36 citations · 104 across the 9 of their papers we have counts for
11 papers
Efficient Grand Canonical Global Optimization with On-the-fly-trained Machine-learning Interatomic Potentials
Jon Eunan Quinlivan Dominguez, Mads-Peter Verner Christiansen, Konstantin M. Neyman +2
The characterization of nanostructured materials under reactive environments is challenging due to the complexity of the structural motifs involved and their chemical transformatio…
Active Δ-learning with universal potentials for global structure optimization
Joe Pitfield, Mads-Peter Verner Christiansen, Bjørk Hammer
Universal machine learning interatomic potentials (uMLIPs) have recently been formulated and shown to generalize well. When applied out-of-sample, further data collection for impro…
Gradient-based grand canonical optimization enabled by graph neural networks with fractional atomic existence
Mads-Peter Verner Christiansen, Bjørk Hammer
Machine learning interatomic potentials have become an indispensable tool for materials science, enabling the study of larger systems and longer timescales. State-of-the-art models…
-model correction of Foundation Model based on the models own understanding
Mads-Peter Verner Christiansen, Bjørk Hammer
Foundation models of interatomic potentials, so called universal potentials, may require fine-tuning or residual corrections when applied to specific subclasses of materials. In th…
Augmentation of Universal Potentials for Broad Applications
Joe Pitfield, Florian Brix, Zeyuan Tang +4
Universal potentials open the door for DFT level calculations at a fraction of their cost. We find that for application to systems outside the scope of its training data, CHGNet\ci…
Efficient ensemble uncertainty estimation in Gaussian Processes Regression
Mads-Peter Verner Christiansen, Nikolaj Rønne, Bjørk Hammer
Reliable uncertainty measures are required when using data based machine learning interatomic potentials (MLIPs) for atomistic simulations. In this work, we propose for sparse Gaus…
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…
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…
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…
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…