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19992023
most citedOn-the-fly machine learning force field generation: Application to melting points

688 citations · 7.1k across the 80 of their papers we have counts for

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Showing 2019Show all

8 papers · 1 filter

cond-mat.str-el2019

Optical response of an interacting polaron gas in strongly polar crystals

S. N. Klimin, J. Tempere, J. T. Devreese +2

Optical conductivity of an interacting polaron gas is calculated within an extended random phase approximation which takes into account mixing of collective excitations of the elec…

physics.chem-ph2019★ 23 cited

RPA natural orbitals and their application to post-Hartree-Fock electronic structure methods

Benjamin Ramberger, Zoran Sukurma, Tobias Schäfer +1

We present a method to approximate post-Hartree-Fock correlation energies by using approximate natural orbitals obtained by the random phase approximation (RPA). We demonstrate the…

cond-mat.mtrl-sci2019★ 40 cited

Minimax Isometry Method: A compressive sensing approach for Matsubara summation in many-body perturbation theory

Merzuk Kaltak, Georg Kresse

We present a compressive sensing approach for the long standing problem of Matsubara summation in many-body perturbation theory. By constructing low-dimensional, almost isometric s…

cond-mat.mtrl-sci2019★ 688 cited

On-the-fly machine learning force field generation: Application to melting points

Ryosuke Jinnouchi, Ferenc Karsai, Georg Kresse

An efficient and robust on-the-fly machine learning force field method is developed and integrated into an electronic-structure code. This method realizes automatic generation of m…

cond-mat.mtrl-sci2019★ 33 cited

First-principle study of the melting temperature of MgO

Max Rang, Georg Kresse

Using first-principles only, we calculate the melting point of MgO, also called periclase or magnesia. The random phase approximation (RPA) is used to include the exact exchange as…

cond-mat.mtrl-sci2019★ 646 cited

Phase transitions of hybrid perovskites simulated by machine-learning force fields trained on-the-fly with Bayesian inference

Ryosuke Jinnouchi, Jonathan Lahnsteiner, Ferenc Karsai +2

Realistic finite temperature simulations of matter are a formidable challenge for first principles methods. Long simulation times and large length scales are required, demanding ye…

cond-mat.mtrl-sci2019★ 35 cited

Small Polarons in Transition Metal Oxides

Michele Reticcioli, Ulrike Diebold, Georg Kresse +1

The formation of polarons is a pervasive phenomenon in transition metal oxide compounds, with a strong impact on the physical properties and functionalities of the hosting material…

cond-mat.mtrl-sci2019★ 55 cited

Assessing model-dielectric-dependent hybrid functionals on the antiferromagnetic transition-metal monoxides MnO, FeO, CoO, and NiO

Peitao Liu, Cesare Franchini, Martijn Marsman +1

Recently, two nonempirical hybrid functionals, dielectric-dependent range-separated hybrid functional based on the Coulomb-attenuating method (DD-RSH-CAM) and doubly screened hybri…