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# # put this line in comment when presentation is finished
# # (no need to clutter repo history with intermediate slides PDFs)
**/presentation.pdf
# **/presentation.pdf
# emacs gitignore (_region_: auctex preview files)
*.el
......
presentation/backmatter/discussion.tex
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......@@ -11,7 +11,7 @@
% KKR theory slide from mavropoulous dft20 kkr lecture; backup slides
{
\setbeamercolor{background canvas}{bg=}
\includepdf[pages={15,17,19,27-28,30-31}]{../resources/fig/external/presentations/rwth-dft20/lecture-20-kkr/Lecture20_KKRGF-Method.pdf}
\includepdf[pages={15,17,19,27-28,29,31}]{../resources/fig/external/presentations/rwth-dft20/lecture-20-kkr/Lecture20_KKRGF-Method.pdf}
}
......
presentation/frontmatter/slides-repo.tex
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......@@ -3,7 +3,7 @@
% \frametitle{Slides metadata}
\begin{center}
Talk held at hzdr-casus group meeting for ongoing collaboration project via
Talk held at HZDR-CASUS \href{https://sites.google.com/view/mlmd}{MLMD} group meeting for ongoing collaboration project via
HIDA Trainee Network.
\vspace*{2em}
......
presentation/mainmatter/introduction.tex
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......@@ -48,6 +48,76 @@
\end{columns}
\end{frame}
% Slide funding % Section introduction
\begin{frame}[c,plain]
\frametitle{My funding}
% \framesubtitle{HIDA, HDSLEE, AIDAS, DAEMON}
\vspace*{0em}
\begin{columns}[c]
\begin{column}{0.3\linewidth}
\textbf{\color{fzjblue}{This research visit}}
{\small HIDA Trainee Network}
\vspace*{1em}
\begin{center}
\hspace*{-2em}\includegraphics[width=0.8\textwidth]{../resources/fig/logos/hida/HiDA_Logo_RGB_kompakt-cropped.png}\vspace*{1em}
% \hspace*{-2em}\includegraphics[width=0.8\textwidth]{../resources/fig/logos/hds-lee/adapted/HDS_LEE_Logo_Solo_RGB_cropped.pdf}\vspace*{1em}
% \hspace*{-2em}\includegraphics[width=0.8\textwidth]{../resources/fig/logos/hds-lee/adapted/HDS_LEE_Logo_Text_RGB_cropped.pdf}
\end{center}
\vspace*{1em}\hrule{}\vspace*{2em}
\textbf{\color{fzjblue}{My PhD}}
{\small HIDA Grad School HDSLEE \&}
{\small EU Joint Virtual Lab}
\vspace*{1em}
\includegraphics[width=0.8\textwidth]{../resources/fig/logos/aidas/AIDAS-72dpi-no-bg.png}%
% {\color{fzjblue}{on Artificial Intelligence, Data Analytics and Scalable Simulation}}
\end{column}
\vrule{}\hspace*{2em}
\begin{column}{0.6\linewidth}
% left side. DAEMON Network
\begin{columns}[c]
\begin{column}{0.35\linewidth}
\vspace*{3em}
\begin{center}
% \includegraphics[width=0.7\textwidth]{../resources/fig/github/best-of-aml-thumbnail-2023-09-05.png}%
\includegraphics[width=1.0\textwidth]{../resources/fig/cost-daemon/daemon-working-groups-circle.png}%
\vspace*{1em}
Join via \\\large{\href{https://cost-daemon.eu/}{cost-daemon.eu}}
\end{center}
\end{column}
\begin{column}{0.6\linewidth}
\begin{center}
\includegraphics[width=0.45\textwidth]{../resources/fig/logos/cost-daemon/cost-daemon-logo-2025.png}%
\hspace*{1em}
\includegraphics[width=0.3\textwidth]{../resources/fig/logos/cost-eu/cost-eu-logo-no-bg.png}%
\end{center}
\emph{European network for data-driven materials science}\vspace*{1em}
{\footnotesize
\begin{itemize}
% \setlength\itemsep{1em}
\item[\textbf{\faPlay{} WG1:}] Community standards: data, workflows and codes for materials design.
