal materials and predict
their properties. However, the training is time-consuming for deep-learning
models, and the regression process is a black box that is hard to interpret.
Also, the preprocess to transfer a crystal structure into the input of ML,
called descriptor, needs to be designed carefully. To efficiently predict
important properties of materials, we propose an approach based on ensemble
learning consisting of regression trees to predict formation energy and elastic
constants based on small-size datasets of carbon allotropes as an example.
Without using any descriptor, the inputs are the properties calculated by
molecular dynamics with 9 different classical interatomic potentials. Overall,
the results from ensemble learning are more accurate than those from classical
interatomic potentials, and ensemble learning can capture the relatively
accurate properties from the 9 classical potentials as criteria for predicting
the final properties.