# Self-learning hybrid Monte Carlo method for isothermal-isobaric ensemble: Application to liquid silica.

@article{Kobayashi2021SelflearningHM, title={Self-learning hybrid Monte Carlo method for isothermal-isobaric ensemble: Application to liquid silica.}, author={Keita Kobayashi and Yuki Nagai and Mitsuhiro Itakura and Motoyuki Shiga}, journal={The Journal of chemical physics}, year={2021}, volume={155 3}, pages={ 034106 } }

Self-learning hybrid Monte Carlo (SLHMC) is a first-principles simulation that allows for exact ensemble generation on potential energy surfaces based on density functional theory. The statistical sampling can be accelerated with the assistance of smart trial moves by machine learning potentials. In the first report [Nagai et al., Phys. Rev. B 102, 041124(R) (2020)], the SLHMC approach was introduced for the simplest case of canonical sampling. We herein extend this idea to isothermal-isobaric… Expand

#### Figures and Tables from this paper

#### References

SHOWING 1-10 OF 50 REFERENCES

Deep machine learning interatomic potential for liquid silica.

- Medicine, Physics
- Physical review. E
- 2020

Developed NNP allows us to describe the structure of the glassy silica with satisfactory accuracy even though no low-temperature configurations were included in the training procedure, and opens up prospects for simulating structural and dynamical properties of liquids and glasses via NNP. Expand

Strategies for the construction of machine-learning potentials for accurate and efficient atomic-scale simulations

- Physics
- Machine Learning: Science and Technology
- 2021

Recent advances in machine-learning interatomic potentials have enabled the efficient modeling of complex atomistic systems with an accuracy that is comparable to that of conventional… Expand

Constructing high‐dimensional neural network potentials: A tutorial review

- Physics
- 2015

A lot of progress has been made in recent years in the development of atomistic potentials using machine learning (ML) techniques. In contrast to most conventional potentials, which are based on… Expand

Efficient training of ANN potentials by including atomic forces via Taylor expansion and application to water and a transition-metal oxide

- Computer Science, Physics
- npj Computational Materials
- 2020

The alternative force-training approach thus simplifies the construction of general ANN potentials for the prediction of accurate energies and interatomic forces for diverse types of materials, as demonstrated here for water and a transition-metal oxide. Expand

Efficient and accurate machine-learning interpolation of atomic energies in compositions with many species

- Physics
- 2017

Author(s): Artrith, N; Urban, A; Ceder, G | Abstract: © 2017 us. Published by the American Physical Society. Machine-learning potentials (MLPs) for atomistic simulations are a promising alternative… Expand

An implementation of artificial neural-network potentials for atomistic materials simulations: Performance for TiO2

- Materials Science
- 2016

Abstract Machine learning interpolation of atomic potential energy surfaces enables the nearly automatic construction of highly accurate atomic interaction potentials. Here we discuss the… Expand

Comparison of different machine learning models for the prediction of forces in copper and silicon dioxide.

- Materials Science, Medicine
- Physical chemistry chemical physics : PCCP
- 2018

It is found that using angular structural fingerprints and a mixture model method significantly improves the accuracy of ML force fields, and an effective structural fingerprint auto-selection method based on the least absolute shrinkage and selection operator and the genetic algorithm is discussed. Expand

Ab initio molecular dynamics for liquid metals.

- Chemistry, Medicine
- Physical review. B, Condensed matter
- 1993

It is shown that the exact calculation of the electronic groundstate at each MD timestep is feasible using modern iterative matrix diagonalization algorithms and together with the use of ultrasoft pseudopotentials, ab initio MD simulations can be extended to open-shell transition metals with a high density of states at the Fermi-level. Expand

Computer Simulations of Supercooled Liquids and Glasses

- Materials Science, Chemistry
- 1998

After a brief introduction to the dynamics of supercooled liquids, we discuss some of the advantages and drawbacks of computer simulations of such systems. Subsequently we present the results of… Expand

Generalized Gradient Approximation Made Simple.

- Physics, Medicine
- Physical review letters
- 1996

A simple derivation of a simple GGA is presented, in which all parameters (other than those in LSD) are fundamental constants, and only general features of the detailed construction underlying the Perdew-Wang 1991 (PW91) GGA are invoked. Expand