Kohn–Sham Density Functional Theory (KSDFT) is the most widely used electronic structure
method in chemistry, physics, and materials science, with thousands of calculations cited …

Abstract Machine learning (ML) is becoming a method of choice for modelling complex
chemical processes and materials. ML provides a surrogate model trained on a reference …

Computational modeling of physical processes in metal-organic frameworks (MOFs) is
highly challenging due to the presence of spatial heterogeneities and complex operating …

With the growth of computational resources, the scope of electronic structure simulations has
increased greatly. Artificial intelligence and robust data analysis hold the promise to …

One essential ingredient in many machine learning (ML) based methods for atomistic
modeling of materials and molecules is the use of locality. While allowing better system-size …

Abstract Machine learning (ML) algorithms are currently emerging as powerful tools in all
areas of science. Conventionally, ML is understood as a fundamentally data‐driven …

The theorems of density functional theory (DFT) establish bijective maps between the local
external potential of a many-body system and its electron density, wavefunction and …

To accurately and efficiently predict the molecular response properties (such as
polarizability) at post-Hartree–Fock levels for condensed-phase systems under periodic …

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

Raman spectroscopy is a powerful and nondestructive method that is widely used to study
the vibrational properties of solids or molecules. Simulations of finite-temperature Raman …