We provide an introduction to Gaussian process regression (GPR) machine-learning
methods in computational materials science and chemistry. The focus of the present review …

The accuracy and efficiency of electronic-structure methods to understand, predict and
design the properties of materials has driven a new paradigm in research. Simulations can …

The low-temperature hydrogenation of CO2 to methanol is of great significance for the
recycling of this greenhouse gas to valuable products, however, it remains a great challenge …

The WIEN2k program is based on the augmented plane wave plus local orbitals (APW+ lo)
method to solve the Kohn–Sham equations of density functional theory. The APW+ lo …

Carbon structures with covalent bonds connecting C60 molecules have been reported,–, but
their production methods typically result in very small amounts of sample, which restrict the …

The importance of the solid–electrolyte interphase (SEI) for reversible operation of Li-ion
batteries has been well established, but the understanding of its chemistry remains …

At present the most successful rechargeable battery is the Li-ion battery, due to the small
size, high energy density, and low reduction potential of Li. Computational materials science …

In the pursuit of urgently needed, energy dense solid-state batteries for electric vehicle and
portable electronics applications, halide solid electrolytes offer a promising path forward with …

Silica-based optical fibers, fiber-based devices and optical fiber sensors are today
integrated in a variety of harsh environments associated with radiation constraints. Under …

Point defects and impurities strongly affect the physical properties of materials and have a
decisive impact on their performance in applications. First-principles calculations have …