\item[\textbf{\faPlay{} WG2:}] Representations and algorithms for materials design for “single-modality” use.
\item[\textbf{WG3:}] Multi-modal machine learning methods for advanced materials design.
\item[\textbf{WG4:}] Process-structure-property relationships in materials. Novel insights and applications.
\item[\textbf{WG5:}] Training, Dissemination, Exploitation, Outreach
\end{itemize}
}
\end{column}
\end{columns}
\end{column}
\end{columns}
\end{frame}
% Slide my research group % Section introduction
......@@ -155,83 +225,25 @@
\end{columns}
\vspace*{0em}
{\large \faGithub{} \href{https://github.com/JuDFTteam}{JuDFTteam}}
{\large \href{http://judft.de}{judft.de} \quad \textbf{//} \quad \faGithub{} \href{https://github.com/JuDFTteam}{JuDFTteam}}
% {\large \faGithub{} \href{https://github.com/JuDFTteam}{JuDFTteam}}
\end{center}
\end{frame}
% Slide funding % Section introduction
% Slide all-electron DFT % Section phd-project
\begin{frame}[c,plain]
\frametitle{My funding}
% \framesubtitle{HIDA, HDSLEE, AIDAS, DAEMON}
\vspace*{0em}
\begin{columns}[c]
\begin{column}{0.3\linewidth}
\textbf{\color{fzjblue}{This research visit}}
{\small HIDA Trainee Network}
\vspace*{1em}
\begin{center}
\hspace*{-2em}\includegraphics[width=0.8\textwidth]{../resources/fig/logos/hida/HiDA_Logo_RGB_kompakt-cropped.png}\vspace*{1em}
% \hspace*{-2em}\includegraphics[width=0.8\textwidth]{../resources/fig/logos/hds-lee/adapted/HDS_LEE_Logo_Solo_RGB_cropped.pdf}\vspace*{1em}
% \hspace*{-2em}\includegraphics[width=0.8\textwidth]{../resources/fig/logos/hds-lee/adapted/HDS_LEE_Logo_Text_RGB_cropped.pdf}
\end{center}
\vspace*{1em}\hrule{}\vspace*{2em}
\textbf{\color{fzjblue}{My PhD}}
{\small HIDA Grad School HDSLEE \&}
{\small EU Joint Virtual Lab}
\vspace*{1em}
\frametitle{JuDFT codes accuracy}
% frametitle notes: PhD project flowchart
\includegraphics[width=0.8\textwidth]{../resources/fig/logos/aidas/AIDAS-72dpi-no-bg.png}%
% {\color{fzjblue}{on Artificial Intelligence, Data Analytics and Scalable Simulation}}
FLEUR and JuKKR are all-electron, full-potential open-source codes.
\vspace*{0em}
\end{column}
\vrule{}\hspace*{2em}
\begin{column}{0.6\linewidth}
% left side. DAEMON Network
\begin{columns}[c]
\begin{column}{0.35\linewidth}
\vspace*{3em}
\begin{center}
% \includegraphics[width=0.7\textwidth]{../resources/fig/github/best-of-aml-thumbnail-2023-09-05.png}%
\includegraphics[width=1.0\textwidth]{../resources/fig/cost-daemon/daemon-working-groups-circle.png}%
\vspace*{1em}
Join via \\\large{\href{https://cost-daemon.eu/}{cost-daemon.eu}}
\end{center}
\end{column}
\includegraphics[width=0.8\textwidth]{../resources/fig/external/papers/bosoniHowVerifyPrecision2024/fig4-clip1.pdf}\footcite{bosoniHowVerifyPrecision2024}
\begin{column}{0.6\linewidth}
\begin{center}
\includegraphics[width=0.45\textwidth]{../resources/fig/logos/cost-daemon/cost-daemon-logo-2025.png}%
\hspace*{1em}
\includegraphics[width=0.3\textwidth]{../resources/fig/logos/cost-eu/cost-eu-logo-no-bg.png}%
\end{center}
\emph{European network for data-driven materials science}\vspace*{1em}
{\footnotesize
\begin{itemize}
% \setlength\itemsep{1em}
\item[\textbf{\faPlay{} WG1:}] Community standards: data, workflows and codes for materials design.
