What question did this study set out to answer?

To provide an introductory overview and tutorial on constructing machine learning interatomic potentials using the Gaussian approximation potential framework.

April 27, 2015International Journal of Quantum ChemistryOpen Access

Gaussian approximation potentials: A brief tutorial introduction

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Authors

ABAlbert P. BartókUniversity of Warwick GCGábor CśanyiUniversity of Cambridge

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Implication

Methodological tutorial demonstrates fitting machine learning interatomic potentials to quantum mechanical data, indicating practical frameworks for molecular simulation.

Key Points

To provide an introductory overview and tutorial on constructing machine learning interatomic potentials using the Gaussian approximation potential framework.
Outlined the Gaussian approximation potential (GAP) framework trained on quantum mechanical total energies and their derivatives.
Discussed atomic environment descriptors and strategies for combining multiple models across different levels of complexity.
Implemented an illustrative example utilizing QUIP, a dedicated software environment for GAP modeling.
Demonstrated how machine learning models reliably interpolate complex quantum mechanical energy surfaces using targeted atomic descriptors.
Established practical workflows for simultaneously integrating multi-fidelity models into atomistic simulations via available software tools.

Cite This Study

Bartók et al. (2015) studied this question.

synapsesocial.com/papers/69dcd0732e182f147e1333fe https://doi.org/10.1002/qua.24927

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Implication

Methodological tutorial demonstrates fitting machine learning interatomic potentials to quantum mechanical data, indicating practical frameworks for molecular simulation.

Gaussian approximation potentials: A brief tutorial introduction

Discussion

Member takes

Implication

Key Points

Cite This Study

Gaussian approximation potentials: A brief tutorial introduction

Discussion

Member takes

Implication

Key Points

Cite This Study

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