193 citations · 1.2k across the 22 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…
Atomistic Global Optimization X: A Python package for optimization of atomistic structures
Mads-Peter Verner Christiansen, Nikolaj Rønne, Bjørk Hammer
Modelling and understanding properties of materials from first principles require knowledge of the underlying atomistic structure. This entails knowing the individual identity and…
Generating stable molecules using imitation and reinforcement learning
Søren Ager Meldgaard, Jonas Köhler, Henrik Lund Mortensen +3
Chemical space is routinely explored by machine learning methods to discover interesting molecules, before time-consuming experimental synthesizing is attempted. However, these met…
Global optimization of atomistic structure enhanced by machine learning
Malthe K. Bisbo, Bjørk Hammer
Global Optimization with First-principles Energy Expressions (GOFEE) is an efficient method for identifying low energy structures in computationally expensive energy landscapes suc…
Efficient global structure optimization with a machine learned surrogate model
Malthe K. Bisbo, Bjørk Hammer
We propose a scheme for global optimization with first-principles energy expressions (GOFEE) of atomistic structure. While unfolding its search, the method actively learns a surrog…