2026-09-18T17:25:05Zhttps://dspace.mit.edu/server/oai/request

oai:dspace.mit.edu:1721.1/1541942024-04-18T03:55:25Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023

Gómez-Bombarelli, Rafael Orlova, Yuliia Massachusetts Institute of Technology. Center for Computational Science and Engineering 2024-04-17T21:10:34Z 2024-04-17T21:10:34Z 2023-09 2023-10-11T18:01:51.643Z https://hdl.handle.net/1721.1/154194 Silica precipitation is a subject of big interest since it occurs in a wide variety of environmental and industrial processes. Even though there are many advances in atomistic simulation research of different forms of silica, the mechanism of silica precipitation has not been fully understood. We propose to study the following process using reactive force-field method (ReaxFF). Despite being a classical force field, ReaxFF can achieve quantum chemical accuracy once the optimal potential coefficients are found. However, the fitting of ReaxFF parameters is a challenge due to the complex functional form of the potential. Several techniques have been proposed to solve this problem, such as evolutionary algorithms, Monte Carlo methods, and simulated annealing. The stochastic nature of these methods requires millions of error evaluations to fit the parameters, which results in excessive optimization times. Recent advances in machine learning made it possible to drastically speed up the process by utilizing the gradient of the potential. In this work, the gradient-based optimization of reactive force-field parameters using Pytorch was performed. We have implemented ReaxFF potential as a Pytorch model. The model’s performance was validated against existing ReaxFF implementations. ReaxFF parameters were fitted to the dataset, which comprised 15345 geometries calculated using a long-range corrected hybrid functional 𝜔B97XD3. S.M. Massachusetts Institute of Technology In Copyright - Educational Use Permitted Copyright MIT http://rightsstatements.org/page/InC-EDU/1.0/ Gradient-Based Optimization of ReaxFF Parameters Using Pytorch for the Study of Silica Precipitation Thesis application/pdf Master Master of Science in Computational Science and Engineering Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="2de85960-16d4-413d-be24-454c573c9974"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Title>Gradient-Based Optimization of ReaxFF Parameters Using Pytorch for the Study of Silica Precipitation</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2023-09</PublicationDate> <Authors> <Author> <DisplayName>Orlova, Yuliia</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://rightsstatements.org/page/InC-EDU/1.0/</License> <Abstract>Silica precipitation is a subject of big interest since it occurs in a wide variety of environmental and industrial processes. Even though there are many advances in atomistic simulation research of different forms of silica, the mechanism of silica precipitation has not been fully understood. We propose to study the following process using reactive force-field method (ReaxFF). Despite being a classical force field, ReaxFF can achieve quantum chemical accuracy once the optimal potential coefficients are found. However, the fitting of ReaxFF parameters is a challenge due to the complex functional form of the potential. Several techniques have been proposed to solve this problem, such as evolutionary algorithms, Monte Carlo methods, and simulated annealing. The stochastic nature of these methods requires millions of error evaluations to fit the parameters, which results in excessive optimization times. Recent advances in machine learning made it possible to drastically speed up the process by utilizing the gradient of the potential. In this work, the gradient-based optimization of reactive force-field parameters using Pytorch was performed. We have implemented ReaxFF potential as a Pytorch model. The model’s performance was validated against existing ReaxFF implementations. ReaxFF parameters were fitted to the dataset, which comprised 15345 geometries calculated using a long-range corrected hybrid functional 𝜔B97XD3.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>