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, are …

The absorption coefficient of the dye used in dye-sensitized solar cells is a major factor in the
total energy efficiency of the cell. In this work, we increased the absorption coefficient of the …

Lead-halide perovskite solar cells (PSCs) have attracted attention due to their outstanding
high power-conversion efficiency. In conventional inorganic solar cells such as Si solar cells, …

The reaction mechanism of the most commonly used anode material, Ni/yttria-stabilized
zirconia (YSZ) cermets, in solid oxide fuel cells (SOFCs) was investigated. Because the reaction …

Feed-forward neural networks (NNs) are a staple machine learning method widely used in
many areas of science and technology, including physical chemistry, computational chemistry…

We show that Gaussian process regression (GPR) allows representing multivariate
functions with low-dimensional terms via kernel design. When using a kernel built with high-…

Photoelectric conversion efficiency (E ff ) of a dye-sensitized solar cell (DSSC) was improved
by localized surface plasmon on silver (Ag) nanoparticles modified with polyacrylate-based …

The electron density determines all properties of a system of nuclei and electrons. It is both
computable and observable. Its topology allows gaining insight into the mechanisms of …

Machine learning (ML) based methods and tools have now firmly established themselves in
physical chemistry and in particular in theoretical and computational chemistry and in …

The density functional-based tight binding (DFTB) method has seen a rise in adoption for
materials modeling, as it offers significant improvement in scalability with accuracy comparable …