| dc.contributor.advisor | Behler, Jörg, Dr. | |
| dc.contributor.author | Herbold, Marius | |
| dc.date.accessioned | 2023-02-24T14:17:17Z | |
| dc.date.available | 2023-03-03T00:50:09Z | |
| dc.date.issued | 2023-02-24 | |
| dc.identifier.uri | http://resolver.sub.uni-goettingen.de/purl?ediss-11858/14541 | |
| dc.identifier.uri | http://dx.doi.org/10.53846/goediss-9735 | |
| dc.format.extent | 172 Seiten | de |
| dc.language.iso | eng | de |
| dc.rights | Attribution-NonCommercial-ShareAlike 4.0 International | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | * |
| dc.subject.ddc | 540 | de |
| dc.title | A Fragment-Based Construction of a Neural Network Potential for Metal-Organic Frameworks | de |
| dc.type | doctoralThesis | de |
| dc.contributor.referee | Behler, Jörg, Dr. | |
| dc.date.examination | 2022-09-19 | de |
| dc.description.abstracteng | In recent years, many types of machine learning potentials (MLPs) have been developed,
which are used to represent the high-dimensional potential-energy surface (PES) of a chemical
system with similar accuracy as electronic structure methods. Commonly used MLPs
rely on atomic energy contributions dependent on the local chemical environments. Frequently,
in addition to the total energies, also atomic forces are used to construct the
potentials, as these provide detailed local information about the PES. Since many systems
are too large for electronic structure calculations, the MLP training is based on
smaller subsystems like molecular fragments or clusters, providing reliable reference forces.
Additionally this procedure can substantially simplify the construction of the training sets.
In this work, a well-defined method is proposed to determine structurally converged molecular
fragments providing reliable training forces for high-dimensional neural network potentials
(HDNNPs) based on the analysis of the Hessian. The Hessian permits the investigation
of the atomic force dependency on the local environment and thus, the method
serves as a locality test and allows to estimate the importance of long-range interactions.
The procedure is illustrated for a series of simple, quasi-one-dimensional molecular model
systems and the metal-organic frameworks IRMOF-1 (commonly known as MOF-5), -10
and-16 as examples for complex organic-inorganic hybrid materials. A fragment radius is
dervied to construct size-converged molecular fragments as the foundation of a HDNNP
data set.
In the formalism of the HDNNP, the atomic force components depend on twice the cutoff
radius compared to the atomic energy contributions. Because of this relation another
set of size-reduced molecular fragment is derived to construct another HDNNP data set.
Both data sets can be represented with similar accuracy. The validation of the resulting
HDNNPs illustrates the equivalence of the predictions. Consequently, very efficient small
molecular fragments are proposed for the construction of HDNNP data set. | de |
| dc.contributor.coReferee | Mata, Ricardo, Dr. | |
| dc.subject.eng | High-Dimensional Neural Network Potential | de |
| dc.subject.eng | HDNNP | de |
| dc.subject.eng | IRMOF | de |
| dc.subject.eng | Metal-Organic-Framework | de |
| dc.subject.eng | MOF | de |
| dc.subject.eng | Fragment-Based | de |
| dc.subject.eng | iso-reticular | de |
| dc.subject.eng | Hessian-Based Assessment | de |
| dc.subject.eng | Cutoff Radius | de |
| dc.subject.eng | Atomic Force Convergence | de |
| dc.identifier.urn | urn:nbn:de:gbv:7-ediss-14541-1 | |
| dc.affiliation.institute | Fakultät für Chemie | de |
| dc.subject.gokfull | Chemie (PPN62138352X) | de |
| dc.description.embargoed | 2023-03-03 | de |
| dc.identifier.ppn | 1837640319 | |
| dc.notes.confirmationsent | Confirmation sent 2023-02-24T14:45:01 | de |