  Deep learning is changing many areas in molecular physics, and it has shown
great potential to deliver new solutions to challenging molecular modeling
problems. Along with this trend arises the increasing demand of expressive and
versatile neural network architectures which are compatible with molecular
systems. A new deep neural network architecture, Molecular Configuration
Transformer (Molecular CT), is introduced for this purpose. Molecular CT is
composed of a relation-aware encoder module and a computationally universal
geometry learning unit, thus able to account for the relational constraints
between particles meanwhile scalable to different particle numbers and
invariant with respect to the trans-rotational transforms. The computational
efficiency and universality make Molecular CT versatile for a variety of
molecular learning scenarios and especially appealing for transferable
representation learning across different molecular systems. As examples, we
show that Molecular CT enables representational learning for molecular systems
at different scales, and achieves comparable or improved results on common
benchmarks using a more light-weighted structure compared to baseline models.
