176 citations · 1.3k across the 25 of their papers we have counts for
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Accurate computational prediction of core-electron binding energies in carbon-based materials: A machine-learning model combining density-functional theory and
Dorothea Golze, Markus Hirvensalo, Patricia Hernández-León +6
We present a quantitatively accurate machine-learning (ML) model for the computational prediction of core-electron binding energies, from which x-ray photoelectron spectroscopy (XP…
Structure and Pore Size Distribution in Nanoporous Carbon
Yanzhou Wang, Zheyong Fan, Ping Qian +2
We study the structural and mechanical properties of nanoporous (NP) carbon materials by extensive atomistic machine-learning (ML) driven molecular dynamics (MD) simulations. To th…
Machine learning force fields based on local parametrization of dispersion interactions: Application to the phase diagram of C
Heikki Muhli, Xi Chen, Albert P. Bartók +4
We present a comprehensive methodology to enable addition of van der Waals (vdW) corrections to machine learning (ML) atomistic force fields. Using a Gaussian approximation potenti…