193 citations · 1.2k across the 22 of their papers we have counts for
23 papers
Generating candidates in global optimization algorithms using complementary energy landscapes
Andreas Møller Slavensky, Mads-Peter V. Christensen, Bjørk Hammer
Global optimization of atomistic structure rely on the generation of new candidate structures in order to drive the exploration of the potential energy surface (PES) in search for…
A machine learning potential for simulating infrared spectra of nanosilicate clusters
Zeyuan Tang, Stefan T. Bromley, Bjørk Hammer
The use of machine learning (ML) in chemical physics has enabled the construction of interatomic potentials having the accuracy of ab initio methods and a computational cost compar…
Machine learning based approach for solving atomic structures of nanomaterials combining pair distribution functions with density functional theory
Magnus Kløve, Sanna Sommer, Bo B. Iversen +2
Determination of crystal structures of nanocrystalline or amorphous compounds is a great challenge in solid states chemistry and physics. Pair distribution function (PDF) analysis…
Atomistic structure search using local surrogate mode
Nikolaj Rønne, Mads-Peter V. Christiansen, Andreas Møller Slavensky +5
We describe a local surrogate model for use in conjunction with global structure search methods. The model follows the Gaussian approximation potential (GAP) formalism and is based…
Top-down formation of ethylene from fragmentation of superhydrogenated polycyclic aromatic hydrocarbons
Zeyuan Tang, Frederik Doktor S. Simonsen, Rijutha Jaganathan +4
Fragmentation is an important decay mechanism for polycyclic aromatic hydrocarbons (PAHs) under harsh interstellar conditions and represents a possible formation pathway for small…
Dimerization of dehydrogenated polycyclic aromatic hydrocarbons on graphene
Zeyuan Tang, Bjørk Hammer
Dimerization of polycyclic aromatic hydrocarbons (PAHs) is an important, yet poorly understood, step in the on-surface synthesis of graphene (nanoribbon), soot formation, and growt…