Effect of three-body interaction on structural features of phosphate glasses from molecular dynamics simulations DOI
Navid Marchin, Shingo Urata, Jincheng Du

et al.

The Journal of Chemical Physics, Journal Year: 2024, Volume and Issue: 161(15)

Published: Oct. 16, 2024

Understanding the structures of phosphate glasses is important to many their technological applications. Molecular dynamics simulations are commonly used generate structure models sodium glasses, and those with partial charge pairwise potentials have been successfully applied for modeling other network such as silicate aluminosilicate glasses. In this work, we show that addition a three-body term essential in regulating intertetrahedral bond angles, well Qn speciation comparison experiments. Simulation results without terms were compared validated experimental results, including neutron factors. Further glass fully relaxed first-principles density functional theory was performed evaluate simulation results. The vital it can significantly improve description short- medium-range properties.

Language: Английский

ABFML: A problem-oriented package for rapidly creating, screening, and optimizing new machine learning force fields DOI
Xingze Geng,

Jianing Gu,

Gaowu Qin

et al.

The Journal of Chemical Physics, Journal Year: 2025, Volume and Issue: 162(5)

Published: Feb. 4, 2025

Machine Learning Force Fields (MLFFs) require ongoing improvement and innovation to effectively address challenges across various domains. Developing MLFF models typically involves extensive screening, tuning, iterative testing. However, existing packages based on a single mature descriptor or model are unsuitable for this process. Therefore, we developed package named ABFML, PyTorch, which aims promote by providing developers with rapid, efficient, user-friendly tool constructing, validating new force field models. Moreover, leveraging standardized module operations cutting-edge machine learning frameworks, can swiftly establish In addition, the platform seamlessly transition graphics processing unit environments, enabling accelerated calculations large-scale parallel simulations of molecular dynamics. contrast traditional from-scratch approaches development, ABFML significantly lowers barriers developing models, thereby expediting application within development

Language: Английский

Citations

0
Applications of machine learning in surfaces and interfaces DOI Open Access
Shaofeng Xu, Jing‐Yuan Wu, Ying Guo

et al.

Chemical Physics Reviews, Journal Year: 2025, Volume and Issue: 6(1)

Published: March 1, 2025

Surfaces and interfaces play key roles in chemical material science. Understanding physical processes at complex surfaces is a challenging task. Machine learning provides powerful tool to help analyze accelerate simulations. This comprehensive review affords an overview of the applications machine study systems materials. We categorize into following broad categories: solid–solid interface, solid–liquid liquid–liquid surface solid, liquid, three-phase interfaces. High-throughput screening, combined first-principles calculations, force field accelerated molecular dynamics simulations are used rational design such as all-solid-state batteries, solar cells, heterogeneous catalysis. detailed information on for

Language: Английский

Citations

0
Revisiting Machine Learning Potentials for Silicate Glasses: The Missing Role of Dispersion Interactions DOI Creative Commons
Alfonso Pedone, Marco Bertani,

Matilde Benassi

et al.

Journal of Chemical Theory and Computation, Journal Year: 2025, Volume and Issue: unknown

Published: April 24, 2025

Machine learning interatomic potentials (MLIPs) offer a promising alternative to traditional force fields and ab initio methods for simulating complex materials such as oxide glasses. In this work, we present the first evaluation of pretrained MACE (Multi-ACE) model [D.P. Kovács et al., J. Chem. Phys. 159(2023), 044118] silicate glasses, using sodium silicates test case. We compare its performance with DeePMD-based MLIP specifically trained on compositions [M. Bertani Theory Comput. 20(2024), 1358-1370] assess their accuracy in reproducing structural dynamical properties. Additionally, investigate role dispersion interactions by incorporating D3(BJ) correction both models. Our results show that while accurately reproduces neutron structure factors, pair distribution functions, Si[Qn] speciation, it performs slightly worst elastic properties calculations. However, is suitable simulations The inclusion significantly improves reproduction density MLIPs, highlighting critical glass modeling. These findings provide insight into transferability general MLIPs disordered systems emphasize need dispersion-aware training data sets developing accurate

Language: Английский

Citations

0
Development and Validation of Neural Network Potentials for Multicomponent Oxide Glasses DOI

Ryuki Kayano,

Yaohiro Inagaki,

Ryuta Matsubara

et al.

The Journal of Physical Chemistry C, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 2, 2024

Language: Английский

Citations

2
Li diffusion in oxygen–chlorine mixed anion borosilicate glasses using a machine-learning simulation DOI
Shingo Urata,

Noriyoshi Kayaba

The Journal of Chemical Physics, Journal Year: 2024, Volume and Issue: 161(13)

Published: Oct. 1, 2024

Lithium-ion conducting borate glasses are suitable for solid-state batteries as an interfacial material between a crystalline electrolyte and electrode, thanks to their superior formability. Chlorine has been known improve the electron conductivity of secondary anion. To examine impact chlorine on lithium dynamics, molecular dynamics (MD) simulations were performed with machine-learning interatomic potential (MLIP). The accuracy MLIP in modeling chlorine-doped (LBCl) borosilicate (LBSCl) was verified by comparing available experimental data density, neutron diffraction S(q), glass transition temperatures (Tg). While MLIP-MD underestimated density when isobaric–isothermal (NPT) ensemble used, models relaxed using NPT after melt-quench simulation employing canonical (NVT) possessed reasonable density. LBCl LBSCl exhibited increased ion diffusion, ions found travel longer distances increase content. According structural analyses, it observed that primarily interacted rather than network formers. Consequently, higher amount showed moderate mobility. In summary, demonstrated chlorine-containing enabled investigation effect conductivity. contrast, first sharp peaks S(q) deviated from diffractions, suggesting additional efforts required accurately model middle-range structure glasses.

Language: Английский

Citations

1
Effect of three-body interaction on structural features of phosphate glasses from molecular dynamics simulations DOI
Navid Marchin, Shingo Urata, Jincheng Du

et al.

The Journal of Chemical Physics, Journal Year: 2024, Volume and Issue: 161(15)

Published: Oct. 16, 2024

Understanding the structures of phosphate glasses is important to many their technological applications. Molecular dynamics simulations are commonly used generate structure models sodium glasses, and those with partial charge pairwise potentials have been successfully applied for modeling other network such as silicate aluminosilicate glasses. In this work, we show that addition a three-body term essential in regulating intertetrahedral bond angles, well Qn speciation comparison experiments. Simulation results without terms were compared validated experimental results, including neutron factors. Further glass fully relaxed first-principles density functional theory was performed evaluate simulation results. The vital it can significantly improve description short- medium-range properties.

Language: Английский

Citations

1