INDEEDopt: a deep learning-based ReaxFF parameterization framework

Abstract 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 INiti...

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Autores principales:	Mert Y. Sengul, Yao Song, Nadire Nayir, Yawei Gao, Ying Hung, Tirthankar Dasgupta, Adri C. T. van Duin
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/e38e18c02dd2449aa860d83c0501a7d3
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spelling	oai:doaj.org-article:e38e18c02dd2449aa860d83c0501a7d32021-12-02T15:45:30ZINDEEDopt: a deep learning-based ReaxFF parameterization framework10.1038/s41524-021-00534-42057-3960https://doaj.org/article/e38e18c02dd2449aa860d83c0501a7d32021-05-01T00:00:00Zhttps://doi.org/10.1038/s41524-021-00534-4https://doaj.org/toc/2057-3960Abstract 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.Mert Y. SengulYao SongNadire NayirYawei GaoYing HungTirthankar DasguptaAdri C. T. van DuinNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492Computer softwareQA76.75-76.765ENnpj Computational Materials, Vol 7, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
spellingShingle	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765 Mert Y. Sengul Yao Song Nadire Nayir Yawei Gao Ying Hung Tirthankar Dasgupta Adri C. T. van Duin INDEEDopt: a deep learning-based ReaxFF parameterization framework
description	Abstract 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.
format	article
author	Mert Y. Sengul Yao Song Nadire Nayir Yawei Gao Ying Hung Tirthankar Dasgupta Adri C. T. van Duin
author_facet	Mert Y. Sengul Yao Song Nadire Nayir Yawei Gao Ying Hung Tirthankar Dasgupta Adri C. T. van Duin
author_sort	Mert Y. Sengul
title	INDEEDopt: a deep learning-based ReaxFF parameterization framework
title_short	INDEEDopt: a deep learning-based ReaxFF parameterization framework
title_full	INDEEDopt: a deep learning-based ReaxFF parameterization framework
title_fullStr	INDEEDopt: a deep learning-based ReaxFF parameterization framework
title_full_unstemmed	INDEEDopt: a deep learning-based ReaxFF parameterization framework
title_sort	indeedopt: a deep learning-based reaxff parameterization framework
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/e38e18c02dd2449aa860d83c0501a7d3
work_keys_str_mv	AT mertysengul indeedoptadeeplearningbasedreaxffparameterizationframework AT yaosong indeedoptadeeplearningbasedreaxffparameterizationframework AT nadirenayir indeedoptadeeplearningbasedreaxffparameterizationframework AT yaweigao indeedoptadeeplearningbasedreaxffparameterizationframework AT yinghung indeedoptadeeplearningbasedreaxffparameterizationframework AT tirthankardasgupta indeedoptadeeplearningbasedreaxffparameterizationframework AT adrictvanduin indeedoptadeeplearningbasedreaxffparameterizationframework
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INDEEDopt: a deep learning-based ReaxFF parameterization framework

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