Advances in machine learning have impacted myriad areas of materials science, such as
the discovery of novel materials and the improvement of molecular simulations, with likely …

Rational design of compounds with specific properties requires understanding and fast
evaluation of molecular properties throughout chemical compound space—the huge set of …

Quantum matter, the research field studying phases of matter whose properties are
intrinsically quantum mechanical, draws from areas as diverse as hard condensed matter …

Density functional theory (DFT) is the standard formalism to study the electronic structure of
matter at the atomic scale. In Kohn–Sham DFT simulations, the balance between accuracy …

We propose a general machine learning-based framework for building an accurate and
widely applicable energy functional within the framework of generalized Kohn–Sham …

We propose a new molecular simulation framework that combines the transferability,
robustness, and chemical flexibility of an ab initio method with the accuracy and efficiency of …

A proper treatment of electron correlation effects is indispensable for accurate simulation of
compounds. Various post-Hartree–Fock methods have been adopted to calculate …

We introduce DeePKS-kit, an open-source software package for developing machine
learning based energy and density functional models. DeePKS-kit is interfaced with …

Two types of approaches to modeling molecular systems have demonstrated high practical
efficiency. Density functional theory (DFT), the most widely used quantum chemical method …

One of the potential applications of a quantum computer is solving quantum chemical
systems. It is known that one of the fastest ways to obtain somewhat accurate solutions …