@article{JiRagyanszkiFournier2023, author = {Ji, Hongchen and Ragyanszki, Anita and Fournier, Rene A.}, title = {Machine learning estimation of reaction energy barriers}, volume = {1229}, journal = {Computational and Theoretical Chemistry}, doi = {10.1016/j.comptc.2023.114332}, pages = {114332}, year = {2023}, abstract = {We describe a machine learning approach to approximate reaction energy barriers (E), requiring as input only estimates of geometry and energies of reactants and products. Using the dataset of Grambow, Pattanaik, and Green [Sci. Data 7 (1 3 7) (2020)] for reactions involving seven or fewer non-hydrogen atoms, 300 reaction features are computed, and an estimate of E is obtained by fitting a Kernel Ridge Regression (KRR) model with Laplacian kernel to a subset of Density Functional Theory reaction barriers. Our main interest is small energy barriers with the goal of modeling reactions in the interstellar medium and circumstellar envelope. We omitted reactions with E > 40 kcal mol-1 to obtain a subset of 5,276 reactions for 5-fold cross-validation. For this set, the KRR model predicts E with a mean absolute error of 4.13 kcal mol-1 and a root-mean square error of 6.02 kcal mol-1.}, language = {en} } @misc{GholamiSchintkeSchuettetal.2018, author = {Gholami, Masoud and Schintke, Florian and Sch{\"u}tt, Thorsten and Reinefeld, Alexander}, title = {Modeling Checkpoint Schedules for Concurrent HPC Applications}, journal = {CoSaS 2018 International Symposium on Computational Science at Scale}, year = {2018}, language = {en} } @masterthesis{Dillmann2013, type = {Bachelor Thesis}, author = {Dillmann, Johannes}, title = {Konsistentes Hinzuf{\"u}gen und Entfernen von Replikaten in XtreemFS}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-43109}, school = {Zuse Institute Berlin (ZIB)}, pages = {42}, year = {2013}, abstract = {In replizierten Systemen muss es m{\"o}glich sein ausgefallene Server zu ersetzen und Replikate dynamisch hinzuzuf{\"u}gen oder zu entfernen. Ver{\"a}nderungen an der Menge der eingesetzten Replikate k{\"o}nnen zu inkonsistenten Daten f{\"u}hren. In dieser Arbeit wird ein Algorithmus f{\"u}r das konsistente Hinzuf{\"u}gen und Entfernen von Replikaten f{\"u}r das verteilte Dateisystem XtreemFS vorgestellt. Der Algorithmus nutzt einen fehlertoleranten Service, der die Replikatliste speichert sowie Ver{\"a}nderungen koordiniert und diese aus globaler Sicht atomar ausf{\"u}hrt.}, language = {de} }