Deep-learning electronic structure calculations show great potential for revolutionizing the
landscape of computational materials research. However, current neural-network …

Deep-learning density functional theory (DFT) shows great promise to significantly
accelerate material discovery and potentially revolutionize materials research. However …

Neural network force fields have significantly advanced ab initio atomistic simulations across
diverse fields. However, their application in the realm of magnetic materials is still in its early …

Deep neural networks capable of representing the density functional theory (DFT)
Hamiltonian as a function of material structure hold great promise for revolutionizing future …

The combination of deep learning and ab initio materials calculations is emerging as a
trending frontier of materials science research, with deep-learning density functional theory …

Realizing large materials models has emerged as a critical endeavor for materials research
in the new era of artificial intelligence, but how to achieve this fantastic and challenging …

The marriage of artificial intelligence (AI) and Materials Genome Initiative (MGI) could
profoundly change the landscape of modern materials research, leading to a new paradigm …

Deep learning electronic structures from ab initio calculations holds great potential to
revolutionize computational materials studies. While existing methods proved success in …