1 citations · 2 across the 6 of their papers we have counts for
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Cluster-based Structural Similarity for Dataset Visualization and Data Selection for Machine Learning Interatomic Potentials
Yuto Iwasaki, Miguel A. Caro
Machine learning interatomic potentials (MLIPs) are essential components for accelerating simulation-driven materials design. Data-efficient MLIP training relies on data-selection…
Molecular augmented dynamics: Generating experimentally consistent atomistic structures by design
Tigany Zarrouk, Miguel A. Caro
A fundamental objective of materials modeling is identifying atomic structures that align with experimental observables. Conventional approaches for disordered materials involve sa…
Linear-scaling calculation of experimental observables for molecular augmented dynamics simulations
Tigany Zarrouk, Miguel A. Caro
Aligning theoretical atomistic structural models of materials with available experimental data presents a significant challenge for disordered systems. The configurational space to…
Experiment-driven atomistic materials modeling: A case study combining X-ray photoelectron spectroscopy and machine learning potentials to infer the structure of oxygen-rich amorphous carbon
Tigany Zarrouk, Rina Ibragimova, Albert P. Bartók +1
An important yet challenging aspect of atomistic materials modeling is reconciling experimental and computational results. Conventional approaches involve generating numerous confi…
Gaussian Approximation Potentials: theory, software implementation and application examples
Sascha Klawohn, Gábor Csányi, James P. Darby +3
Gaussian Approximation Potentials are a class of Machine Learned Interatomic Potentials routinely used to model materials and molecular systems on the atomic scale. The software im…