\item[\textbf{\faPlay{} WG2:}] Representations and algorithms for materials design for “single-modality” use.
\item[\textbf{WG3:}] Multi-modal machine learning methods for advanced materials design.
\item[\textbf{WG4:}] Process-structure-property relationships in materials. Novel insights and applications.
\item[\textbf{WG5:}] Training, Dissemination, Exploitation, Outreach
\end{itemize}
}
\end{column}
\end{columns}
\end{column}
\end{columns}
\vspace*{0em}
\end{frame}
% Slide AiiDA engine % Section introduction
\begin{frame}[plain,c]
% \frametitle{{\small AiiDA}}
......@@ -281,45 +293,6 @@
\end{frame}
% Slide PhD project flowchart % Section phd-project
\begin{frame}
\frametitle{My PhD project}
% frametitle notes: PhD project flowchart
\framesubtitle{\href{https://go.fzj.de/wasmer}{go.fzj.de/wasmer}}
\vspace*{0em}
\includegraphics[width=1.0\textwidth]{../resources/fig/presentation-2023-02/atomistic-ml/classification-of-atomistic-ml_presentation-2023-02_02-emph-both_ktikz.pdf}
\vspace*{2em}
\begin{columns}[t]
\hspace{1em}
\begin{column}{0.6\linewidth}
\begin{center}
Electronic structure emulator
for fast SCF convergence
\end{center}
\end{column}
\vrule{}
\hspace{1em}
\begin{column}{0.39\linewidth}
\begin{center}
Magnetic property prediction
for spin dynamics simulation
\end{center}
\end{column}
\end{columns}
\end{frame}
% Slide MTIs (magnetic topological insulators) % Section co-doping
{
\setbeamercolor{background canvas}{bg=}
\includepdf[pages=1]{../resources/fig/presentation-2023-03/ruess/ruess-TIs.pdf}
}
%%% Local Variables:
%%% mode: latex
%%% TeX-master: "../presentation"
......
presentation/mainmatter/jij-prediction.tex 0 → 100644
View file @ 03e3610a
\section{Machine learning exchange interactions}
\label{sec:ml-exc-inter}
% Slide PhD project flowchart % Section phd-project
\begin{frame}
\frametitle{Vision: Electronic structure learning}
% frametitle notes: PhD project flowchart
\framesubtitle{as integrated, high-level multiscale workflows}
\vspace*{0em}
\includegraphics[width=1.0\textwidth]{../resources/fig/presentation-2023-02/atomistic-ml/classification-of-atomistic-ml_presentation-2023-02_02-emph-both_ktikz.pdf}
\vspace*{2em}
\begin{columns}[t]
\hspace{1em}
\begin{column}{0.6\linewidth}
\begin{center}
Better \enquote{initial guess}
for fast SCF convergence
\end{center}
\end{column}
\vrule{}
\hspace{1em}
\begin{column}{0.39\linewidth}
\begin{center}
Magnetic property prediction
(ML-Exc) for spin dynamics
\end{center}
\end{column}
\end{columns}
\end{frame}
% Slide MTIs (magnetic topological insulators) % Section co-doping
{
\setbeamercolor{background canvas}{bg=}
\includepdf[pages=1]{../resources/fig/presentation-2023-03/ruess/ruess-TIs.pdf}
}
% Slide study design % Section co-doping
\begin{frame}[plain,c]
\frametitle{Project \enquote{ML-Exc}}
\framesubtitle{Magnetic co-doping of topological insulators}
\hspace*{1em} \ce{Bi2Te3} \hspace*{3em} Dimer clusters of \(3d\), \(4d\) transition
metal defects\vspace*{-1em}
\begin{columns}[c]
\hspace*{1em}
\begin{column}{0.6\textwidth}
\begin{center}
\includegraphics[width=0.35\textheight]{../resources/fig/external/papers/mozumderHighthroughputMagneticCodoping2024/processed/Fig1-a.pdf}%
\hspace*{-0.5em}
\includegraphics[width=0.80\textwidth]{../resources/fig/external/papers/mozumderHighthroughputMagneticCodoping2024/processed/FigA3-extract-6.pdf}%
\end{center}
\end{column}
\hspace*{3em}
\begin{column}{0.4\textwidth}
{\small
Single-impurity dabase, N=2'000.
