  Due to the vast chemical space, discovering materials with a specific
function is challenging. Chemical formulas are obligated to conform to a set of
exacting criteria such as charge neutrality, balanced electronegativity,
synthesizability, and mechanical stability. In response to this formidable
task, we introduce a deep learning-based generative model for material
composition and structure design by learning and exploiting explicit and
implicit chemical knowledge. Our pipeline first uses deep diffusion language
models as the generator of compositions and then applies a template-based
crystal structure prediction algorithm to predict their corresponding
structures, which is then followed by structure relaxation using a universal
graph neural network-based potential. The density functional theory (DFT)
calculations of the formation energies and energy-above-the-hull analysis are
used to validate new structures generated through our pipeline. Based on the
DFT calculation results, six new materials, including Ti2HfO5, TaNbP, YMoN2,
TaReO4, HfTiO2, and HfMnO2, with formation energy less than zero have been
found. Remarkably, among these, four materials, namely Ti2$HfO5, TaNbP, YMoN2,
and TaReO4, exhibit an e-above-hull energy of less than 0.3 eV. These findings
have proved the effectiveness of our approach.
