001020056 001__ 1020056
001020056 005__ 20240226075240.0
001020056 037__ $$aFZJ-2023-05857
001020056 1001_ $$0P:(DE-Juel1)186072$$aWasmer, Johannes$$b0$$eCorresponding author$$ufzj
001020056 1112_ $$aRWTH Aachen University$$cAachen$$d2022-11-01 - 2023-03-01$$gsisclab2022-project6$$wGermany
001020056 245__ $$aSiSc Lab 2022, Project 6. A machine learning playground in quantum mechanical simulation.
001020056 260__ $$c2022
001020056 3367_ $$2DRIVER$$alecture
001020056 3367_ $$031$$2EndNote$$aGeneric
001020056 3367_ $$2BibTeX$$aMISC
001020056 3367_ $$0PUB:(DE-HGF)17$$2PUB:(DE-HGF)$$aLecture$$blecture$$mlecture$$s1704277352_16938$$xBlog
001020056 3367_ $$2ORCID$$aLECTURE_SPEECH
001020056 3367_ $$2DataCite$$aText
001020056 500__ $$ahttps://iffmd.fz-juelich.de/RhXA4J_JTwebjYKG35TbuQ
001020056 520__ $$aDensity functional theory (DFT) is one of the most widely used simulation techniques. About a third of world supercomputing time is spent each year on such calculations. DFT approximates the solution to the Schrödinger equation, to elucidate the electronic structure of materials and molecules. While it makes quantum many-body problems in a lot of systems of interest tractable, it still is computationally demanding. It typically scales in O(N^3) with the number of electrons in the system, limiting its application to systems with a few thousand atoms at most. Over the last 15 years, the development of surrogate models based on machine learning (ML) has steadily gained momentum in the field of atomistic simulation. In ab initio molecular dynamics for instance, machine-learned interatomic potentials at a fraction of the cost and comparable accuracy of mechanistic methods have already become mainstream. Now these surrogate models also start to increasingly be developed to predict the underlying electronic structure properties of atomic systems directly.In this project, the students will be given the chance to play around with a wide array of state-of-the-art models in this field, from traditional kernel methods to deep graph convolution networks. They will be provided with a computational infrastructure and training datasets from DFT calculations. The challenges ladder they will climb has the rungs a) understanding the electronic structure data, b) understanding the reasoning behind the various surrogate ML modeling approaches, c) discovering common features of the atomic systems to come up with clever optimizations of the model architectures, and d) achieving reasonable prediction accuracy for the targeted electronic structure properties. The project goals will be adjusted, in a reasonable range given the limited time frame, according to the speed of progress and the particular interests of the students.Expected prerequisites: Applied Quantum Mechanics, basic Python skills. Desired, but optional: Physics track, some hands-on ML experience.Advisors : Johannes Wasmer, Philipp Rüßmann , Stefan Blügel
001020056 536__ $$0G:(DE-HGF)POF4-5211$$a5211 - Topological Matter (POF4-521)$$cPOF4-521$$fPOF IV$$x0
001020056 536__ $$0G:(DE-Juel1)HDS-LEE-20190612$$aHDS LEE - Helmholtz School for Data Science in Life, Earth and Energy (HDS LEE) (HDS-LEE-20190612)$$cHDS-LEE-20190612$$x1
001020056 7001_ $$0P:(DE-Juel1)157882$$aRüssmann, Philipp$$b1$$ufzj
001020056 7001_ $$0P:(DE-HGF)0$$aChen, Po-Yen$$b2$$eContributor
001020056 7001_ $$0P:(DE-HGF)0$$aBurdulea, Ilinca$$b3$$eContributor
001020056 7001_ $$0P:(DE-HGF)0$$aA, Lixia$$b4$$eContributor
001020056 8564_ $$uhttps://iffgit.fz-juelich.de/phd-project-wasmer/teaching/sisclab2022-project6
001020056 909CO $$ooai:juser.fz-juelich.de:1020056$$pVDB
001020056 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)186072$$aForschungszentrum Jülich$$b0$$kFZJ
001020056 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)157882$$aForschungszentrum Jülich$$b1$$kFZJ
001020056 9131_ $$0G:(DE-HGF)POF4-521$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5211$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vQuantum Materials$$x0
001020056 9141_ $$y2023
001020056 920__ $$lyes
001020056 9201_ $$0I:(DE-Juel1)IAS-1-20090406$$kIAS-1$$lQuanten-Theorie der Materialien$$x0
001020056 9201_ $$0I:(DE-Juel1)PGI-1-20110106$$kPGI-1$$lQuanten-Theorie der Materialien$$x1
001020056 980__ $$alecture
001020056 980__ $$aVDB
001020056 980__ $$aI:(DE-Juel1)IAS-1-20090406
001020056 980__ $$aI:(DE-Juel1)PGI-1-20110106
001020056 980__ $$aUNRESTRICTED