High-dimensional neural network potentials for metal surfaces: A prototype study for copper

By Nongnuch Artrith, J. Behler. 2012

243 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Materials Chemistry

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

In Materials Chemistry

Citations in the corpus

JAX, M.D.: End-to-End Differentiable, Hardware Accelerated, Molecular Dynamics in Pure Python (2019, methodology): “…2017; Ma et al., 2019), as well as making MD simulations faster and more accurate (Behler & Parrinello, 2007; Behler, 2011; Artrith et al., 2011; Artrith & Behler, 2012; Artrith et al., 2018; Deringer et al., 2018a; Bartók et al., 2018; Yao et al., 2018; Seko et al., 2015; Deringer et al.,…”
Extending the applicability of the ANI deep learning molecular potential to Sulfur and Halogens. (2020, methodology): “25,39 This QBC method uses the disagreement of an ensemble of models to accurately predict energies for a given molecule.”
The MLIP package: moment tensor potentials with MPI and active learning (2020, methodology): “Such a strategy was first implemented by Artrith and Behler in [1] for the neural network potential.”
End-to-End Differentiable , Hardware Accelerated , Molecular Dynamics in Pure Python (2019, methodology): “…2017; Ma et al., 2019), as well as making MD simulations faster and more accurate (Behler & Parrinello, 2007; Behler, 2011; Artrith et al., 2011; Artrith & Behler, 2012; Artrith et al., 2018; Deringer et al., 2018a; Bartók et al., 2018; Yao et al., 2018; Seko et al., 2015; Deringer et al.,…”
Machine Learning a General-Purpose Interatomic Potential for Silicon (2018, background): “Modeling the short-range interactions with artificial neural networks (NNs) really took off about a decade ago [35], starting with the bulk phases of silicon [36,37], with many more to follow: describing some silicon defects [38], the graphite-diamond transition [39], bulk zinc oxide [40], copper wi”
Machine Learning for High-entropy Alloys: Progress, Challenges and Opportunities (2022, background): “Another strategy is to estimate the prediction error with multiple models and add new data when the prediction error is larger than a threshold value, which is demonstrated for neural networks [125, 126, 127] and Gaussian process [128, 129].”