\href{https://go.fzj.de/judit}{go.fzj.de/judit}\vspace*{1em}
Dimer database, N=2'000\footcite{mozumderHighthroughputMagneticCodoping2024}.\vspace*{3em}
Co-doping can help to control
\begin{itemize}
\item critical \(T_c\) of QAHE
\item exchange splitting \(\Delta_{xc}\)
\item long-range magnetic ordering
\end{itemize}\vspace*{1em}
for applications in spintronics and
fault-tolerant quantum computing.
}
\end{column}
\end{columns}
\end{frame}
% Slide AiiDA-KKR workflows % Section co-doping
\begin{frame}[plain,c]
\frametitle{Project \enquote{ML-Exc}}
\framesubtitle{\logoAiida{}-KKR workflows\footcite{russmannAiiDAKKRPluginIts2021}}
\vspace*{-1em}
\begin{columns}[t]
\hspace*{-2em}
\begin{column}{0.3\linewidth}
% {\footnotesize Single impurity}
\begin{center}
\includegraphics[width=0.8\linewidth]{../resources/fig/presentation-2023-03/ruess/ruess-aiida-kkr-paper-workflow-c.pdf}
\end{center}
\end{column}
\begin{column}{0.5\linewidth}
\hspace*{-3em}
\begin{center}
% {\footnotesize \(N+1\) impurities}
\includegraphics[width=0.8\linewidth]{../resources/fig/external/papers/mozumderHighthroughputMagneticCodoping2024/processed/FigA2-no-subfigure-labels.pdf}%
\end{center}
\end{column}
\end{columns}
\vspace*{-0em}
\begin{flushright}
Extended Heisenberg Hamiltonian.
\(H = -\frac{1}{2}\sum_{i,j}J_{ij} \, \vec{S}_i \cdot \vec{S}_j
-\frac{1}{2}\sum_{i,j}\vec{D}_{ij} \cdot \left( \vec{S}_i \times \vec{S}_j \right)
\)
Exchange constants from method of infinitesimal rotations\footcite{liechtensteinLocalSpinDensity1987}.
