This review highlights recent advances in machine learning (ML)‐assisted design of energy
materials. Initially, ML algorithms were successfully applied to screen materials databases …

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …

Interatomic Interaction Models for Magnetic Materials: Recent Advances Page 1 Chinese
Physics Letters ACCEPTED MANUSCRIPT Interatomic Interaction Models for Magnetic …

Nonadiabatic molecular dynamics (NA-MD) is a powerful tool to model far-from-equilibrium
processes, such as photochemical reactions and charge transport. NA-MD application to …

Calculating perturbation response properties of materials from first principles provides a vital
link between theory and experiment, but is bottlenecked by the high computational cost …

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 …

Hybrid density functional calculations are essential for accurate description of electronic
structure, yet their widespread use is restricted by the substantial computational cost. Here …

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

Supervised machine learning approaches have been increasingly used in accelerating
electronic structure prediction as surrogates of first-principle computational methods, such …

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