684 citations · 2.5k across the 32 of their papers we have counts for
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An Accurate and Transferable Machine Learning Potential for Carbon
Patrick Rowe, Volker L Deringer, Piero Gasparotto +2
We present an accurate machine learning (ML) model for atomistic simulations of carbon, constructed using the Gaussian approximation potential (GAP) methodology. The potential, nam…
Machine learning driven simulated deposition of carbon films: from low-density to diamondlike amorphous carbon
Miguel A. Caro, Gábor Csányi, Tomi Laurila +1
Amorphous carbon (a-C) materials have diverse interesting and useful properties, but the understanding of their atomic-scale structures is still incomplete. Here, we report on exte…
Understanding the Geometric Diversity of Inorganic and Hybrid Frameworks through Structural Coarse-Graining
Thomas C. Nicholas, Andrew L. Goodwin, Volker L. Deringer
Much of our understanding of complex structures is based on simplification: for example, metal-organic frameworks are often discussed in the context of "nodes" and "linkers", allow…
Combining phonon accuracy with high transferability in Gaussian approximation potential models
Janine George, Geoffroy Hautier, Albert P. Bartók +2
Machine learning driven interatomic potentials, including Gaussian approximation potential (GAP) models, are emerging tools for atomistic simulations. Here, we address the methodol…