Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well
known from computer science is broadly affecting many aspects of various fields including …

Two-dimensional (2D) material research is rapidly evolving to broaden the spectrum of
emergent 2D systems. Here, we review recent advances in the theory, synthesis …

In recent years, we have been witnessing a paradigm shift in computational materials
science. In fact, traditional methods, mostly developed in the second half of the XXth century …

Electron–phonon coupling is important in many physical phenomena, eg photosynthesis,
catalysis and quantum information processing, but its impacts are difficult to grasp on the …

Emerging machine learning (ML) methods are widely applied in chemistry and materials
science studies and have led to a focus on data‐driven research. This Minireview …

With its extremely strong capability of data analysis, machine learning has shown versatile
potential in the revolution of the materials research paradigm. Here, taking dielectric …

We introduce HP, an implementation of density-functional perturbation theory, designed to
compute Hubbard parameters (on-site U and inter-site V) in the framework of DFT+U and …

Accurate computational predictions of band gaps are of practical importance to the modeling
and development of semiconductor technologies, such as (opto) electronic devices and …

We review the GPAW open-source Python package for electronic structure calculations.
GPAW is based on the projector-augmented wave method and can solve the self-consistent …

An underestimation of the fundamental band gap values by the density functional theory
within the local density approximation and associated approaches is a well-known …