CONFERENCE PROCEEDINGS
Advanced Search
Home > Proceedings > Volume 12950 > Article
Presentation + Paper
9 May 2024 GPU-accelerated data-driven framework of hybrid ReaxFF
Xing Quan Wang, Denvid Lau
Author Affiliations +
Proceedings Volume 12950, Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XVIII; 129500D (2024) https://doi.org/10.1117/12.3012034
Event: SPIE Smart Structures + Nondestructive Evaluation, 2024, Long Beach, California, United States
Abstract
In computational materials science, simulation techniques must balance bridging time and length scales with maintaining high accuracy at a reasonable computational cost. In this study, a data-driven parametrization method through machine learning algorithms is proposed. The proposed method aims to decrease the time required for parameter optimization, enhancing efficiency of ReaxFF potential. This innovative approach employs a combination of reactive and non-reactive molecular dynamics simulations to simulate phenomena that demand extended time scales or involve larger systems beyond the conventional capabilities of ReaxFF. ML algorithms are utilized between the reactive and non-reactive stages to forecast non-reactive forcefield parameters by considering the updated bond topology of the system. The proposed algorithm can be accelerated on the GPU, achieving optimized management of the computing power and memory requirements imposed by ReaxFF MD on computer hardware. This study is envisioned to promote the application of ReaxFF in large and complex material systems which aims to provide more efficient and accurate predictions compared to traditional ReaxFF.
Conference Presentation
© (2024) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Xing Quan Wang and Denvid Lau "GPU-accelerated data-driven framework of hybrid ReaxFF", Proc. SPIE 12950, Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XVIII, 129500D (9 May 2024); https://doi.org/10.1117/12.3012034
ACCESS THE FULL ARTICLE
ORGANIZATIONAL
Sign in with credentials provided by your organization.
INSTITUTIONAL
Select your institution to access the SPIE Digital Library.
SELECT YOUR INSTITUTION
PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
PERSONAL SIGN IN
No SPIE Account? Create one
PURCHASE THIS CONTENT
SUBSCRIBE TO DIGITAL LIBRARY
50 downloads per 1-year subscription
Members: $195
Non-members: $335 ADD TO CART
25 downloads per 1 - year subscription
Members: $145
Non-members: $250 ADD TO CART
PURCHASE SINGLE ARTICLE
Includes PDF, HTML & Video, when available
Members: $17.00
Non-members: $21.00 ADD TO CART
PROCEEDINGS
 6 PAGES + PRESENTATION
DOWNLOAD PAPER SAVE TO MY LIBRARY
WATCH
PRESENTATION
GET CITATION
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Computer simulations
Data modeling
Chemical reactions
Molecular interactions
Detection and tracking algorithms
Molecules
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top