Pretraining Strategy for Neural Potentials
Zehua Zhang Zijie Li Amir Barati Farimani
We propose a mask pretraining method for Graph Neural Networks (GNNs) to
improve their performance on fitting potential energy surfaces, particularly in
water systems. GNNs are pretrained by recovering spatial information related to
masked-out atoms from molecules, then transferred and finetuned on atomic
forcefields. Through such pretraining, GNNs learn meaningful prior about
structural and underlying physical information of molecule systems that are
useful for downstream tasks. From comprehensive experiments and ablation
studies, we show that the proposed method improves the accuracy and convergence
speed compared to GNNs trained from scratch or using other pretraining
techniques such as denoising. On the other hand, our pretraining method is
suitable for both energy-centric and force-centric GNNs. This approach
showcases its potential to enhance the performance and data efficiency of GNNs
in fitting molecular force fields.