  The evolution of AI and high-throughput technologies has boosted a rapid
increase in the number of new materials, challenging our computational ability
to comprehensively analyze their properties. Relaxed crystal structures often
serve as the foundational basis for further property calculations. However,
determining equilibrium structures traditionally involves computationally
expensive iterative calculations. Here, we develop DeepRelax, an efficient deep
generative model designed for rapid structural relaxation without any iterative
process. DeepRelax learns the equilibrium structural distribution, enabling it
to predict relaxed structures directly from their unrelaxed counterparts. The
ability to perform structural relaxation in just a few hundred milliseconds per
structure, combined with the scalability of parallel processing, makes
DeepRelax particularly useful for large-scale virtual screening. To demonstrate
the universality of DeepRelax, we benchmark it against three different
databases of X-Mn-O oxides, Materials Project, and Computational 2D Materials
Database with various types of materials. In these tests, DeepRelax exhibits
both high accuracy and efficiency in structural relaxation, as further
validated by DFT calculations. Finally, we integrate DeepRelax with an
implementation of uncertainty quantification, enhancing its reliability and
trustworthiness in material discovery. This work provides an efficient and
trustworthy method to significantly accelerate large-scale computations,
offering substantial advancements in the field of computational materials
science.