\( % Liechtenstein infinitesimal roation Jij from KKR-GF
\mathcal{J}_{ij} = -\frac{1}{\pi} \operatorname{Im} \int_{-\infty}^{E_F}
\mathrm{d} E \operatorname{Tr}[\delta t_i G_{ij} \delta t_j G_{ji}]
\)
\end{flushright}
\end{frame}
% Slide results Jijs 1 % Section co-doping
{
\setbeamercolor{background canvas}{bg=}
\includepdf[pages=1]{../resources/fig/external/papers/mozumderHighthroughputMagneticCodoping2024/originals/Fig4.pdf}
}
% Slide AiiDA-Spirit workflows % Section jij-prediction
\begin{frame}[plain,c]
\frametitle{Spin dynamics with \logoAiida{}-\raisebox{-0.4em}{\logoSpiritWithText{}}\footcite{russmannAiiDASpiritPluginAutomated2022}}
\vspace*{-2em}
\begin{columns}[t]
\begin{column}{0.4\linewidth}
\begin{center}
\includegraphics[width=1.0\linewidth]{../resources/fig/external/papers/mozumderHighthroughputMagneticCodoping2024/processed/FigA2-no-subfigure-labels.pdf}%
% \includegraphics[width=1.0\linewidth]{../resources/fig/presentation-2023-03/ruess/ruess-aiida-kkr-paper-workflow-c.pdf}\footcite{russmannAiiDAKKRPluginIts2021}
\vspace*{1em}
Liechtenstein formula
\[
\mathcal{J}_{ij} = -\frac{1}{\pi} \operatorname{Im} \int_{-\infty}^{E_F}
\mathrm{d} E \operatorname{Tr}[\delta t_i G_{ij} \delta t_j G_{ji}]
\]
ML-IAP approach. \(E_k = \sum_k E_k \longrightarrow J_{ij} =
\sum_k \left( J_{ij} \right)_k\)
\end{center}
\end{column}
\begin{column}{0.55\linewidth}
\begin{center}
\includegraphics[width=1.0\linewidth]{../resources/fig/presentation-2023-03/ruess/ruess-aiida-spirit-paper-workflow.pdf}%
Landau-Lifshitz-Gilbert equation
\[
\frac{\partial \vec{S}_i}{\partial t}=-\gamma^{\prime} \vec{S}_i \times \vec{B}_i^{\text {eff }}-\lambda \vec{S}_i \times\left(\vec{S}_i \times \vec{B}_i^{\text {eff }}\right)
\]
% \vspace*{2em}
\normalsize{\href{https://juspin.de}{juspin.de}}
\end{center}
\end{column}
\end{columns}
\end{frame}
% Slide project design % Section jij-prediction
\begin{frame}[plain,c]
\frametitle{Project \enquote{ML-Exc}}
\framesubtitle{Model selection}
\begin{table}[H]
\resizebox{\columnwidth}{!}{%
\centering
\input{../resources/table/jij-prediction-model-selection}%
% \caption[short caption]{Long caption.}
% \label{tab:model-selection}
}
\end{table}
\end{frame}
% Slide exc-int tensor prediction % Section jij-prediction
\begin{frame}
\frametitle{Project \enquote{ML-Exc}}
\framesubtitle{Tensorial interaction}
Heisenberg Hamiltonian in tensor form.
\[\mathcal{H}_H=-\sum_{j>i} \vec{m}_i \cdot \mathcal{J}_{i j} \vec{m}_j\]
Tensor components: isotropic, anti-symmetric (DMI) and anisotropic or
traceless symmetric part (neglected so far).
