# Improving accuracy of interatomic potentials: more physics or more data? A case study of silica

@article{Novikov2019ImprovingAO,
title={Improving accuracy of interatomic potentials: more physics or more data? A case study of silica},
author={Ivan S. Novikov and Alexander V. Shapeev},
journal={Materials Today Communications},
year={2019}
}
• Published 11 August 2018
• Physics
• Materials Today Communications
25 Citations

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## References

SHOWING 1-10 OF 68 REFERENCES
Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space
• Chemistry
The journal of physical chemistry letters
• 2015
A systematic hierarchy of efficient empirical methods to estimate atomization and total energies of molecules and is achieved by a vectorized representation of molecules (so-called Bag of Bonds model) that exhibits strong nonlocality in chemical space.
Data-Driven Learning of Total and Local Energies in Elemental Boron.
• Materials Science
Physical review letters
• 2018
This model readily provides atom-resolved, local energies and thus deepened insight into the frustrated β-rhombohedral boron structure, and opens the door for the efficient and automated generation of GAPs, and other machine-learning-based interatomic potentials, to suggest their usefulness as a tool for materials discovery.
Transferable Dynamic Molecular Charge Assignment Using Deep Neural Networks.
• Chemistry
Journal of chemical theory and computation
• 2018
HIP-NN charge predictions are many orders of magnitude faster than direct DFT calculations, and combined results provide further evidence that ML (specifically HIP-NN) provides a pathway to greatly increase the range of feasible simulations while retaining quantum-level accuracy.
Representing the potential-energy surface of protonated water clusters by high-dimensional neural network potentials.
• Chemistry
Physical chemistry chemical physics : PCCP
• 2015
A reactive full-dimensional NN potential for protonated water clusters up to the octamer is presented, showing that the energetic, structural, and vibrational properties are in excellent agreement with DFT results making the NN approach a very promising candidate for developing a high-quality potential for water.
Machine learning based interatomic potential for amorphous carbon
• Materials Science
• 2017
We introduce a Gaussian approximation potential (GAP) for atomistic simulations of liquid and amorphous elemental carbon. Based on a machine learning representation of the density-functional theory
A first principles based polarizable O(N) interatomic force field for bulk silica.
• Materials Science
The Journal of chemical physics
• 2010
A reformulation of the Tangney-Scandolo interatomic force field for silica is presented, which removes the requirement to perform an Ewald summation and the resulting O(N) scheme makes it possible to model hundreds of thousands of atoms with modest computational resources, without compromising the force field accuracy.
Learning scheme to predict atomic forces and accelerate materials simulations
• Physics
• 2015
It is argued that pathways are available to systematically and continuously improve the predictive capability of such a learned force field in an adaptive manner, and that this concept can be generalized to include multiple elements.
Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems.
• Computer Science
Physical review letters
• 2018
This work introduces a formalism that extends existing schemes and makes it possible to perform machine learning of tensorial properties of arbitrary rank, and for general molecular geometries, and derives a tensor kernel adapted to rotational symmetry.
Fast machine learning models of electronic and energetic properties consistently reach approximation errors better than DFT accuracy
• Computer Science
• 2017
Numerical evidence is presented that ML model predictions for all properties can reach an approximation error to DFT which is on par with chemical accuracy.