  Machine learning (ML) models utilizing structure-based features provide an
efficient means for accurate property predictions across diverse chemical
spaces. However, obtaining equilibrium crystal structures typically requires
expensive density functional theory (DFT) calculations, which limits ML-based
exploration to either known crystals or a small number of hypothetical
crystals. Here, we demonstrate that the application of Bayesian optimization
with symmetry constraints using a graph deep learning energy model can be used
to perform "DFT-free" relaxations of crystal structures. Using this approach to
significantly improve the accuracy of ML-predicted formation energies and
elastic moduli of hypothetical crystals, two novel ultra-incompressible hard
materials MoWC2 (P63/mmc) and ReWB (Pca21) were identified and successfully
synthesized via in-situ reactive spark plasma sintering from a screening of
399,960 transition metal borides and carbides. This work addresses a critical
bottleneck to accurate property predictions for hypothetical materials, paving
the way to ML-accelerated discovery of new materials with exceptional
properties.
