activity
20182020
most citedData-driven kinetic energy density fitting for orbital-free DFT: linear vs Gaussian process regression

46 citations · 52 across the 4 of their papers we have counts for

collaborators

6 papers

cond-mat.mtrl-sci2020

Halide perovskites: third generation photovoltaic materials empowered by metavalent bonding

Matthias Wuttig, Carl-Friedrich Schoen, Mathias Schumacher +4

Third-generation photovoltaic (PV) materials combine many advantageous properties, including a high optical absorption together with a large charge carrier mobility, facilitated by…

physics.chem-ph20204 cited

Automatic selection of active spaces for strongly correlated systems using machine learning algorithms

Pavlo Golub, Andrej Antalik, Libor Veis +1

The active-space quantum chemical methods could provide very accurate description of strongly correlated electronic systems, which is of tremendous value for natural sciences. The…

cond-mat.mtrl-sci20202 cited

Discovering electron transfer driven changes in chemical bonding in lead chalcogenides (PbX, where X = Te, Se, S, O)

S. Maier, S. Steinberg, Y. Cheng +8

Understanding the nature of chemical bonding in solids is crucial to comprehend the physical and chemical properties of a given compound. To explore changes in chemical bonding in…

physics.comp-ph202046 cited

Data-driven kinetic energy density fitting for orbital-free DFT: linear vs Gaussian process regression

Sergei Manzhos, Pavlo Golub

We study the dependence of kinetic energy densities (KED) on density-dependent variables that have been suggested in previous works on kinetic energy functionals (KEF) for orbital-…

physics.chem-ph2019

Revisiting Backbonding: The Influence of Orbitals on Metal-CO Bonds and Ligand Red Shifts

Daniel Koch, Yingqian Chen, Pavlo Golub +1

The concept of backbonding is widely used to explain the complex stabilities and CO stretch frequency red shifts of transition metal carbonyls. We theoretically investigate a n…

physics.comp-ph2018

Kinetic energy densities based on the fourth order gradient expansion: performance in different classes of materials and improvement via machine learning

Pavlo Golub, Sergei Manzhos

We study the performance of fourth-order gradient expansions of the kinetic energy density (KED) in semi-local kinetic energy functionals depending on the density-dependent variabl…