\[\mathcal{J}_{i j}=J_{i j} \mathbb{1} + \mathcal{J}_{i j}^A+\mathcal{J}_{i
j}^S\]
with \(J_{i j}^{x x}=J_{i j}^{y y}=J_{i j}^{z z}=\frac{1}{3} J_{i j}\) and
\[\mathcal{J}_{i j}^A=\left[\begin{array}{ccc}
0 & J_{i j}^{x y} & J_{i j}^{x z} \\
J_{i j}^{y x} & 0 & J_{i j}^{y z} \\
J_{i j}^{z x} & J_{i j}^{z y} & 0
\end{array}\right]=\left[\begin{array}{ccc}
0 & J_{i j}^{x y} & -J_{i j}^{z x} \\
-J_{i j}^{x y} & 0 & J_{i j}^{y z} \\
J_{i j}^{z x} & -J_{i j}^{y z} & 0
\end{array}\right]=\left[\begin{array}{ccc}
0 & -D_{i j}^z & D_{i j}^y \\
D_{i j}^z & 0 & -D_{i j}^x \\
-D_{i j}^y & D_{i j}^x & 0
\end{array}\right]\]
\end{frame}
% Slide higher-order exchange interactions
{
\setbeamercolor{background canvas}{bg=}
\includepdf[pages=1]{../resources/fig/external/presentations/spinqx23/hkatsumoto/spinqx23-hkatsumoto-page24.pdf}
}
% Slide relativistic exchange interactions
{
\setbeamercolor{background canvas}{bg=}
\includepdf[pages=1]{../resources/fig/external/presentations/spinqx23/hkatsumoto/spinqx23-hkatsumoto-page25.pdf}
}
% Slide best-of-aml % Section odds-and-ends
\begin{frame}[plain,c]
\frametitle{Community resources}
\framesubtitle{Best of atomistic machine learning}
\vspace*{-4em}
\begin{columns}[c]
\begin{column}{0.5\linewidth}
\vspace*{3em}
\begin{center}
% \includegraphics[width=0.7\textwidth]{../resources/fig/github/best-of-aml-thumbnail-2023-09-05.png}%
\includegraphics[width=0.9\textwidth]{../resources/fig/github/best-of-aml-thumbnail-2024-07-08.png}%
Largest list of atomistic ML tools on the web (400+), auto-ranked,
regular updates\footcite{wasmerBestAtomisticMachine2023}
\vspace*{2em}
\Large{\href{https://go.fzj.de/best-of-aml}{go.fzj.de/baml}}
\end{center}
\end{column}
\begin{column}{0.4\linewidth}
\includegraphics[width=0.85\textwidth]{../resources/fig/github/best-of-aml-contents-2024-07-08.png}%
\end{column}
\end{columns}
\end{frame}
%%% Local Variables:
%%% mode: latex
%%% TeX-master: "../presentation"
%%% mode: flyspell
%%% ispell-local-dictionary: "english"
%%% End:
presentation/mainmatter/kkr-jlcdm.tex
View file @ 03e3610a
\section{KKR-JLCDM}
\section{Better initial guess}
\label{sec:kkr-jlcdm}
% Slide outlook vimp-prediction % Section odds-and-ends
% Slide PhD project flowchart % Section phd-project
\begin{frame}
\frametitle{Vision: Electronic structure learning}
% frametitle notes: PhD project flowchart
\framesubtitle{as integrated, high-level multiscale workflows}
\vspace*{0em}
\includegraphics[width=1.0\textwidth]{../resources/fig/presentation-2023-02/atomistic-ml/classification-of-atomistic-ml_presentation-2023-02_02-emph-both_ktikz.pdf}
\vspace*{2em}
\begin{columns}[t]
\hspace{1em}
\begin{column}{0.6\linewidth}
\begin{center}
Better \enquote{initial guess}
for fast SCF convergence
\end{center}
\end{column}
\vrule{}
\hspace{1em}
\begin{column}{0.39\linewidth}
\begin{center}
Magnetic property prediction
(ML-Exc) for spin dynamics
\end{center}
\end{column}
\end{columns}
\end{frame}
% Slide AiiDA-KKR workflows % kkr-theory
\begin{frame}[plain,c]
\frametitle{Project \enquote{Better initial guess}}
\framesubtitle{\logoAiida{}-KKR workflows}
\vspace*{0em}
\begin{columns}[t]
\vspace*{-1em}
