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Activity Number: 348 - Statistical Engineering and Applications in Physical Sciences
Type: Contributed
Date/Time: Wednesday, August 5, 2020 : 10:00 AM to 2:00 PM
Sponsor: Section on Physical and Engineering Sciences
Abstract #313832
Title: Optimization of ReaxFF Parameters
Author(s): Yao Song* and Ying Hung and Tirthankar Dasgupta
Companies: and Rutgers University and Rutgers University
Keywords: ReaxFF force field; multi-objective optimization; machine learning; statistical thinking

The ReaxFF incorporates complex functions with associated parameters in order to describe the inter and intra-atomic interactions in materials systems. A typical ReaxFF force field consists of approximately a hundred parameters per element type. During the development of a force field for a molecular system of interest, these parameters are optimized to reproduce reference values with reasonable accuracy, which is a multi-objective optimization problem. A standard approach is to reduce multi-objective optimization problem  to a one-dimensional optimization problem by defining the objective function in the certain form. However, the optimization problem is non-trivial and challenging due to the high dimensionality of the input space and the difficulty of fitting the objective function well with a surrogate model. In this talk, we propose an approach that is based on a combination of modern machine learning approaches and careful ``statistical thinking'' to achieve the target. 

Authors who are presenting talks have a * after their name.

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