  With many frameworks based on message passing neural networks proposed to
predict molecular and bulk properties, machine learning methods have
tremendously shifted the paradigms of computational sciences underpinning
physics, material science, chemistry, and biology. While existing machine
learning models have yielded superior performances in many occasions, most of
them model and process molecular systems in terms of homogeneous graph, which
severely limits the expressive power for representing diverse interactions. In
practice, graph data with multiple node and edge types is ubiquitous and more
appropriate for molecular systems. Thus, we propose the heterogeneous
relational message passing network (HermNet), an end-to-end heterogeneous graph
neural networks, to efficiently express multiple interactions in a single model
with {\it ab initio} accuracy. HermNet performs impressively against many
top-performing models on both molecular and extended systems. Specifically,
HermNet outperforms other tested models in nearly 75\%, 83\% and 94\% of tasks
on MD17, QM9 and extended systems datasets, respectively. Finally, we elucidate
how the design of HermNet is compatible with quantum mechanics from the
perspective of the density functional theory. Besides, HermNet is a universal
framework, whose sub-networks could be replaced by other advanced models.