\begin{column}{0.3\linewidth}
% {\footnotesize Single impurity}
\begin{center}
\includegraphics[width=0.95\linewidth]{../resources/fig/presentation-2023-03/ruess/ruess-aiida-kkr-paper-workflow-c.pdf}\footcite{russmannAiiDAKKRPluginIts2021}
\end{center}
\end{column}
\begin{column}{0.5\linewidth}
\vspace*{-2em}
\begin{center}
\includegraphics[width=0.75\textheight]{../resources/fig/aiida-kkr/graph//kkr_imp_wc.pdf}%
\end{center}
% \framezoom<1><2>(3.5cm,0.5cm)(4.5cm,3.25cm) % upper part, kkr_scf workflow
% \framezoom<1><3>(4.5cm,3.5cm)(4.5cm,5.25cm) % lower part, kkr_imp workflow
\end{column}
\end{columns}
\vspace*{-0em}
\end{frame}
% Slide dataset generation (single-impurity-database)
\begin{frame}[plain]
\frametitle{Electronic structure emulator}
\frametitle{Project \enquote{Better initial guess}}
\framesubtitle{Data generation}
% frametitle notes: Vimp-prediction qnd
\begin{columns}[c]
\begin{column}{0.45\linewidth}
{\small
\textbf{Task} Predict electron potential difference \(\Delta
V_{imp}(\vec{r})\) \& use as initial guess for \logoAiida -KKR\logoKKRimpCircled{}\vspace{0.5em}
\textbf{Data} 10'000 single impurity in elemental crystal combinations
(\(60 \times 40\) elements) from DFT\vspace{0.5em}
\textbf{Challenges}
\begin{itemize}
\item[\textcolor{fzjblue}{\faFlag}] \textbf{Collaboration} Adapt the linear Jacobi-Legendre Charge Density
Model (JLCDM)\footcite{focassioLinearJacobiLegendreExpansion2023a} to
all-electron DFT
\item Large chemical feature space
\item From radial to full equivariant potential
\item Intermediate SCF results
\item Should be code-agnostic
\item From collinear to noco magnetism
\end{itemize}\vspace{0.5em}
\textbf{Data} 10'000 impurity embeddings into elemental crystals\vspace{0.5em}
\textbf{Target} Electron potential difference \(\Delta
V_{imp}(\vec{r})\) \vspace{0.5em}
}
\includegraphics[width=1.0\linewidth]{../resources/fig/aiida-kkr-ml/da/conv_heatmap_scale-factor_count_linscale_annot.pdf}%
\begin{center}
{\footnotesize \textcolor{fzjgray50}{Dataset map. Rows: Element of host crystal,
columns: impurity atom, color: num. calculations.}}
\end{center}
\end{column}
\begin{column}{0.55\linewidth}
\vspace*{-2em}
\begin{columns}
\begin{column}{0.25\linewidth}
\hspace*{2.0em}
......@@ -57,22 +110,47 @@
{\footnotesize \textcolor{fzjgray50}{Spherical impurity potentials in first Voronoi cell of \ce{Hg}:\ce{X}
embeddings,\\left upper to bottom right: \(V\), \(V\!-\!V^0\), \(|V|\), \(|V\!-\!V^0|\).}}
\end{center}
% \includegraphics[width=0.8\linewidth]{../resources/fig/aiida-kkr-ml/da/conv_heatmap_scale-factor_count_linscale.pdf}%
\end{column}
\end{columns}
\end{frame}
\begin{frame}[plain]
% \frametitle{Dataset analysis}
% \framesubtitle{}
\emph{Single-impurity database} \(\boldsymbol{\vert}\) Charge doping
\vspace{-1em}
\begin{center}
\includegraphics[width=0.88\textwidth]{../resources/fig/aiida-kkr-ml/da/conv_heatmap_charge-neutrality-imp_linscale.pdf}%
\end{center}
% Slide Jacobi-Legendre framework
\begin{frame}[plain,c]
\frametitle{The Jacobi-Legendre framework\footcite{dominaJacobiLegendreFrameworkMachine2024}}
\framesubtitle{for electronic structure representation}
\vspace*{0em}
\begin{columns}[c]
