Development of a new parameter optimization scheme for a reactive force field based on a machine learning approach

2019 ◽  
Author(s):  
Hiroya Nakata ◽  
Shandan Bai
Keyword(s):  
Machine Learning ◽  
Force Field ◽  
Parameter Optimization ◽  
Learning Approach ◽  
Reactive Force ◽  
Optimization Scheme ◽  
Reactive Force Field ◽  
Machine Learning Approach

Machine Learning Potential: A Combination of Reactive Force Field and Message Passing Neural Networks

2021 ◽  
Author(s):  
Liyuan Xue ◽  
Feng Guo ◽  
Yushi Wen ◽  
Shiquan Feng ◽  
Xiaona Huang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Force Field ◽  
Material Properties ◽  
Message Passing ◽  
Field Model ◽  
Reactive Force ◽  
Computational Tool ◽  
Learning Potential ◽  
Reactive Force Field

The Reactive Force Field (ReaxFF) is a powerful computational tool for exploring material properties. In this work, we proposed an enhanced reactive force field model, which using Message Passing Neural...

Intelligent-ReaxFF: Evaluating the reactive force field parameters with machine learning

2020 ◽  
Vol 172 ◽  
pp. 109393 ◽  
Author(s):  
Feng Guo ◽  
Yu-Shi Wen ◽  
Shi-Quan Feng ◽  
Xiao-Dong Li ◽  
Heng-Shuai Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Force Field ◽  
Reactive Force ◽  
Reactive Force Field ◽  
Force Field Parameters

1552-P: Machine Learning Approach to Decision-Making for Initial Insulin Use in Japanese Patients with Type 2 Diabetes

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1552-P
Author(s):  
KAZUYA FUJIHARA ◽  
MAYUKO H. YAMADA ◽  
YASUHIRO MATSUBAYASHI ◽  
MASAHIKO YAMAMOTO ◽  
TOSHIHIRO IIZUKA ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Decision Making ◽  
Japanese Patients ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Insulin Use
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