169 citations · 304 across the 4 of their papers we have counts for
4 papers
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…
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…
Atomistic Structure Learning Algorithm with surrogate energy model relaxation
Henrik Lund Mortensen, Søren Ager Meldgaard, Malthe Kjær Bisbo +2
The recently proposed Atomistic Structure Learning Algorithm (ASLA) builds on neural network enabled image recognition and reinforcement learning. It enables fully autonomous struc…
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…