Skip to content
Back Home
Computational Chemical Science Center
  • Home
  • About
    • Mission & Major Goals
    • Contact us
  • People
  • Management
    • Executive Committee
    • Internal Advisory Board
    • Scientific Advisory Board
  • Research
  • Software
  • Data
  • Publications
    • Published Papers
    • Papers Under Review
  • News & Events
    • CSI Workshop – July 2023
    • Past Events
    • Pictures
  • Tutorials
  • Search
Back Home
Computational Chemical Science Center
  • Search
  • Home
  • About
    • Mission & Major Goals
    • Contact us
  • People
  • Management
    • Executive Committee
    • Internal Advisory Board
    • Scientific Advisory Board
  • Research
  • Software
  • Data
  • Publications
    • Published Papers
    • Papers Under Review
  • News & Events
    • CSI Workshop – July 2023
    • Past Events
    • Pictures
  • Tutorials
Home » Publications » Structure of disordered TiO2 phases from ab initio based deep neural network simulations
Publications

Structure of disordered TiO2 phases from ab initio based deep neural network simulations

by Douglas Rosso|Published October 20, 2020

Marcos Calegari Andrade, M. F. & Selloni, A. Structure of disordered TiO2 phases from ab initio based deep neural network simulations, Phys. Rev. Mater. 4, 113803 (2020)

Abstract

URL: https://doi.org/10.1103/PhysRevMaterials.4.113803

 

You may also like

Published May 30, 2023

Dynamics of Aqueous Electrolyte Solutions: Challenges for Simulations

A. Z. Panagiotopoulos and S. Yue, J. Phys. Chem. B 127: 430-37 (2023) URL: https://doi.org/10.1021/acs.jpcb.2c07477

Published June 24, 2020

DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models

Y. Zhang, H. Wang, W. Chen, J. Zeng, L. Zhang, H. Wang, W. E Comp. Phys. Comm. (2020) URL: https://www.sciencedirect.com/science/article/pii/S001046552030045X

Published July 23, 2023

Why dissolving salt in water decreases its dielectric permittivity

Chunyi Zhang, Shuwen Yue, Athanassios Z. Panagiotopoulos, Michael L. Klein and Xifan Wu, Phys. Rev. Lett. 131, 076801 (2023) URL: https://doi.org/10.1103/PhysRevLett.131.076801  

Published March 11, 2021

When do short-range atomistic machine-learning models fall short?

Shuwen Yue*, Maria Carolina Muniz*, Marcos F. Calegari Andrade, Linfeng Zhang, Roberto Car, and Athanassios Z. Panagiotopoulos, J. Chem. Phys. 154, 034111 (2021) […]

Post navigation

  • Previous post Proton-transfer dynamics in ionized water chains using real-time Time Dependent Density Functional Theory
  • Back to post list
  • Next post Hydrogen dynamics in supercritical water probed by neutron scattering and computer simulations

© 2023 Computational Chemical Science Center – All rights reserved

Powered by WP – Designed with the Customizr theme