Sökning: id:"swepub:oai:DiVA.org:kau-86341" >
Curvature Constrain...
-
Ammothum Kandy, Akshay KrishnaUppsala universitet,Strukturkemi
(författare)
Curvature Constrained Splines for DFTB Repulsive Potential Parametrization
- Artikel/kapitelEngelska2021
Förlag, utgivningsår, omfång ...
-
2021-02-19
-
American Chemical Society (ACS),2021
-
printrdacarrier
Nummerbeteckningar
-
LIBRIS-ID:oai:DiVA.org:kau-86341
-
https://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-86341URI
-
https://doi.org/10.1021/acs.jctc.0c01156DOI
-
https://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-181552URI
-
https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-434543URI
Kompletterande språkuppgifter
-
Språk:engelska
-
Sammanfattning på:engelska
Ingår i deldatabas
Klassifikation
-
Ämneskategori:ref swepub-contenttype
-
Ämneskategori:art swepub-publicationtype
Anmärkningar
-
The Curvature Constrained Splines (CCS) methodology has been used for fitting repulsive potentials to be used in SCC-DFTB calculations. The benefit of using CCS is that the actual fitting of the repulsive potential is performed through quadratic programming on a convex objective function. This guarantees a unique (for strictly convex) and optimum two-body repulsive potential in a single shot, thereby making the parametrization process robust, and with minimal human effort. Furthermore, the constraints in CCS give the user control to tune the shape of the repulsive potential based on prior knowledge about the system in question. Herein, we developed the method further with new constraints and the capability to handle sparse data. We used the method to generate accurate repulsive potentials for bulk Si polymorphs and demonstrate that for a given Slater-Koster table, which reproduces the experimental band structure for bulk Si in its ground state, we are unable to find one single two-body repulsive potential that can accurately describe the various bulk polymorphs of silicon in our training set. We further demonstrate that to increase transferability, the repulsive potential needs to be adjusted to account for changes in the chemical environment, here expressed in the form of a coordination number. By training a near-sighted Atomistic Neural Network potential, which includes many-body effects but still essentially within the first-neighbor shell, we can obtain full transferability for SCC-DFTB in terms of describing the energetics of different Si polymorphs.
Ämnesord och genrebeteckningar
Biuppslag (personer, institutioner, konferenser, titlar ...)
-
Wadbro, Eddie,1981-Umeå universitet,Institutionen för datavetenskap,Umeå universitet, Institutionen för datavetenskap(Swepub:umu)edwa0002
(författare)
-
Aradi, BálintBremen Center for Computational Materials Science, University of Bremen, Am Fallturm 1, Bremen, Germany
(författare)
-
Broqvist, PeterUppsala universitet,Strukturkemi(Swepub:uu)petbr908
(författare)
-
Kullgren, Jolla,1978-Uppsala universitet,Strukturkemi(Swepub:uu)jolku587
(författare)
-
Uppsala universitetStrukturkemi
(creator_code:org_t)
Sammanhörande titlar
-
Ingår i:Journal of Chemical Theory and Computation: American Chemical Society (ACS)17:3, s. 1771-17811549-96181549-9626
Internetlänk
Hitta via bibliotek
Till lärosätets databas