.png){kind=icon}
.png){kind=icon}
Return
INDEEDopt: a deep learning-based ReaxFF parameterization framework
.png){kind=icon}
OA
AI
DOI：10.1038/s41524-021-00534-4.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Empirical interatomic potentials require optimization of force field parameters to tune interatomic interactions to mimic ones obtained by quantum chemistry-based methods. The optimization of the parameters is complex and requires the development of new techniques. Here, we propose an INitial-DEsign Enhanced Deep learning-based OPTimization (INDEEDopt) framework to accelerate and improve the quality of the ReaxFF parameterization. The procedure starts with a Latin Hypercube Design (LHD) algorithm that is used to explore the parameter landscape extensively. The LHD passes the information about explored regions to a deep learning model, which finds the minimum discrepancy regions and eliminates unfeasible regions, and constructs a more comprehensive understanding of physically meaningful parameter space. We demonstrate the procedure here for the parameterization of a nickel-chromium binary force field and a tungsten-sulfide-carbon-oxygen-hydrogen quinary force field. We show that INDEEDopt produces improved accuracies in shorter development time compared to the conventional optimization method.
Keywords:
REACTIVE FORCE-FIELD
GLOBAL OPTIMIZATION
ENERGY
OXIDATION
STRENGTH
MODELS
NICKEL
FUEL
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
11.9
Papers:
2.4K
Citations:
1.7W
View more organizations