Size and Quality of Quantum Mechanical Data Set for Training Neural Network Force Fields for Liquid Water-Reference-Cited by-同舟云学术

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Size and Quality of Quantum Mechanical Data Set for Training Neural Network Force Fields for Liquid Water

Published:2023-02-02 Issue:6 Volume:127 Page:1422-1428
ISSN:1520-6106
Container-title:The Journal of Physical Chemistry B
language:en
Short-container-title:J. Phys. Chem. B

Author:

Gomes-Filho Márcio S.¹,Torres Alberto²,Reily Rocha Alexandre³^ORCID,Pedroza Luana S.¹^ORCID

Affiliation:

1. Centro de Ciências Naturais e Humanas, Universidade Federal do ABC, Santo André, 09210-580 São Paulo, Brazil

2. Instituto de Física, Universidade de São Paulo, São Paulo 05508-090, Brazil

3. Institute of Theoretical Physics, São Paulo State University, Campus São Paulo 01140-070, Brazil

Funder

Fundação de Amparo à Pesquisa do Estado de São Paulo

Publisher

American Chemical Society (ACS)

Subject

Materials Chemistry,Surfaces, Coatings and Films,Physical and Theoretical Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpcb.2c09059

Reference71 articles.

1. Classical molecular dynamics

2. Molecular Dynamics Simulation for All

3. A Review of Recent Progress in Molecular Dynamics and Coarse-Grain Simulations Assisted Understanding of Wettability

4. E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials

5. Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A machine learning potential construction based on radial distribution function sampling;Journal of Computational Chemistry;2024-09-03

2. Tuning Interfacial Water Friction through Moiré Twist;ACS Nano;2024-06-10

3. Perspective: Atomistic simulations of water and aqueous systems with machine learning potentials;The Journal of Chemical Physics;2024-05-01

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