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Parameterization of empirical forcefields for glassy silica using machine learning

Published online by Cambridge University Press:  23 May 2019

Han Liu ,
Zipeng Fu ,
Yipeng Li ,
Nazreen Farina Ahmad Sabri  and
Mathieu Bauchy
Han Liu
Affiliation:
Physics of AmoRphous and Inorganic Solids Laboratory (PARISlab), Department of Civil and Environmental Engineering, University of California, Los Angeles, CA 90095, USA
Zipeng Fu
Affiliation:
Physics of AmoRphous and Inorganic Solids Laboratory (PARISlab), Department of Civil and Environmental Engineering, University of California, Los Angeles, CA 90095, USA Department of Computer Science, University of California, Los Angeles, CA 90095, USA
Yipeng Li
Affiliation:
Physics of AmoRphous and Inorganic Solids Laboratory (PARISlab), Department of Civil and Environmental Engineering, University of California, Los Angeles, CA 90095, USA
Nazreen Farina Ahmad Sabri
Affiliation:
Physics of AmoRphous and Inorganic Solids Laboratory (PARISlab), Department of Civil and Environmental Engineering, University of California, Los Angeles, CA 90095, USA
Mathieu Bauchy*
Affiliation:
Physics of AmoRphous and Inorganic Solids Laboratory (PARISlab), Department of Civil and Environmental Engineering, University of California, Los Angeles, CA 90095, USA
*
Address all correspondence to Mathieu Bauchy at [email protected]
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Abstract

The development of reliable, yet computationally efficient interatomic forcefields is key to facilitate the modeling of glasses. However, the parameterization of novel forcefields is challenging as the high number of parameters renders traditional optimization methods inefficient or subject to bias. Here, we present a new parameterization method based on machine learning, which combines ab initio molecular dynamics simulations and Bayesian optimization. By taking the example of glassy silica, we show that our method yields a new interatomic forcefield that offers an unprecedented agreement with ab initio simulations. This method offers a new route to efficiently parameterize new interatomic forcefields for disordered solids in a non-biased fashion.

Type
Artificial Intelligence Research Letters
Information
MRS Communications , Volume 9 , Issue 2 , June 2019 , pp. 593 - 599
Copyright
Copyright © Materials Research Society 2019 

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