\begin{column}{0.68\linewidth}
\begin{center}
% \includegraphics[width=1.0\linewidth]{../resources/fig/external/papers/dominaJacobiLegendreFrameworkMachine2024/processed/fig5.1-with-polynomials.png}%
\includegraphics[width=0.9\linewidth]{../resources/fig/external/papers/dominaJacobiLegendreFrameworkMachine2024/originals/fig4.1.pdf}%
\vspace*{0.0em}
\includegraphics[width=0.9\linewidth]{../resources/fig/external/papers/dominaJacobiLegendreFrameworkMachine2024/processed/fig-5.1-first-row-only.png}%
\end{center}
\end{column}
\vrule{} % vertical separator line
\hspace*{1em}
\begin{column}{0.32\linewidth}
\vspace*{0em}
\includegraphics[width=0.75\linewidth]{../resources/fig/external/papers/dominaJacobiLegendreFrameworkMachine2024/processed/fig6.2-300dpi-narrower-1.png}%
\end{column}
\end{columns}
\vspace*{0em}
\end{frame}
% \begin{frame}[plain]
% % \frametitle{Dataset analysis}
% % \framesubtitle{}
% \emph{Single-impurity database} \(\boldsymbol{\vert}\) Charge doping
% \vspace{-1em}
% \begin{center}
% \includegraphics[width=0.88\textwidth]{../resources/fig/aiida-kkr-ml/da/conv_heatmap_charge-neutrality-imp_linscale.pdf}%
% \end{center}
% \end{frame}
\begin{frame}[plain]
\frametitle{Atom-based JLCDM}
......
presentation/mainmatter/kkr-theory.tex
View file @ 03e3610a
......@@ -16,33 +16,9 @@
% KKR theory slide from mavropoulous dft20 kkr lecture
{
\setbeamercolor{background canvas}{bg=}
\includepdf[pages={5-8,10-14,18,21-22}]{../resources/fig/external/presentations/rwth-dft20/lecture-20-kkr/Lecture20_KKRGF-Method.pdf}
\includepdf[pages={5-8,10-14,18,21-22,24,30}]{../resources/fig/external/presentations/rwth-dft20/lecture-20-kkr/Lecture20_KKRGF-Method.pdf}
}
% Slide AiiDA-KKR workflows % kkr-theory
\begin{frame}[plain,c]
% \frametitle{\logoAiida{}-KKR workflows\footcite{russmannAiiDAKKRPluginIts2021}}
\frametitle{\logoAiida{}-KKR workflows}
\vspace*{0em}
\begin{columns}[t]
\vspace*{-1em}
\begin{column}{0.3\linewidth}
% {\footnotesize Single impurity}
\begin{center}
\includegraphics[width=0.95\linewidth]{../resources/fig/presentation-2023-03/ruess/ruess-aiida-kkr-paper-workflow-c.pdf}\footcite{russmannAiiDAKKRPluginIts2021}
\end{center}
\end{column}
\begin{column}{0.5\linewidth}
\vspace*{-2em}
\begin{center}
\includegraphics[width=0.8\textheight]{../resources/fig/aiida-kkr/graph//kkr_imp_wc.pdf}%
\end{center}
% \framezoom<1><2>(3.5cm,0.5cm)(4.5cm,3.25cm) % upper part, kkr_scf workflow
% \framezoom<1><3>(4.5cm,3.5cm)(4.5cm,5.25cm) % lower part, kkr_imp workflow
\end{column}
\end{columns}
\vspace*{-0em}
\end{frame}
%%% Local Variables:
......
presentation/presentation.pdf 0 → 100644 LFS
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File added
presentation/presentation.tex
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......@@ -180,6 +180,7 @@
\input{mainmatter/introduction}
\input{mainmatter/kkr-theory}
\input{mainmatter/kkr-jlcdm}
\input{mainmatter/jij-prediction}
% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% BACKMATTER
......
resources @ b77470b7
Compare 1d751f3f...b77470b7
Subproject commit 1d751f3fcbb54b2c95439808a5a8724492ef64dc
Subproject commit b77470b789cde8bf1732d6af327ac4f942f5dc4c