From 6fd90cb029475d7fb884bea7ee83f62aee0796a4 Mon Sep 17 00:00:00 2001
From: johannes wasmer <johannes.wasmer@gmail.com>
Date: Sun, 26 Jan 2025 18:27:53 +0100
Subject: [PATCH] update bibliography
---
bib/bibliography.bib | 13 +++++++++++++
1 file changed, 13 insertions(+)
diff --git a/bib/bibliography.bib b/bib/bibliography.bib
index c93c926..b1a3c08 100644
--- a/bib/bibliography.bib
+++ b/bib/bibliography.bib
@@ -20462,6 +20462,19 @@ Subject\_term\_id: electronic-properties-and-materials;quantum-hall;superconduct
keywords = {/unread,conference contribution,FZJ,Jij,ML,PGI,PGI-1/IAS-1}
}
+@unpublished{wasmerSiScLab20222022,
+ title = {{{SiSc Lab}} 2022, {{Project}} 6. {{A}} Machine Learning Playground in Quantum Mechanical Simulation.},
+ author = {Wasmer, Johannes and Chen, Po-Yen and Burdulea, Ilinca and A, Lixia and Rüßmann, Philipp and Blügel, Stefan and Naumann, Uwe},
+ date = {2022-11-01},
+ doi = {10.5281/zenodo.10440416},
+ url = {https://zenodo.org/records/10440416},
+ urldate = {2025-01-27},
+ abstract = {This is a copy of the practical course SiSc Laboratory (SiScLab; course homepage at the organizing chair) in the master program Simulation Sciences at RWTH Aachen University, in the term WS 2022/23, for project 6,~~~A machine learning playground in quantum mechanical simulation at Forschungszentrum Jülich, Institute of Advanced Simulation, division PGI-1/IAS-1 Quantum Theory of Materials, in the group Materials for quantum information technology. The course repository is here. The organization and meetings page is here. Project description. Density 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\textasciicircum 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},
+ langid = {english},
+ keywords = {/unread,atomistic machine learning,atomistic modeling,density functional theory,feature engineering,machine learning,machine learning potentials,magnetic interactions,property prediction},
+ file = {/Users/wasmer/Zotero/storage/CIJVT5BS/10440416.html}
+}
+
@online{weiGraphLearningIts2023,
title = {Graph {{Learning}} and {{Its Applications}}: {{A Holistic Survey}}},
shorttitle = {Graph {{Learning}} and {{Its Applications}}},
--
GitLab